Browse Topic: Fault detection

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Fault Diagnosis of Packaging Machine Bearings Based on EEMD-CNN-BiLSTM2026-99-1601To be published on 07/24/2026
During the high-speed operation of packaging machines, if the abnormal components evolve into faults, the packaging machines often stop for inspection or even damage, causing production stagnation and huge economic losses. If key variables are predicted and faults are identified before the evolution of packaging machine failures, it is of great significance to ensure equipment safety and reduce maintenance costs and losses for enterprises. The purpose of fault prediction is to use the information modeling of equipment historical data to output the changes in key features before component failures in the future. Firstly, for the redundant data of multiple measurement points of the same variable in the packaging machine process variables, Pearson correlation analysis is used to obtain more accurate variable data. We reuse adaptive empirical mode decomposition (EEMD) for signal processing and feature extraction, reduce redundant information, use convolutional neural network (CNN) models for spatial feature learning, and then use bidirectional long short-term memory models to capture temporal dependencies of CNN information for capturing time series data. A model is established on the normal training set to fit the normal state of the packaging machine, identify different types and degrees of equipment fault characteristics through normal test set data, and send the predicted results of the equipment state to the fault classifier for judgment to determine whether to issue a fault warning. The results indicate that this article has validated the effectiveness of the model in fault feature extraction and high-precision fault classification through training on equipment status data.
Wu, Aimin, Liu, Shixian, Zhao, Lihui, Liu, Zhao, Wu, Tao, Li, Lianbing
Research on Unmanned Aerial Vehicle Fault Detection Method Based on 1DCNN-BiLSTM2026-99-18667/17/2026
In recent years, drone technology has seen widespread application in both civilian and military fields. By 2025, China will introduce supportive policies from multiple dimensions, including industrial development, technological innovation, and application promotion, to significantly increase the number of UAVs in use and their frequency. However, drones are prone to malfunctions due to factors such as bad weather and electromagnetic interference, which may result in serious consequences, including property damage and casualties. Therefore, improving the accuracy of fault detection and the response time of drones is of great significance. Although current research has made progress, there are still deficiencies: First, most of them rely on a single or limited data source, resulting in incomplete information and vulnerability to interference, which leads to low detection accuracy and reliability; Second, traditional methods are mostly based on fixed thresholds or simple rules, lacking real-time dynamic monitoring and adaptive analysis capabilities, making it difficult to issue timely warnings of potential faults. To this end, this study proposes a multi-scale time series prediction model based on multimodal and multi-branch, integrating multimodal data, constructing a dual-branch architecture, and combining deep learning and attention mechanisms to enhance the anomaly detection effect of unmanned aerial vehicles. A dual-branch anomaly detection model based on 1DCNN-BiLSTM and continuous wavelet transform is proposed, including a trajectory prediction difference branch and a full time series data branch. In the dual-branch output stage, the attention gating mechanism is utilized to fuse features and improve the detection performance. The experimental results show that this model performs excellently in both normal trajectory prediction and anomaly detection, providing an effective solution for drone anomaly detection.
Pu, Zhenglin, Zhang, Lin
Research on Multi-Model Fusion Method for Small Satellite Abnormal State Detection Based on Machine Learning2026-99-18177/17/2026
Aiming at problems such as low efficiency and poor accuracy in fault identification for traditional small satellites, this paper proposes a multi-model fusion method based on machine learning. By constructing a telemetry data preprocessing module based on the Data Generation Adversarial Network, it effectively deals with outliers and fills missing values. Combining single model methods such as polynomial curve fitting, the grey model, and the ARMA model, and introducing the Long Short-Term Memory network and Gated Recurrent Unit to fuse with these models enhance the ability to process complex data features. The prediction results of each model are fused using machine learning methods, and finally, the fused value is taken as the final prediction result. The numerical simulation results show that this prediction method can predict the anomalies of different types of satellite telemetry parameters and has achieved good results.
Liu, Biyan, Chen, Ye, Guo, Qi
Adaptive Class-Incremental Learning Intelligent Fault Diagnosis Method for Unmanned Aerial Vehicle Rolling Bearings2026-99-05967/10/2026
As a key component of unmanned aerial vehicles (UAVs), the stable operation of motor bearings is of vital importance to the stability of UAVs. In view of the incomplete data set in the actual diagnosis process, samples not encountered during model training are highly likely to appear. This paper proposes an Adaptive Class-Incremental Learning(ACIL) intelligent fault diagnosis method. This method construct a ResNet framework embedded with Coordinate Attention as the base architecture for class-incremental learning. Furthermore, the Information Preservation Example Selection(IPES) method is utilized to alleviate catastrophic forgetting and update the model from the previous phase using knowledge distillation under coordinate attention. The effectiveness of this method is verified through experiments on the bearing test dataset. The results show that, both average incremental accuracy and average incremental forgetting rate achieve state-of-the-art performance, which means that the performance of the proposed method outperforms than those of other methods.
Song, Ziyang, Lu, Jiantao, Wu, Wei, Li, Shunming
Research on the Dynamic Simulation Method of Gearbox Based on ADAMS and MATLAB2026-99-05197/10/2026
The gearbox is a key component of the mechanical transmission system, and its fault diagnosis is essential to the reliability of the equipment. However, obtaining fault samples under actual working conditions for gearbox fault diagnosis is challenging. In this paper, the rigid-flexible coupling dynamic simulation model of the gearbox is established, and the co-simulation of gear normal, crack, and breakage is carried out in the ADAMS and MATLAB environments. The comparison between the simulated and measured signals shows that the simulation method can accurately reflect the key characteristics, such as rotation frequency and meshing frequency, and verify its reliability and accuracy. The research results can provide effective data support for gearbox fault diagnosis and improve the operational safety of mechanical systems.
Li, Dongxiao, Zhang, Qianqi, Zhang, Zhongzheng, Li, Yongbo
A Novel Rotating Component Comprehensive Diagnostic Network Based on Stacked Auto Encoders and Sparse Filter Combined with Generative Adversarial Network2026-99-05347/10/2026
Nowadays, the majority of intelligent fault diagnosis approaches are still centered on individual faulty components, while only a limited number of models are capable of performing integrated diagnosis for rotating systems that consist of shafts, bearings, and gears. Under variable-speed operating conditions, the large scale of vibration data further complicates the process of effective feature extraction. To improve these challenges, this study develops a comprehensive diagnostic framework for rotating components, termed WGAN-SAFC. The proposed architecture integrates a Wasserstein Generative Adversarial Network (WGAN) with a hybrid structure of stacked autoencoders and sparse filtering (SAFC). SAFC integrates the feature-learning capability of SAE and the sparsity-driven representation of SF, while incorporating adversarial data generation to address sample imbalance and enhance fault diagnosis performance. Experimental verification on collected vibration datasets demonstrates that WGAN-SAFC achieves superior diagnostic accuracy and robustness compared with existing methods.
Li, Shunming, Feng, Mengqi
Steering System Fault Diagnosis and Fault-Tolerant Control of Active Wheel-Corner Electric Vehicles10-10-04-00367/9/2026
The Active Wheel-Corner (AWC) integrates driving, braking, steering, and suspension systems into the wheel end, forming a fully drive-by-wire, four-wheel independent steering and four-wheel independent driving (4WIS&4WID) vehicle platform. While improving vehicle control performance, the full by-wire architecture also places higher demands on system reliability and fault tolerance. The steer-by-wire system has electrical, communication, and software failure risks, which may cause the vehicle to lose steering capability and trigger severe traffic accidents. This article proposes a hierarchical active fault-tolerant control strategy based on fault information reconstruction (FAST-FTC), enabling fault diagnosis and active fault-tolerant control when the steer-by-wire system fails, effectively ensuring the steering maneuverability and lateral stability of the vehicle under fault conditions. First, the strategy designs an adaptive observer combined with the Dugoff tire model to estimate nonlinear tire forces, while introducing a fault factor to achieve quantitative grading of steering system faults. Second, a hierarchical controller is designed for steering system faults. The upper-level controller, based on Adaptive Super-Twisting Sliding Mode Control (AST-SMC), determines the generalized forces required to track the desired trajectory under different fault conditions. The lower-level controller, based on the Fault-Aware Model Predictive Control (FA-MPC) strategy, dynamically adjusts weight matrices according to the fault factor and tire reconstructed stiffness, coordinating the allocation of four-wheel driving and the steering of healthy wheels to ensure lateral stability. Finally, the effectiveness of the proposed active fault-tolerant control strategy is validated through Hardware-in-the-Loop (HIL) simulation and real-vehicle tests.
