Browse Topic: Fault detection

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Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry2026-01-0161To be published on 04/07/2026
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. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. 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. The core of the approach is 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
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
Bearing Fault Diagnosis Method Based on Optimized VMD and Deep Timing Fusion2026-01-0158To be published on 04/07/2026
In the current field of rolling bearing fault diagnosis, two critical challenges persist. First, it is difficult to effectively extract fault features from nonlinear and non-stationary vibration signals. Second, precise diagnosis remains a challenge, especially when distinguishing between different fault types and capturing incipient faults with weak characteristic information. To address these issues, this paper proposes a novel fault diagnosis method based on adaptively optimized Variational Mode Decomposition (VMD) and Deep Temporal Fusion. First, the method improves the traditional Sparrow Search Algorithm (SSA). It enhances SSA’s global optimization capability through strategies like chaotic population initialization and adaptive perturbation. This improved SSA enables efficient global optimization of VMD’s key parameters. Leveraging these optimized parameters, the method decomposes modal signal components with different center frequencies from the vibration signal. This process
Wen, Chao, Zhong, Hong, Wang, Liangmo, Chen, Yong, Gao, Qiang, Li, Yong
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
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
Gao, Zhengpeng, Gao, Pingping, Chang, Penghui, Liu, Gang, Wu, Ji
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
Yang, Zhan, Chen, Xiaobo, Zheng, Ruixiang, Li, Mian
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
Khan, Misbah Ullah, Gupta, Vishal
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
R, Rajarajeswari, N, Kalaiarasi, Francis, Elgin Calister
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
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
Uthaman, Sreekumar, Mulay, Abhijit B, Nikam, Sandip B.
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
Kumar, Pankaj, Deole, Kaushik, Hivarkar, Umesh
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
Kushwaha, Rakesh, Patel, Hiral, Navale, Pradeep
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
Borole, Akash, Warke, Umakant, Chakra, Pipun, Jaisankar, Gokulnath
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
R, Akash, Burud, Priti Raju, Gumma, Muralidhar
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
More, Shweta, Ghanwat, Hemant, Shetti, Suraj, Jape, Akshay, Kulkarni, Shraddha, Jagdale, Nitin
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
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
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
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
Winkler, Alexander, Mayr, Stefan, Grabmair, Gernot
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
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
Bezbaruah, Puja, Yadav, Ankit, Pilakkattu, Deepak
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
Jagtap, Virendra Shashikant, Ganguly, Gourav, Mitra, Partha, Patidar, Sachin
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
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
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
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
Yella, Akash, Bharinikala, Yuva Venkat Ajay, Sundar, Sriram
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
McShane, John, Celik, Levent, Aideyan, Iwinosa, Brooks, Richard, Pesé, Mert D.
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
Wang, Hao, Wang, Ning, Li, Yuan, Tang, Xinyue
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
Bongards, Anita, Liu, Xiaobing, Beemer, Maria, Gajowski, Daniel, Rama, Neeraj, Shah, Keya, Fallahdizcheh, Amirhossein
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
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
Wang, Fang, Sun, Zhipeng, Ma, Tianyi, Dai, Xiaoqian, Dai, Ce, Yan, Pengfei, Ma, Xiaole, Chen, Liduo, Ma, Haishuo, Shen, Shaopeng
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
Zhu, Shaopeng, Wang, Yifeng, Xiong, Qinghui, Geng, Jun, Chen, Huipeng
Fault Diagnosis Strategy for Proton Exchange Membrane Fuel Cells Based on PSO-BP Neural Network Using Particle Swarm Optimization Algorithm2025-01-70851/31/2025
This paper presents a fault diagnosis strategy that integrates model-based and data-driven approaches for a 115 kW proton exchange membrane fuel cell used in vehicles. First, a stack subsystem model was developed in the MATLAB/Simulink platform based on the working principles and structure of PEMFC, and validated with experimental data. Subsequently, faults in the air and hydrogen inlet pipelines were simulated, and the resulting fault data were subjected to preprocessing steps, including cleaning, normalization, and feature extraction, to enhance the efficiency of subsequent data processing. Finally, a BP neural network optimized by particle swarm optimization was employed to achieve fault tree-based classification diagnosis. Experimental results indicate that the diagnosis accuracy of the BP neural network reached 96.04%, with an additional accuracy improvement of approximately 2.4% after PSO optimization.