Xiao, Feng, Jiang, Yueyong, Cheng, Rui, Xu, Changhe, Tang, Xiangjiao, Gao, Fengling, Li, Jianhua
Harmonizing Generative AI and ISO 26262 for Safe and Intelligent ADAS Systems2026-01-07857/1/2026
This paper investigates the integration of Artificial Intelligence (AI) within radar-based perception for Advanced Driver Assistance Systems (ADAS) under safety considerations aligned with ISO 26262 [1] for functional safety and ISO 21448 (SOTIF) [2] for performance-related safety of the intended functionality. The study evaluates a hybrid architecture in which AI-based perception modules are combined with deterministic supervisory mechanisms to maintain safety compliance. A simulation-based case study using CARLA with radar sensor modeling is presented to compare a deterministic radar perception pipeline with an AI-enhanced approach under nominal and degraded environmental conditions. Performance is evaluated using precision, recall, and F1 score metrics. Results indicate improved recall and F1 score under adverse scenarios for the AI-based perception module, accompanied by a moderate increase in false positives. The paper discusses architectural constraints required to limit non-deterministic behavior, including confidence gating, deterministic supervision, and scenario-based validation. The findings are limited to simulation and are intended to provide preliminary insights into the technical and safety implications of incorporating AI-based radar perception within ISO 26262-compliant ADAS architectures.
Jain, Yesha
Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection2026-01-07176/20/2026
In recent years, the automotive industry has actively explored the application of various AI-based models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformers to improve defect detection rates at the End-of-Line (EOL) stage. However, implementing these approaches in the Noise, Vibration, and Harshness (NVH) area face several practical challenges: ① extended evaluation times compared to other data types, which limit the quantity of training data and lead to overfitting; ② label imbalance caused by the relatively small amount of defect data; ③ reduced labeling accuracy due to human error; ④ decreased robustness under domain shifts such as changes in jig fixtures, test environments, and signal-to-noise ratio (SNR); ⑤ diminished model reliability when new defect arise during development; and ⑥ constraints imposed by compatibility requirements with existing test equipment. This study proposes a Convolutional Autoencoder (CAE) based framework trained on NVH datasets collected from normal and defective Column-type Electric Power Steering (C-EPS) systems. Latent variables at the bottleneck layer are used for dimension reduction, enabling visualization and unsupervised classification using a clustering algorithm. A classification model derived from the encoder is fine-tuned with clustered data, and Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable AI (XAI) technique, is applied to extract Feature Frequency Maps (FFM) highlighting defect-related noise and vibration characteristics. The proposed approach does not rely on the deep learning model to directly classify defect. Instead, it utilizes extracted FFM as weights(mask) to detect defect. This method enables quantitative data representation and ensures high applicability with existing EOL equipment. Post-processing within the FFM enables root cause analysis, reducing issue resolution time and supporting integration with conventional signal analysis techniques.
Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik
Electric Health-Monitoring Wiring (E-Wiring) for Aircraft2026-26-07856/1/2026
Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. This paper introduces an advanced Electric Health-Monitoring Wiring (E-Wiring) system that integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based cloud analytics for fleet-wide insights. We have an existing method to detect by using acoustic sensing method which can detect ongoing insulation chafing or a cut, they are limited in identifying pre-existing damages and by adding multiple acoustics in the existing wire harnesses it’ll add extra load to the aircraft. To overcome this, the system incorporates Time Domain Reflectometry (TDR) technology to detect both existing and potential wiring faults. The TDR circuitry interfaces with onboard devices, injecting test signals into wiring to pinpoint insulation anomalies or conductor breaks without adding significant weight or complexity. The proposed E-Wiring and EHMS solution enhances aircraft safety, reduces maintenance costs, and improves operational availability, offering a scalable approach for both retrofit and new-generation aircraft.
Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha
Research on Fault Diagnosis Agent of Monorail Crane Based on Knowledge Graph and Lightweight Large Model2026-99-07415/15/2026
The monorail crane is important in mining operations, and its operation affects both safety and efficiency. Currently, fault diagnosis for monorail cranes has several challenges, such as heterogeneous mixing of multimodal data, poor use of knowledge, low real-time requirements, and high deployment costs for large-scale models. To solve these problems, we present an agent framework using a multimodal knowledge graph and a lightweight large model. In particular, we construct a fault knowledge graph for monorail cranes, organizing professional knowledge about components, failure modes, symptoms, and maintenance. By employing retrieval-augmented generation (RAG) technology, the knowledge graph is merged with the Qwen lightweight large model (low-rank adaptation) for fine-tuning to develop a diagnostic agent with task planning, tool invocation and memory. The experimental results show that the agent framework reduces “machine hallucination” and outperforms conventional diagnostic accuracy, response speed and resource efficiency, thus offering a safe and efficient solution for intelligent operation and maintenance of mining equipment.
Zhang, Yixuan, Xue, Shun, Bi, Xiang, Wei, Xing, Kang, Ranyu, Jue, Jie, Cheng, Liruiran
Flight Simulation Studies of a Classical Octocopter with an Inoperative MotorF-0082-2026-02305/5/2026
This study investigates the post-failure flight dynamics of a 1200 lb classical octocopter under single motor inoperative condition using nonlinear time-domain simulations with a baseline feedback controller. A physics based propulsion sizing strategy is developed using IEC duty cycle definitions where continuous requirements are derived from nominal hover with margin and short time capability is used to accommodate elevated post failure loads. The selected motor satisfies both regimes and enables transient overdrive without excessive weight penalty. Simulation results in hover and forward flight at the best range speed showing that the vehicle can recover from any single motor failure and retrim using inherent redundancy without fault identification. However, recovery involves significant transient attitude excursions and altitude loss, and requires substantial increases in motor power, with multiple motors exceeding S1 power limits. Post-failure maneuver simulations indicate retained controllability with some degradation and increased coupling. These simulations demonstrate that the proposed motor sizing enables necessary operation post-failure while avoiding unnecessary oversizing.
Lemelin, Dakoda, Gandhi, Farhan, Fong, Weston
Proactive Fault Detection in EV Batteries Via Hybrid Method: Cell-to-Cell Variation Pattern and Physics-Based Model Insights2026-01-03914/7/2026
This study presents a distinct methodology for the early detection of faulty cells in electric vehicle (EV) battery systems, leveraging temporal voltage deviation patterns under real-world charging scenarios alongside outputs from a physics-based model. A comparative longitudinal analysis was conducted on a fleet of twelve EVs—six exhibiting stable performance and the other six demonstrating early-stage anomalies characterized by intermittent transitions from drive to neutral mode. These behavioral cues were investigated as precursors to deeper battery degradation. The analysis focused on cell-level voltage dispersion in battery pack during the mid-to-high state-of-charge (SoC) range (approx. 20–30% to full charge). Vehicles in healthy condition consistently displayed minimal voltage deviation between BMS-measured cell voltages and physics-based model predictions, whereas those with latent faults showed markedly higher variance, particularly between the highest battery and model-expected cell voltages. Notably, this voltage divergence was often accompanied by a modest yet recurrent thermal rise of 2–3°C, suggesting early-stage thermal non-uniformity. All vehicles were monitored over extended distances under diverse, real-world driving and environmental conditions, enhancing the robustness and generalizability of the findings. The proposed approach underscores the diagnostic value of tracking voltage deviation trajectories as a non-intrusive, scalable means of forecasting cell-level degradation. This framework could significantly advance predictive maintenance strategies, improving both the reliability and operational lifespan of EV battery packs.
Jawle, Bharat Sanjay, Selvakumar, Ashwin, Puttoji Rao, Nagaraj Kumar
Predictive Maintenance of EV Drive Unit Oil Pumps through Machine Learning on Fleet Telemetry Data2026-01-01614/7/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. Specifically, we develop an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of anomaly severity. A fully automated pipeline ingests daily telemetry, performs session segmentation, executes predictive modeling, and records anomaly outcomes in backend databases for continuous monitoring and engineering review. The proposed framework enables continuous, fleet-wide predictive maintenance of DU oil pumps. It improves early detection of degradation, reduces vehicle downtime, enhances safety, and increases customer satisfaction. More broadly, it highlights the potential of large-scale data analytics and machine learning to advance predictive maintenance and reliability in electric vehicle (EV) systems.
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
WITHDRAWAL NOTICE2026-01-0158.014/7/2026
Real-Time Fault Diagnosis and Reliability Analysis in Lithium-Ion BESS via Bayesian Fault Propagation Networks2026-01-70232/27/2026
The rapid integration of intermittent renewable energy sources (RES) poses significant operational challenges for modern power systems. Lithium-ion battery (LIB)–based battery energy storage systems (BESS) have become vital for grid stability and energy management. However, large-scale deployment of BESS has led to increasing incidents such as fires and explosions, raising serious concerns regarding their safety and reliability. To overcome the limitations of traditional reliability assessment methods—such as reliability block diagrams (RBD), fault tree analysis (FTA), and Markov models—this study proposes an integrated fault detection and reliability analysis framework that combines FTA, failure mode and effects analysis (FMEA), and a Bayesian Fault Propagation Network (BFPN). The framework systematically models fault propagation across component, subsystem, and system levels, dynamically updating the prior probabilities of basic failure events using a Gaussian Mixture Model (GMM) and Expectation–Maximization (EM) algorithm. Conditional Probability Tables (CPTs) are recalculated through Maximum Likelihood Estimation (MLE) with logical relationships to achieve accurate and adaptive fault probability estimation. A multi-feature fusion indicator, the State Severity Indicator (SSI), is further introduced to evaluate system health in real time. A qualitative comparison with representative fault modeling and detection approaches—including Bayesian Network, FTA-DBN, and various machine learning methods—shows that the proposed BFPN offers a well-balanced trade-off between interpretability and real-time performance. Simulation experiments under both single- and multiple-fault scenarios demonstrate that the proposed framework accurately detects typical fault events and provides early warnings before fault escalation. Under complex coupled fault conditions, it effectively captures fault interactions and predicts cascading failures across subsystems and the overall BESS, showing strong robustness and diagnostic capability for real-time reliability assessment in modern energy storage systems.
Yang, Zhan, Chen, Xiaobo, Zheng, Ruixiang, Li, Mian
Fault Diagnosis Method of Lithium-Ion Batteries Based on OCSVM and RLMQD Algorithms2026-01-70202/27/2026
To address the challenges of recognizing abnormal states, detecting subtle early warning signs, and quantifying fault severity in scenarios involving simultaneous multiple faults in lithium-ion batteries, this study proposes a dual-layer fault diagnosis framework that integrates One-Class Support Vector Machine (OCSVM) and Robust Local Mahalanobis Distance Quantile (RLMQD) algorithm. First, a three-dimensional multi-scale feature space, incorporating voltage, kurtosis, and voltage change rate, is constructed to detect abnormal battery states via OCSVM and dynamically filter abnormal time periods with improved adaptability. Second, a computationally efficient RLMQD-based quantization algorithm is developed, which employs a small-scale sliding window and adaptively selects healthy cells to construct reference distributions. By incorporating low-quantile thresholds, the algorithm enhances early abnormality detection and significantly reduces false positives. Subsequently, fault severity is quantified through scale-weighted fusion and normalization, enabling accurate evaluation across diverse abnormal modes. Finally, The diagnostic performance of the proposed method is comprehensively validated through three sets of simulation experiments and real-vehicle data collected under realistic operating conditions. The results demonstrate that the proposed method accurately identifies both single-point and clustered anomalies, corresponds closely with actual fault conditions and exhibiting strong generalization capability. In real vehicle validation, the method achieves 95.79% accuracy, 100% recall, and a 93.3% F1 score in abnormal detection tasks. Furthermore, It demonstrates robustness and interpretability, enabling multi-type abnormal detection and fault severity evaluation without reliance on extensive fault datasets, thereby offering high suitablility for online monitoring and early warning in actual Battery Management Systems.
Wei, Fuxing, Yang, Libing, Wang, Zonglei, Xia, Xuelei, Shen, Jiangwei, Chen, Zheng
A Hierarchical Fault Detection Method for Lithium-Ion Batteries in Real-World Vehicle Applications2026-01-70062/27/2026
With the rapid expansion of the electric vehicle market, the safety of lithium-ion batteries, which serve as the main power source, has become a critical concern. Current mainstream methods for battery fault detection generally face a technical bottleneck of struggling to balance high accuracy with a low false alarm rate. Furthermore, constrained by algorithmic complexity and data processing efficiency, detection speeds often fail to meet the practical demands of real-time monitoring. As a result, developing more efficient and accurate fault detection technologies has emerged as a key challenge urgently needing to be addressed in the industry. This paper proposes a hierarchical fault detection framework for lithium-ion batteries that integrates voltage change characteristics with a Local Outlier Factor (LOF) scoring mechanism. The framework aims to achieve early identification and accurate diagnosis of abnormal battery states through multi-dimensional feature extraction and algorithmic fusion. In the first layer, decentralized voltage data are standardized using the 3σ rule to identify potentially anomalous batteries. In the secondary analysis phase, a sliding time-window mechanism is adopted to dynamically capture voltage sequences. Within each window, voltage variations are calculated along both vertical and horizontal directions, and statistical metrics, including mean, standard deviation, range, and increment are derived. Principal component analysis is then applied to extract key features, and battery anomalies are evaluated and confirmed based on the maximum LOF score. Experimental validation using datasets from vehicles that experienced thermal runaway events, along with data from 1,000 normal vehicles, demonstrates that the proposed method significantly improves the accuracy of battery fault detection. It also provides early warnings up to 17 days prior to the occurrence of thermal runaway.
Gao, Zhengpeng, Gao, Pingping, Chang, Penghui, Liu, Gang, Wu, Ji
Artificial Intelligence and Cloud Synergy for Scalable Battery Management Systems2026-28-01192/12/2026
Due to the rapid transformation of EVs and the battery storage system, the battery management system (BMS) is essential to ensure optimal performance of the battery storage piles. A BMS monitors and controls parameters such as SOC, voltage, current, and temperature. A traditional BMS has a minimum support of analytics, and it’s limited to local processing. However, when the battery information is uploaded to the internet, it becomes easier to manage maintenance and track the battery’s performance from anywhere in the world. This Cloud-based system is easy and made earlier, thereby giving a system alarm before the issue becomes big. Managing many batteries at once saves a significant amount of money in places like EV charging stations and Energy Storage Systems (BESS). Software updates to the system can also be sent remotely. Also, a BMS connected to the cloud can be used to support weaker grids in an instant if it needs the reactive power support. Cloud integration of BMS with the grid network will help in better planning of energy management at load dispatch centers. A BMS managing a pack of batteries at a renewable energy system can help to understand power demand and decide when the best time is to charge or discharge. So, this can monitor all the batteries without being near them. Further, identifying the problems is work that focuses on an ML-RL-based battery management system connected to the cloud to control and monitor the Voltage, temperature, Cell balancing, SOC, SOH, and fault identification. This BMS system has easy scalability to thousands of batteries connected. As the demand for EVs and clean energy soars, this cloud-integrated BMS would play an important role in managing the batteries that are part of that system, making it smarter, efficient, and reliable. The proposed Q-learning–based Cloud BMS achieves 96.5% energy efficiency, 3.2% SOC RMSE, and zero safety violations across 75,000 simulated samples, using an adaptive 6,000-state Q-learning agent validated through real-time cloud integration.
R, Rajarajeswari, N, Kalaiarasi, Francis, Elgin Calister
Achieving Reliability, Safety & Security in SDV OS Architecture2026-28-01222/12/2026
Software-defined vehicles are those whose functionalities and features are primarily governed by software, thus allowing continuous updates, upgrades, and the introduction of new capabilities throughout their lifecycle. This shift from hardware-centric to software-driven architectures is a major transformation that reshapes not only product development and operational strategies but also business models in the automotive industry. An SDV operating system provides the base platform to manage vehicle software and enable those advanced functionalities. Unlike traditional embedded or general-purpose operating systems, it is designed to meet the particular demands of modern automotive architectures. Reliability, safety, and security become crucial because even minor faults may have serious consequences. Key challenges to be handled by the SDV OS include how to handle software bugs, perform real-time processing, address functional safety and SOTIF compliance, adhere to regulations, minimize attack surface exposure, and protect against remote access and data breaches. This is achieved via sound architectural principles, including a CSM for fine-grained access control, a lean and minimal kernel to reduce vulnerabilities, secure and efficient inter-process communication, and user-level drivers to provide better fault isolation. The key novelty of this approach rests on the fact that it uses open-source kernels, libraries, and tools that guarantee flexibility, clarity, and community-driven innovation. It provides a flexible runtime environment and OS-level isolation using virtualization, safe hardware sharing, and adherence to safety standards to set up the SDV OS as a resounding, secure, and future-ready base for next-generation automotive systems.
Khan, Misbah Ullah, Gupta, Vishal
A Machine Learning Framework for Fault Detection, Isolation, and Severity Prediction of Autonomous VTOL AircraftSM-2026-VLADA-52151/27/2026
Fault detection in autonomous VTOL aircraft is critical because even minor degradations can quickly destabilize multirotor vehicles in safety-critical environments. However, real-flight fault detection remains challenging due to sensor noise, environmental disturbances, and the nonlinear aeromechanics of multirotor platforms. This study proposes a comprehensive machine-learning framework for rotor fault detection, isolation, and severity prediction using real flight data. A convolutional neural network (CNN) architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level. The framework is first validated using simulated data generated by our in-house flight dynamic model. Next, to verify the framework using real flight data, a hexcopter was designed, fabricated and flight tested for both nominal and faulty cases by introducing controlled blade-tip breakage. The trained model achieves rotor-wise fault classification accuracies above 99% and sample-wise severity estimation accuracy of 96% within a ±1% tolerance in experimental data, demonstrating strong generalization and supporting real-time health monitoring for autonomous VTOL systems.