Wang, Ze, Zhu, Shaopeng, Chen, Ping, Li, Congxin, Zhou, Wenhua
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
Han, Wei, Chang, Yingjie
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
Ni, Shengqi, Zhang, Cunman, Zhu, Yuan, Zhong, Xiaolong
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
Tan, Piqiang, Yang, Aoji, Liu, Xiang, Yao, Chaojie
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
Fault Detection of Diesel Engines Using Artificial Neural Network03-18-02-00101/20/2025
This study presents a method for identifying the reliability state of diesel engines by utilizing artificial neural networks (ANNs). The Sulzer 6AL20/24 marine diesel engine was selected as the test subject for this research. Vibration signals were collected during tests conducted on a laboratory test stand under normal operating conditions and during simulations of six different engine faults. Next, the recorded signals were analyzed and transformed into labeled samples for supervised learning. In this phase, the time histories of the vibration signals were divided into segments and augmented, with several key features calculated for each segment. Highly correlated signals were excluded from further analysis based on the Pearson correlation coefficient. The processed samples were then used to train and fine-tune the ANN. The trained ANN was subsequently used to identify the engine’s reliability state and classify the present fault type. To evaluate the effectiveness of the proposed
Pająk, Michał, Kluczyk, Marcin, Muślewski, Łukasz, Lisjak, Dragutin
Digital Twin-Enabled Fault Detection for Suspension Systems in Autonomous Mining Haulage Vehicles2024-36-017012/20/2024
Autonomous vehicles for mining operations offer increased productivity, reduced total cost of ownership, decreased maintenance costs, improved reliability, and reduced operator exposure to harsh mining environments. A large flow of data exists between the remote operation and the ore haul vehicle, and part of the data becomes information for the maintenance sector which it monitors the operating conditions of various systems. One of the systems deserving attention is the suspension system, responsible for keeping the vehicle running and within a certain vibration condition to keep the asset operational and productive. Thus, this work aims to develop a digital twin-assisted system to evaluate the harmonic response of the vehicle’s body. Two representations were created based on equations of motion that modeled the oscillatory behavior of a mass-damper system. One of the representations indicates a quarter of the ore transport truck’s hydraulic system in a healthy state, called a virtual
Rosa, Leonardo Olimpio, Branco, César Tadeu Nasser Medeiros
Development and Simulation of an AUTOSAR Software Component for Functional Safety according to ISO 26262 Fault Detection and Safety Mechanisms2024-36-013212/20/2024
In the context of advancing automotive electronic systems, ensuring functional safety as per ISO 26262 standards has become of primary importance. This paper presents the development of an AUTOSAR-compliant Software Component (SWC) applied to ISO 26262 applications. Using MATLAB/Simulink, we design and simulate a SWC that operates within the AUTOSAR architecture, focusing on fault detection and activation of safety mechanisms. The SWC is built to monitor specific system parameters and operational anomalies. Upon detecting a fault, it triggers predefined safety mechanisms to mitigate risks and ensure system integrity. The simulation focus on capability to accurately identify faults and execute safety measures effectively, thus demonstrating a practical approach to enhance automotive system safety implementation and its reuse. This paper not only highlights the importance of ISO 26262 in the automotive industry but also illustrates the feasibility of developing and integrating safety
Santiago, Frederico Victor Scoralick, dos Santos Machado, Cleberson, Imbasciati, Henrique, Costa, Silvio Romero Alves
Redundant Brake Boost Control Strategy of Integrated Electro-hydraulic Braking System2024-01-703812/13/2024