Sarker, Rippon, Dabaghian, Pedram, Halder, Atanu, Goyal, Raman
Self-Supervised Diagnostics & Prognostics Framework for Predictive Maintenance in Electric Vehicles2026-26-06731/16/2026
Predictive maintenance is critical to improving reliability, safety and operational efficiency of connected vehicles. However, classic supervised learning methods for fault prediction rely heavily on large-scale labeled data of failures, which are difficult to obtain and maintain a manually built dataset of failure events in real automotives settings. In this paper, we present a novel self-supervised anomaly detection model that makes predictions on the faults without the need for labeled failures by using only the operational data when the systems or robots are healthy. The method relies on self-supervised pretext tasks, like masked signal reconstruction and future telemetry prediction, to extract nominal multi-sensor dynamics (i.e., temperature, pressure, current, vibration) while jointly minimizing the deviation between encoded/decoded signals and normal patterns in the latent space. A unsupervised anomaly detection model is then used to detect when the learned patterns are violated. This in conjunction with data driven predictive allows for early fault detection on key subsystems such as batteries, electric motors, brake systems, and cooling systems. They tested the framework on some public benchmark datasets, and it’s pretty good at catching early anomalies with high accuracy and recall even better than the usual threshold-based methods. The study points out how important it is to use data from normal, healthy systems to build maintenance strategies that can scale well, adapt easily, and save costs, especially for connected vehicle fleets. Plus, the model helps explain what’s going on by identifying which telemetry signals are behind the anomalies, making it easier to take timely and practical maintenance actions. This work basically offers a new, practical way to keep vehicle health in check ahead of time, helping fleets stay up and running longer while cutting down on surprise breakdowns and expensive repairs.
Kumar, Pankaj, Deole, Kaushik, Hivarkar, Umesh
Testing Framework and Safety Evaluation for Vehicle-to-Vehicle (V2V) Charging Systems in Electric Mobility2026-26-01761/16/2026
This paper presents a comprehensive testing framework and safety evaluation for Vehicle-to-Vehicle (V2V) charging systems, incorporating advanced theoretical modeling and experimental validation of a modern, integrated 3-in-1 combo unit (PDU, DCDC, OBC). The proliferation of electric vehicles has necessitated the development of resilient and flexible charging solutions, with V2V technology emerging as a critical decentralized infrastructure component. This study establishes a rigorous mathematical framework for power flow analysis, develops novel safety protocols based on IEC 61508 and ISO 26262 functional safety standards, and presents comprehensive experimental validation across 47 test scenarios. The framework encompasses five primary test categories: functional performance validation, power conversion efficiency optimization, electromagnetic compatibility (EMC) assessment, thermal management evaluation, and comprehensive fault-injection testing including Byzantine fault scenarios. Through systematic experimental validation using advanced power electronics simulation and hardware-in-the-loop (HIL) testing, we demonstrate 98.2% power conversion efficiency, sub-50ms fault detection response times, and compliance with automotive safety integrity level ASIL-D requirements. Our results establish the theoretical foundations and practical validation methodologies essential for next-generation V2V charging infrastructure deployment.
Uthaman, Sreekumar, Mulay, Abhijit B, Nikam, Sandip B.
A Digital Twin Framework for Real-Time Optimization of Solar-Integrated Energy Systems2026-26-04181/16/2026
In the context of increasing global energy demand and growing concerns about climate change, the integration of renewable energy sources with advanced modelling technologies has become essential for achieving sustainable and efficient energy systems. Solar energy, despite its considerable potential, continues to face challenges related to performance variability, limited real-time insights, and the need for reactive maintenance. To overcome these barriers, this work presents a Digital Twin framework aimed at optimizing solar-integrated energy systems through real-time monitoring, predictive analytics, and adaptive control. This work presents a Digital Twin framework designed to address the challenges of designing, operating, maintaining, and estimating renewable energy systems, specifically solar power, based on dynamic load demand. The framework enables real-time forecasting and prediction of energy outputs, ensuring systems operate efficiently and maintain peak performance across diverse conditions. The proposed methodology mirrors the physical system using real-time data inputs, environmental conditions, and physics-based models to create a high-fidelity virtual replica. This allows for dynamic analysis of energy flows, load forecasting, system performance prediction, and scenario testing to optimize design and operational strategies. By integrating predictive analytics, Digital Twin adapts to changing conditions, enabling proactive maintenance, fault detection, and system calibration to meet future load demands. Experimental validation demonstrates that the framework improves system efficiency, adaptability, and reliability, with scalable applications for both centralized and decentralized energy systems. Additionally, its integration with cloud-based platforms and IoT technologies enables real-time monitoring, facilitating continuous optimization and data-driven decision-making. This Digital Twin approach provides an intelligent, data-driven solution for the renewable energy sector, facilitating sustainable, resilient, and efficient energy infrastructures that can reliably meet evolving load demands while optimizing performance throughout their lifecycle.
R, Akash, Burud, Priti Raju, Gumma, Muralidhar
Fatigue Failure Analysis for Automotive Transmission2026-26-05771/16/2026
A fatigue failure in the transmission input shaft was identified during a bench-level endurance test under 2nd gear loading conditions. The test transmission’s input shaft comprises fixed 1st, reverse, and 2nd gears, with the remaining gears mounted as floating. The shaft was subjected to cyclic torsional loads, and failure occurred after a defined number of cycles. Metallurgical analysis revealed a brittle fracture surface with crack initiation at the outer surface, propagating to core in a helical pattern, ultimately resulting in complete shaft fracture. To monitor and replicate the failure, the test setup was instrumented with a Reilhofer Delta Analyzer for early fault detection. TTL signals from accelerometers mounted on the transmission and a bench speed sensor were fed into the system, which generates FFT spectra and trend indices. A warning alarm triggered upon deviation in the trend index, indicating premature damage initiation. The test was subsequently halted for component inspection, revealing tool serration marks and initial hairline cracks near the 2nd gear location. Order analysis confirmed the dominance of the 2nd gear order and its harmonics. Additionally, the trend index energy was six times higher than baseline levels, indicating abnormal mechanical behavior. Finite element simulations were conducted on models with and without tool serration marks on the input shaft. The results showed significantly elevated stress concentration and shear stress in the failure zone for shafts with tool marks, whereas smooth shafts exhibited no severe stress concentrations. The input shaft was re-machined to eliminate tooling marks, and subsequent testing showed no abnormal trend index variation, confirming alignment with simulation predictions. This paper outlines the root cause analysis, simulation correlation, and mitigation strategy to prevent fatigue failure in rotating transmission components under cyclic loads. The methodology is scalable to similar components operating under dynamic loading environments.
Kushwaha, Rakesh, Patel, Hiral, Navale, Pradeep
A Machine Learning Approach to Early-Stage Failure Prediction in Traction Motor Manufacturing2026-26-02811/16/2026
Traction motors technology has, driving the EV industry forward with more efficient, lightweight, and durable solutions. However, despite these advancements, noise testing at the end of the production line remains a critical stage for identifying manufacturing defects in traction motors. Hence early fault detection in traction motors is crucial to ensure safety and reliability of EV. This research contributes a solution that predicts early-fault detection, supporting improved reliability, reduced material cost and minimizing process time in the series production line. To identify the root cause of this problem, historical quality data has been acquired from manufacturing plants to enable efficient analysis. Feature selection was then carried out using embedded and wrapper methods to identify the most important features. These selected features were subsequently used as input for ML models. The best accuracy was achieved using SVC model for early-stage motor failure prediction.
Gaikwad, Pooja, Nangare, Kapilraj, Suryawanshi, Chaitanya
Shaping the Future of Mobility with Innovative Tailgate Testing Solutions2026-26-04731/16/2026
As vehicles evolve toward increased automation and comfort, Power Operated Tailgate (POT) have become a common feature, especially in premium and mid-segment vehicles. These systems, although user-friendly on the surface, involve complex interactions between electronic control units (ECUs), sensors, actuators, and mechanical systems. Ensuring the reliability, safety, and robustness of these features under diverse operating conditions presents a significant validation challenge. Traditional testing methods, which rely heavily on physical prototypes and manual interaction, are often time-consuming, expensive, and prone to human error. Moreover, testing certain safety [3] features, such as anti-pinch or stall protection, under real physical conditions poses inherent risks and limitations. This paper presents a Hardware-in-Loop (HiL)[1] based testing approach for POT [2] systems, offering a safer, faster, and more comprehensive alternative to conventional validation methods. The HiL platform is built around a real-time test environment using Real Time Software, framework, integrated with MATLAB/Simulink [5] based plant models representing motor behaviour, hall sensors, and tailgate dynamics. The ECU under test communicates via CAN [4] and other physical I/Os, while the plant models simulate realistic vehicle responses in real time. The HiL approach enables full automation of functional, diagnostic, and safety validation of the tailgate system including open/close commands, fault injections (open circuit, short faults), latch and sensor logic, and anti-pinch scenarios. This methodology significantly reduces prototype dependence, accelerates ECU software validation, and increases overall test coverage. Results show substantial improvements in fault detection, regression testing efficiency. The proposed solution demonstrates how HiL [1] testing is not only a cost-effective validation method but also a strategic enabler for scalable and safe development of automotive mechatronic systems. This paper concludes by discussing the long-term benefits and future scope of enhancing the HiL setup with remote diagnostics, and seamless integration with other systems. The automotive industry is undergoing a transformation with a growing emphasis on comfort, convenience, and automation. Power Operated Tailgate (POT) have become an integral part of modern vehicles, offering hands-free access, anti-pinch
More, Shweta, Ghanwat, Hemant, Shetti, Suraj, Jape, Akshay, Kulkarni, Shraddha, Jagdale, Nitin
Powering the Future: Addressing Power Architecture Bottlenecks in Next Gen Automotive Platforms2026-26-06881/16/2026
As the automotive industry moves from conventional function oriented embedded ECU-based systems to Code-driven system, the core electrical and electronic (E&E) architecture is also being redesigned to support more software-driven functionality. Modern and centralized architectures promise scalability and software-driven flexibility, but they also introduce significant challenges in power distribution—an area that remains underexplored despite its critical role in overall vehicle safety and performance. Our paper aims at the adoption of the traditional power distribution approach for Next Gen vehicle architecture. It requires a fresh look at how power is distributed. In a novel E&E architecture, a single power harness supplies battery voltage to each zone. If there's a failure or voltage drop, it can affect multiple functions within that zone at once, and management of voltage regulation, thermal dissipation, and EMI/EMC compliance becomes crucial. Adding to the complexity, safety-critical systems need power redundancy and isolation to meet Functional Safety standards. Mixed-criticality designs further complicate power management, as they demand strict segregation between critical and non-critical power loads to preserve functionality under fault conditions. The integration of software-controlled power switching and dynamic power management introduces additional failure modes previously unrecognized. Consequently, real-time monitoring and power fault detection are becoming vital for maintaining the health of a vehicle’s power distribution network. Traditional diagnostics, such as On-Board Diagnostics, offer limited checks and periodic alerts, primarily for engine and transmission faults. Advanced capabilities are essential. Through an investigative lens, this paper identifies the key bottlenecks in power distribution and proposes areas for further research and innovation aimed at ensuring resilience, safety, and performance in next-generation vehicles.
Borole, Akash, Warke, Umakant, Chakra, Pipun, Jaisankar, Gokulnath
Hybrid Safety Framework for Autonomous Off-Highway Vehicles2025-28-032511/6/2025
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems. Additionally, emerging standards such as ISO21448 (SOTIF) used to detect and mitigate unsafe AI outputs, and ISO8800(AI safety) which focus on safety assurance for AI-based systems, offer valuable insights but requires additional adaptation. AI-driven systems are vulnerable to cyber threats that can endanger the vehicle safety. Integration of ISO21434 (cybersecurity for vehicles) and EU Cyber Resilience Act (CRA) has been added as a key regulatory framework with safety standards is highly needed to address safety failures induced by cyber threats. This paper introduces a hybrid safety framework that integrates ISO safety standards ISO26262, ISO19014, ISO12100, ISO25119, ISO21448, ISO21434, ISO 18497, ISO/CD 24882, EU Cyber Resilience Act and ISO8800 with AI innovations enhance the safety and reliability of autonomous OHVs. The proposed framework makes use of sensor fusion, explainable AI for transparent decision-making, especially in safety-critical scenarios, and a real-time fail-safe mechanism to manage critical failure scenarios such as power failures, communication loss, and sensor degradation by switching to a safe state. To confirm the effectiveness of this hybrid approach, digital twin simulation software along with additional technologies in OHV applications are used. The results demonstrate significant improvements in more accurate fault detection, efficient responses, and overall system resilience, highlighting the benefits of merging AI safety techniques with established ISO standards.
Muthusamy, Sugantha
Revolutionizing Vehicle Warranty Management with AI and Real-Time Data Integration2025-28-030411/6/2025
This paper introduces an AI-powered mobile application designed to enhance vehicle warranty management through real-time diagnostics, predictive maintenance, and personalized support. The system supports multi-modal inputs (text, voice, image, video), integrates real-time On-Board Diagnostics (OBD) data, and accesses OEM warranty terms via secure APIs. It employs supervised, unsupervised, and reinforcement learning to deliver accurate fault detection, tailored recommendations, and automated claim decisions. Contextual analysis and continuous learning improve precision over time. The application also provides service cost estimates, part availability, and proactive maintenance alerts. This approach improves customer satisfaction, reduces warranty costs, and streamlines aftersales support. Utilizing advanced AI and machine learning algorithms, the application interprets customer queries through multiple input modes—text, voice, video, and image—and retrieves relevant information from the manufacturer’s database to provide accurate and timely responses. Continuous data collection and learning (Model retraining monthly or quarterly as per new data availability) enhance the system’s precision over time, significantly improving customer satisfaction and support quality. Beyond warranty management, the application offers comprehensive features such as product quality assessments, tailored servicing plans, estimated service and replacement costs, part availability from nearby dealers, and streamlined warranty support requests. By analyzing contextual factors like vehicle make, model, usage patterns, and environmental conditions, the system delivers highly personalized responses. Integration with real-time On-Board Diagnostics (OBD) data further refines the app’s capabilities, enabling it to address customer concerns with precision. As the system evolves through ongoing data accumulation, its machine learning models continuously improve, ensuring increasingly accurate and relevant support. This holistic approach bridges the gap between vehicle owners and manufacturers, providing users with transparent, intelligent, and proactive warranty and maintenance solutions throughout the vehicle ownership lifecycle.
Ramekar, Vedant Madhav, Chaudhari, Hemant
Automating Fault Detection Time Interval in Automotive Functional Safety Using HIL Simulation2025-28-027411/6/2025
Functional safety is driven by number of standards like in automotive its driven by ISO26262, in Aerospace its driven by DO-178C, and in Medical its driven by IEC 60601. Automotive electronic controllers must adhere to state-of-the-art functional safety standard provided by ISO26262. A critical functional safety requirement is the Fault Handling Time Interval (FHTI), which includes the Fault Detection Time Interval (FDTI) and Fault Reaction Time Interval (FRTI). The requirements for FHTI are derived from Failure Mode Effect Analysis (FMEA) conducted at the system level. Various fault categories are analyzed, including electrical faults (e.g., short to battery, short to ground, open circuits), systemic faults (e.g., sensor value stuck, sensor value beyond range), and communication faults (e.g., incorrect CAN message signal values). Controllers employ strategies such as debouncing and fault time maturity to detect these faults. Numerous FDTI requirements must be verified to ensure compliance with FMEA-identified faults. Significant portion of total quantum of Test procedures of entire system are fault injection test cases, Manual testing of these cases is cumbersome, hence automating these tests is crucial for efficient regression testing. In HIL environment, ECU variables and communication signals are available for processing within tool which contains fault information which needs to be processed for FDTI calculations. The paper examines diverse strategies to handle the complexity of FDTI test cases in the HIL environment through automation, leveraging tool features, time trigger, time synchronization, post-processing techniques and real-time calculations during test execution to process FDTI calculations, ensuring thorough verification of functional safety requirements.
Lengare, Sunil, Yadav, Vikaskumar, Shiraskar, Pallavi
Safety Critical Series Arc Detection and Measurement in Medium and High Voltage DC Circuits2025-32-001211/3/2025
Direct current (DC) systems are increasingly used in small power system applications ranging from combined heat and power plants aided with photovoltaic (PV) installations to powertrains of small electric vehicles. A critical safety issue in these systems is the occurrence of series arc faults, which can lead to fires due to high temperatures. This paper presents a model-based method for detecting such faults in medium- and high-voltage DC circuits. Unlike traditional approaches that rely on high-frequency signal analysis, the proposed method uses a physical circuit model and a high-gain observer to estimate deviations from nominal operation. The detection criterion is based on the variance of a disturbance estimate, allowing fast and reliable fault identification. Experimental validation is conducted using a PV system with an arc generator to simulate faults. The results demonstrate the effectiveness of the method in distinguishing fault events from normal operating variations. The method is compared with a recursive least squares estimator, showing improved performance in terms of sensitivity. The approach offers a cost-effective and robust solution to improve safety in DC power systems, particularly in PV and electric mobility applications.
Winkler, Alexander, Mayr, Stefan, Grabmair, Gernot
Diagnostics Methodology for Turbocharger System Using Data Driven Models2025-28-040410/30/2025
In a conventional powertrain driven by Internal combustion (IC) engines, turbocharger (TC) is a key component for enhancing performance and efficiency. Predominantly turbochargers are used to serve multiple purposes of downsizing, increased power, better fuel efficiency, reduced emissions, and improved performance at high altitudes. TC is responsible for fulfilling the air mass requirement of the engine at different operating conditions. Failure of TC system leads to abnormal engine operation. If the TC hardware is beyond repair, the associated replacement cost is very high. Ultimately, a predictive diagnostics approach is required to identify the issue with TC so that the failure of TC could be avoided. The proposed methodology uses advanced artificial intelligence technique called recurrent neural network (RNN) and long short-term memory (LSTM) network for predicting faults in a typical TC system. In this study, actual values of TC speed and boost pressure are obtained from physical sensors present on the vehicle whereas estimated values of TC speed and boost pressure are obtained from data driven models. To enable predictive diagnostic of TC, a fault detection unit is incorporated which differentiates between the various fault conditions such as TC hardware fault or sensor fault. For initial validation, this methodology was applied to a healthy TC system to ensure that fault conditions were not getting active. For further validation, a faulty TC system was selected. Using the proposed approach, degradation in the boost pressure and TC speed for faulty TC was successfully identified. Various fault conditions and steps involved in the fault detection are clearly described.
Jagtap, Virendra Shashikant, Ganguly, Gourav, Mitra, Partha, Patidar, Sachin
Optimizing Electric Vehicle Motor Testing with Fine Gaussian SVM and Bilayered Neural Network2025-28-039710/30/2025
Electric vehicles are shaping the future of the automotive industry, with the drive motor being a crucial component in their operation. Ensuring motor reliability requires rigorous testing using specialized test benches to validate key performance parameters. However, inefficiencies in the helical gear configuration within these test systems have led to frequent malfunctions, affecting production flow. This study focuses on optimizing the motor test bench by refining critical design parameters through vibration signal analysis and machine learning techniques. Vibrational data is collected under different gear configurations, utilizing an accelerometer integrated with a Data Acquisition (DAQ) system and MATLAB-based directives for seamless data collection. Machine learning classifiers, including Fine Gaussian SVM and Bilayered Neural Network, are applied to categorize signals into normal and faulty conditions, both with and without a 0.25 KW load. The analysis reveals that SVM achieves an accuracy of approximately 85%, while the neural network attains 92%, demonstrating superior fault detection capabilities. This approach enhances the reliability and efficiency of motor testing, ultimately contributing to improved production processes in electric vehicle manufacturing.
S, Ravikumar, Sharik, N, Syed, Shaul, V, Muralidharan, D, Pradeep Kumar
Performance Investigation of Air Conditioning System in Electric Vehicle by Fault Detection and Diagnosis2025-28-035310/30/2025
The study emphasizes on detection of different faults and refrigerant leakage as well as performance investigation of automobile air conditioning system for an electric vehicle by varying various operating conditions. A refrigerant leak in an EV isn't just an inconvenience; it's a potential threat to vehicle range and usability, lifespan and health of the expensive battery pack, overall vehicle performance, passenger safety and comfort, component longevity (motor, power electronics), environmental responsibility. Due to the refrigerant leakage, the cooling system performance degrades, and components tend to fail. Because of that this study is focusing on deriving an algorithm to have an early detection of fault and leakage in the vehicle. The performance of the system is predicted for actual conditions of operation encountered by the automobile air conditioning system. The objective of the present work includes predicting the causes and effects of refrigerant leakage in AC system of Electric vehicle, developing numerical simulation model of the air conditioning system and an algorithm to detect different faults and predict the performance with and without leakage which uses the refrigerant R134a. For this purpose, a simulation model and a leak detection algorithm have been developed for a small automobile air conditioning system with R134a refrigerant. The Coefficient of Performance (COP), cooling capacity values are compared for both with and without refrigerant leakage in the AC system. For that, the temperature and pressure values of different points from the simulation model of the AC system are taken and used by the algorithm in stateflow model to detect the leakage in the system. After the complete analysis it has been noticed that the COP of the AC system drops significantly from 4.7 with 0% fault to 3.59 with 66% fault, and cooling capacity drops from 1.398kW with 0% fault to 0.46kW with 66% fault, which indicates the deterioration of the system over time.
Bezbaruah, Puja, Yadav, Ankit, Pilakkattu, Deepak
Automated Test Process for Highly Configurable Software-Defined Mobility Systems in CI/CD2025-01-02977/2/2025
In the pursuit of customizability and evolvability of vehicle functions, manufacturers shift towards software-defined vehicles to enable flexible customization and over-the-air updates. This results in multiple variants and versions of a vehicle model. While shifting to software-defined vehicles (SDVs) adds value and flexibility for customers, manufacturers struggle with homologating new and updated functionality because existing testing processes do not scale for high-frequency release cycles that limit available testing resources. Overcoming this challenge by using a coherent test process designed for testing continuously evolving variant-rich systems will be one of the key enablers. This paper presents an innovative end-to-end pipeline for efficient and comprehensive testing of variant-rich vehicle functionality tailored to an application in continuous development. Our transferable test pipeline employs sample-based variant selection, a software-in-the-loop environment for executing selected variants, and scenario-based testing using mutation-based scenario fuzzing. A central test controller is responsible for managing the process. We evaluate our test pipeline with regard to its fault detection capability, using the YOLO object detection algorithm as a variant-rich test object in the CARLA simulator. Our results show that the testing process outperforms random variant sampling and scenario mutation in detecting faults.
Hettich, Lennard, Pett, Tobias, Nägele, Ann-Therese, Schindewolf, Marc, Eriş, Halit, Wagner, Stefan, Sax, Eric, Schaefer, Ina, Weyrich, Michael
Leveraging Vision Language Models and Prompt Engineering for Bearing Defect Classification from Vibration Signals2025-01-01275/5/2025
Bearings are fundamental components in automotive systems, ensuring smooth operation, efficiency, and longevity. They are widely used in various automotive systems such as wheel hubs, transmissions, engines, steering systems etc. Early detection of bearing defects during End-of-Line (EOL) testing and operational phases is crucial for preventive maintenance, thereby preventing system malfunctions. In the era of Industry 4.0, vibrational, accelerometer, and other IoT sensors are actively engaged in capturing performance data and identifying defects. These sensors generate vast amounts of data, enabling the development of advanced data-driven applications and leveraging deep learning models. While deep learning approaches have shown promising results in bearing fault diagnosis, they often require extensive data, complex model architectures, and specialized hardware. This study proposes a novel method leveraging the capabilities of Vision Language Models (VLMs) and Large Language Models (LLMs) for accurate and efficient bearing defect classification. The dataset used in this study is sourced from the Case Western Reserve University (CWRU) bearing failure laboratory, comprising data on approximately 12 different bearing health conditions. The CWRU dataset is widely recognized as a benchmark for validating fault detection models. Vibration sensor data from the bearing is transformed into time-frequency spectrograms using Short-Time Fourier Transform (STFT). Advanced prompt engineering techniques guide the VLMs to extract discriminative features from these spectrograms. The extracted features are then processed by LLMs for defect classification. This approach achieved 90% overall F1 score in test set, comparable to state-of-the-art deep learning methods, while offering advantages in terms of simplicity, generalizability, and reduced computational requirements. Also, this methodology has broad applicability in various domains involving spectrogram analysis, particularly in similar noise and vibration signal applications.
Chandrasekaran, Balaji, Cury, Rudoniel
A Comparative Analysis of Fault Diagnosis by Vibration Signals for Critical Gear Components in Electric Vehicle Motor Testing Machines Using Machine Learning Algorithms2025-01-00405/5/2025
Electric vehicles (EVs) are shaping the future of mobility, with drive motors serving as a cornerstone of their efficiency and performance. Motor testing machines are essential for verifying the functionality of EV motors; however, flaws in testing equipment, such as gear-related issues, frequently cause operational challenges. This study focuses on improving motor testing processes by leveraging machine learning and vibration signal analysis for early detection of gear faults. Through statistical feature extraction and the application of classifiers like Wide Naive Bayes and Coarse Tree, the collected vibration signals were categorized as normal or faulty under both loaded (0.275 kW) and no-load conditions. A performance comparison demonstrated the superior accuracy of the wide neural networks algorithm, achieving 95.3%. This methodology provides an intelligent, preventive maintenance solution, significantly enhancing the reliability of motor testing benches.
S, Ravikumar, Sharik, N, Syed, Shaul, V, Muralidharan, D, Pradeep Kumar
Enhancing Efficiency in Manufacturing through Automated AI Agent-Based Bearing Defect Diagnostic System2025-01-01585/2/2025
Industrial bearings are critical components in aerospace, industrial, and automotive manufacturing, where their failures can result in costly downtime. Traditional fault diagnosis typically depends on time-consuming on-site inspections conducted by specialized field engineers. This study introduces an automated Artificial Intelligence virtual agent system that functions as a maintenance technician, empowering on-site personnel to perform preliminary diagnoses. By reducing the dependence on specialized engineers, this technology aims to minimize downtime. The Agentic Artificial Intelligence system leverages agents with the backbone of intelligence from Computer Vision and Large Language Models to guide the inspection process, answer queries from a comprehensive knowledge base, analyze defect images, and generate detailed reports with actionable recommendations. Multiple deep learning algorithms are provisioned as backend API tools to support the agentic workflow. This study details the architectural design of the agentic system and provides a real-time simulation of its workflow. In this study, inspection reports previously conducted by live technicians are used as a surrogate for simulating the diagnostic process carried out by agents. Validation of the system is studied by industry standard metrics like RAGAS comparing reports generated by field technicians versus AI agent generated reports. This feasibility study gets a score of 0.72, and it shows good promise for automating the time-consuming defect identification process. The concepts discussed can be extended to other similar problems, demonstrating their potential to enhance operational efficiency across sectors. This AI agentic workflow automation is constantly evolving, and further studies are needed to improve current performance levels and to mitigate the risk factors for productionizing this solution.
Chandrasekaran, Balaji
Development of a Robust Framework for Fault Detection and Quantification in Automotive Drum Brakes Using Vibration Measurements and ANN10-09-03-00244/15/2025
Passenger safety is of utmost importance in the automotive industry. Hence, the health of the components, especially the brake system, should be effectively monitored. On account of the significance of artificial intelligence in recent times, any brake fault resulting during operation can be accurately detected using a combination of advanced measurement techniques and machine learning algorithms. The current study focuses on developing and evaluating a robust framework to quantify and classify the faults of a general automotive drum brake. For this purpose, a new experiment for a drum brake, which can be operated under a controlled environment with known levels of faults, is developed. The experiment is instrumented to measure the fundamental dynamic signals (such as brake torque, the angular velocity of the brake drum, and brake shoe accelerations) during a braking event. The response signals from several experiments with various faults and operating conditions serve as the input dataset for establishing the fault quantification algorithm. Multiple variants of this algorithm are devised using different subsets of the input dataset. The selection of features in each variant is done through sensitivity-based segregation with the help of artificial neural networks. The performance of all the variants is comparatively evaluated, and the best among them is determined based on the fault quantification error. Finally, fault classification is carried out using the best variant after establishing the classification thresholds based on the confusion matrix. The following are the novel aspects of this work: (i) design and development of a laboratory experiment for drum brakes that can imitate a real-life braking condition; (ii) measurement of the dynamic response of the system during a typical braking event with a controlled type and level of brake fault using appropriate instrumentation; (iii) estimation of the magnitude of multiple brake faults, in addition to their classification; and (iv) identification of the critical vibration measurements necessary for detecting faults in brakes. In addition, the physical insights into the brake system response, selected features, and the fault quantification algorithm are presented. The proposed framework can also be implemented for fault diagnosis in different automotive subsystems by using an equivalent experiment. The goal of the current work is to develop a simple in situ tool for monitoring the health and diagnosing faults in automotive drum brakes. When integrated with other smart diagnostic and prognostic features, this tool can help automotive manufacturers improve passenger safety.
Yella, Akash, Bharinikala, Yuva Venkat Ajay, Sundar, Sriram
Cloud Based Digital Twin Development for High-Voltage Battery Systems in Commercial Vehicles2025-01-82144/1/2025
Over recent years, BorgWarner has intensified its efforts to explore and leverage trending technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to enhance products and processes. This includes digital twin technology, which has potential use cases for system behavior analysis, product optimization and predictive maintenance. This paper outlines the development process of a digital twin for a commercial vehicle battery, which serves as a demonstrator and learning platform for this technology. In order to assess the feasibility as well as hard- and software requirements, a cloud-based digital twin demonstrator was developed, integrating vehicle telemetry data with physics-based battery electric and thermal models, and an aging prediction algorithm. The key components are an Internet of Things (IoT) gateway, simulation models, data processing and ingestion pipelines, a machine learning algorithm for anomaly detection, and visualizations of telemetry and simulation data. A custom dashboard developed during the work enables monitoring of the battery's state of charge (SOC), state of health (SOH), and temperatures in real-time, as well as offline analysis of historical data. The below work gives an overview of tools and methods used and describes challenges and the corresponding solutions in building up a digital twin of a vehicle component in its use phase.
Bongards, Anita, Liu, Xiaobing, Beemer, Maria, Gajowski, Daniel, Rama, Neeraj, Shah, Keya, Fallahdizcheh, Amirhossein
A Fault Prediction Model for Electric Vehicle Charging Equipment Based on Adaptive Dynamic Thresholds2025-01-81174/1/2025
The surge in electric vehicle usage has expanded the number of charging stations, intensifying demands on their operation and maintenance. Public charging stations, often exposed to harsh weather and unpredictable human factors, frequently encounter malfunctions requiring prompt attention. Current methods primarily employ data-driven approaches or rely on empirical expertise to establish warning thresholds for fault prediction. While these approaches are generally effective, the artificially fixed thresholds they employ for fault prediction limit adaptability and fall short in sensitivity to special scenarios, timings, locations, and types of faults, as well as in overall intelligence. This paper presents a novel fault prediction model for charging equipment that utilizes adaptive dynamic thresholds to enhance diagnostic accuracy and reliability. By integrating and quantifying Environmental Influence Factors (EF), Scenario Influence Factors (SF), Fault Severity Factors (FF), and Charging Equipment Status Factors (CF) into a cohesive predictive framework, our model dynamically adjusts thresholds based on a comprehensive analysis of these factors. Using a dataset of 560,000 charging records from Hangzhou, the model employs a batch offline reinforcement learning approach based on a Markov Decision Process (MDP). Threshold adjustments are optimized via a Deep Q-learning Network (DQN) to maximize long-term rewards. The proposed system is evaluated through metrics such as advance warning time, alert precision, and recall rates. Results demonstrate the model’s ability to provide timely, accurate fault detection and enhance alert effectiveness, thereby improving the reliability and efficiency of electric vehicle charging networks.
Wang, Hao, Wang, Ning, Li, Yuan, Tang, Xinyue
LLM-Powered Fuzz Testing of Automotive Diagnostic Protocols2025-01-80914/1/2025
Modern vehicles contain tens of different Electronic Control Units (ECUs) from several vendors. These small computers are connected through several networking busses and protocols, potentially through gateways and converters. In addition, vehicle-to-vehicle and internet connectivity are now considered requirements, adding additional complexity to an already complex electronic system. Due to this complexity and the safety-critical nature of vehicles, automotive cyber-security is a difficult undertaking. One critical aspect of cyber-security is the robust software testing for potential bugs and vulnerabilities. Fuzz testing is an automated software testing method injecting large input sets into a system. It is an invaluable technique across many industries and has become increasingly popular since its conception. Its success relies highly on the “quality” of inputs injected. One shortcoming associated with fuzz testing is the expertise required in developing “smart” fuzz testing tools (fuzzers). Developing a fuzzer requires expertise on various topics, from input types and underlying networks to potential system configurations. Moreover, fuzzers are generally not transferable between different systems, limiting their reuse. This study investigates whether Generative AI technologies can meaningfully assist in their development by comparing an AI-generated fuzzer against a commercial one. An automotive fuzzer focusing on Unified Diagnostic Services (UDS) was developed by exclusively querying an AI model. First, the pre-trained AI is taught the underlying structure and constraints of UDS and is then used to generate semantically valid test cases. The effectiveness of test cases for vulnerability and fault detection is evaluated. The impact of specific queries and the underlying protocol network configurations on the generated test cases is then investigated through comparison with a commercial fuzzer.
McShane, John, Celik, Levent, Aideyan, Iwinosa, Brooks, Richard, Pesé, Mert D.
A Comparative Study on Fault Diagnosis of Electrical Vehicle Motor Testing Machine Gear Components Using Machine Learning Algorithms2025-28-01772/7/2025
Electric vehicles (EVs) are paving the way for future mobility, with drive motors playing a central role in their efficiency and performance. Motor testing machines are crucial for validating EV motors, yet flaws in testing equipment, such as gear issues, often lead to operational disruptions. This study aims to enhance motor testing by implementing machine learning and vibration signal analysis to detect gear faults early. Using statistical feature extraction and classifiers like Quadratic SVM and Bagged Trees, the collected vibration signals are categorized as normal or faulty under loaded (0.275 kW) and no-load conditions. Performance comparison reveals the Bagged Trees algorithm's superior accuracy of 95.3%. This approach offers an intelligent, preventive maintenance solution, improving the motor test bench’s reliability.
S, Ravikumar, Syed, Shaul, V, Muralidharan, D, Pradeep Kumar
Fault Diagnosis of PEMFC Based on Fast EIS Measurement and Optimized Random Forest Algorithm2025-01-70801/31/2025
Electrochemical impedance spectroscopy (EIS) is often used for fault diagnosis as an important parameter to characterize the state of fuel cells. However, online diagnosis requires high real-time performance and usually can only measure single-frequency or dual-frequency impedance. Too few diagnostic features make it difficult for traditional fault diagnosis methods based on EIS to ensure high accuracy. Therefore, this paper proposes a fault diagnosis method based on fast EIS measurement and an optimized random forest algorithm. Firstly, using a multi-sine excitation signal to realize the simultaneous measurement of multi-frequency impedance, provides more health status information in a single measurement. To solve the problem of large signal peaks caused by the superimposed signals, the phase is optimized by the genetic algorithm, which reduces the crest factor of the excitation signal. Then, multi-frequency impedance is used as a training feature for the random forest (RF) algorithm to realize the diagnosis of flooding and drying faults. The particle swarm optimization (PSO) algorithm is used to optimize the algorithm's hyperparameters to improve the identification accuracy. Finally, experimental verification is carried out based on the fault dataset of an automotive fuel cell, and the results show that the accuracy of the proposed algorithm can reach 99%, which is better than other common methods.
Ni, Shengqi, Zhang, Cunman, Zhu, Yuan, Zhong, Xiaolong
Safety Analysis of Traction Batteries Based on Micro Internal Short Circuit Damage2025-01-70171/31/2025
The internal short circuit of a traction battery is one of the most typical failure mechanisms that can lead to thermal runaway, potentially triggering thermal propagation across the entire battery system. This phenomenon poses significant safety risks, especially in electric vehicles and large-scale energy storage systems. Therefore, it is essential to explore and understand the internal short circuit behavior to mitigate these risks. One of the most effective testing methods for reproducing an internal short circuit is the penetration test, where specific test conditions must be carefully designed based on the failure behavior. Among these conditions, the penetration step length plays a crucial role, as it directly influences the short circuit dynamics. Despite the importance of penetration step length, there is currently no standardized test procedure that dictates how to select the appropriate step size for different battery samples. This gap in standardization complicates the ability to replicate internal short circuit behavior consistently across various battery chemistries and configurations. In this study, penetration tests were conducted using a thin nail with varying step lengths to better understand the impact of these parameters on the electrical and thermal responses during failure. The analysis of the evolution of electrical and thermal parameters under different conditions provides valuable data for developing a universal test procedure. Such a procedure would facilitate the accurate simulation of internal short circuits, ultimately improving battery safety by helping engineers design more resilient battery systems and establishing benchmarks for industry-wide testing standards.
Wang, Fang, Sun, Zhipeng, Ma, Tianyi, Dai, Xiaoqian, Dai, Ce, Yan, Pengfei, Ma, Xiaole, Chen, Liduo, Ma, Haishuo, Shen, Shaopeng
Data Generation and Preliminary Fault Detection of Power Battery Based on GAN Algorithm2025-01-70091/31/2025
The safety of power batteries is an important issue that has attracted widespread attention in new energy vehicle technology. In this paper, Generative Adversarial Networks (GAN) are introduced, and the data generation and fault diagnosis of power battery life-cycle data are carried out. GAN is composed of a pair of generators and discriminators, combining signal processing with neural networks, using the discriminator architecture based on Fourier transform and the generator architecture based on wavelet transform, so that the neural network can learn the characteristics of power battery life-cycle data from the perspective of time and frequency domain, and use the good performance of wavelet transform in data denoising and repair to generate high-quality and low-noise data, and use Fourier transform to target the characteristics of periodicity. Identify and distinguish the periodic characteristics and time-frequency domain data characteristics in the generated data and laboratory data. The results show that the GAN architecture adopted in this paper can generate high-quality power battery charge and discharge cycle data, and can observe the location of power battery fault data.
Tan, Piqiang, Yang, Aoji, Liu, Xiang, Yao, Chaojie
Fault Diagnosis of Proton Exchange Membrane Fuel Cells Based on Deep Learning and Transfer Learning2025-01-70761/31/2025
To accurately identify the fault types of proton exchange membrane fuel cell (PEMFC) systems under continuously varying operating currents, this study develops a comprehensive PEMFC system model and proposes a robust fault diagnosis method based on the ResNet50 convolutional neural network (CNN) and transfer learning (TL). Initially, using Matlab/Simulink, a PEMFC model is constructed based on the electrochemical reaction mechanisms and empirical formulas that characterize the operation of the fuel cell. This model primarily includes the fuel cell stack and various auxiliary systems, such as the thermal management system, air supply system, and hydrogen supply system, each crucial for optimal performance. By varying the model parameters, sensor data is generated for five distinct operating conditions. After preprocessing the data, the Gramian Angular Field (GAF) technique is utilized to convert the time series data from each sensor into fault data images, which then serve as input for the ResNet50 CNN. Ultimately, the implementation of transfer learning involves utilizing the pre-trained weights of the ResNet50 model in the training process of this model. This approach aims to improve both the convergence rate and the generalization capacity of the classification model. A comprehensive dataset for fault diagnosis has been established, comprising a total of 4,000 samples, with 800 image samples generated for each distinct operating state. The diagnostic results demonstrate that the integrated PEMFC system attains an exceptional diagnostic accuracy of 100.0% across five distinct operational scenarios: standard operating conditions, reduced air pressure at the compressor inlet, increased air temperature at the compressor inlet, heightened stack temperature, and an obstructed anode gas supply line. These results demonstrate that the proposed method not only exhibits high classification accuracy but also displays remarkable robustness in fault diagnosis applications.
Zhu, Shaopeng, Wang, Yifeng, Xiong, Qinghui, Geng, Jun, Chen, Huipeng
Research on Weak Fault Diagnosis Method of Dual - Axes Precision Gear Transmission System2025-01-70521/31/2025
This paper focuses on the weak fault diagnosis of a dual - axes precision gear transmission system. Firstly, it elaborates on the structure and working principle of the system. Comprising components like azimuth and pitch channels, motors, and control units, the pitch channel's gear transmission chain is a key research area. Subsequently, fault modes and their harmfulness are analyzed. Different faults such as tooth surface wear and pitting are considered. These faults can lead to serious consequences like system failure and mission deviation. Based on this, a test system is constructed. It includes sensors and a data acquisition system to simulate faults and collect vibration signals. The signals are then analyzed to understand the system's behavior. Finally, a weak fault feature index based on time - domain entropy is developed. A threshold setting method based on severity index is also proposed. These methods together enable the accurate diagnosis of weak faults in the system, which is crucial for ensuring the proper functioning and reliability of the dual - axes precision gear transmission system.
Han, Wei, Chang, Yingjie
Multi-step Prediction of Battery Temperature and Thermal Fault Diagnosis Based on Informer2025-01-70191/31/2025
Lithium-ion batteries are prone to thermal failures under extreme conditions, leading to thermal runaway and safety risks such as fire or explosion. Therefore, effective temperature prediction and diagnosis are crucial. This paper proposes a thermal fault diagnosis method based on the Informer time series model. By extracting temperature-related features and conducting correlation analysis, a 9-dimensional input parameter matrix is constructed. Experimental results show that the model can maintain an absolute temperature prediction error within 0.5°C when predicting 10 seconds in advance, with higher accuracy than the LSTM model. Additionally, a three-level warning mechanism based on the forgetting coefficient further enhances diagnostic accuracy. Validation using test data and real vehicle data demonstrates that this method can efficiently diagnose and locate thermal faults in batteries, with low computational costs, making it suitable for online applications.
Sun, Yefan, Zhu, Xiaopeng, Zhang, Zhengjie, Peng, Zhaoxia, Yang, Shichun, Liu, Xinhua
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