The traditional braking system has been unable to meet the redundant safety requirements of the intelligent vehicle for the braking system. At the same time, under the change of electrification and intelligence, the braking system needs to have the functions of braking boost, braking energy recovery, braking redundancy and so on. Therefore, it is necessary to study the redundant braking boost control of the integrated electro-hydraulic braking system. Based on the brake boost failure problem of the integrated electro-hydraulic brake system, this paper proposes a redundant brake boost control strategy based on the Integrated Brake Control system plus the Redundant Brake Unit configuration, which mainly includes fault diagnosis of Integrated Brake Control brake boost failure, recognition of driver braking intention based on pedal force, pressure control strategy of Integrated Brake Control brake boost and pressure control strategy of Redundant Brake Unit brake boost. The designed control
Dexing, Lao, Luping, Yan, Qinghai, Sui, Long, Cao, Shang, Gao, Zhigang, Chen, Mingxing, Ren, Zhicheng, Chen
Anomaly Detection in Fleet Vehicle Data and Statistical Approach to Develop Engine System OBD Thresholds2024-28-019512/5/2024
On-Board-Diagnostics (OBD) are crucial for ensuring the proper functioning of Engine’s emission control system by continuously monitoring various sensors and components. When the failure is detected, the Check Engine Light is triggered on Vehicle’s dashboard, alerting the driver to seek professional service to address the issue. However, the task of developing the monitoring strategies and performing robust calibration is challenging and time consuming. Model in loop (MIL) Simulation and testing is a technique used to understand and estimate the behavior of a system or sub system. The diagnostics model can be tested and refined within the model-based environment allowing a complex system to be efficiently regulated. MIL framework could be explored at various stages of development from early in the design phase to later stages of series developments through vehicle fleet data. This framework allows early identification and correction of errors and bugs in a standalone dependent
Kumar, Amit, Hegde, Karthik, Challa, Krishna, H, YASHWANTH
Safe Phase Current Measurement for Motor Control Applications2024-28-014112/5/2024
As the automotive industry progresses towards electrification, driven by need for sustainability and reduced emissions, the traction inverter emerges as pivotal component of electric vehicles (EVs). Serving as the interface between the vehicle’s control systems, motor and battery. The traction inverter’s performance directly impacts the efficiency, sustainability and overall functionality of electric drive systems. A critical function of the traction inverter is measurement of phase currents in each motor phase, enabling precise control of the motor’s torque and rational speed. This capability is essential for optimizing efficiency, enhancing performance and ensuring safety key aspects of modern electric vehicle technology. This paper introduces method for measuring phase currents in Permanent Magnet Synchronous Motors (PMSM) utilizing the Enhanced Versitile Analog-Digital-Converter (EVADC) integrated within Infineon’s Aurix Tricore. This technology preferred for its rapid conversion
Birari, Ashwini Anil
Equalization System Engineering Design for reliable Battery Management on eVTOL2024-01-702711/15/2024
Electrical vertical take-off and landing vehicle (eVTOL) are more and more popular in future’s urban mobility. How to improve the reliability of the battery, is the key problem. Battery Management System (BMS) through the battery status monitoring, charging and discharging control, temperature management, fault diagnosis, battery equalisation and other core measures to improve the battery reliability and performance, of which battery equalisation technology plays a vital role. BMS manages batteries through battery status monitoring, charging and discharging control, temperature management, fault diagnosis, battery equalisation and other core measures to ensure the safety, reliability and performance of batteries. This paper analyses the inconsistency mechanism of batteries, introduces the classification of mainstream balancing circuits, describes the advantages and disadvantages of different types of balancing technologies, introduces the practical application scheme of passive
Feng, Guo, Zhang, Xinfeng, Li, Hong Dun, Yue, Han
Knowledge Push Mechanism of Rolling Bearing Fault Diagnosis Employing Deep Residual Network Architecture2024-01-60049/11/2024
Rolling bearings play a critical role in rotating machinery, with their fatigue life directly impacting equipment’s operational reliability. This underscores the significant engineering application value of “fault diagnosis” (FD) technology for rolling bearings in mechanical, automation, and aerospace domains. Literature reviews highlight that a substantial portion of failures in machinery such as jet turbine engines, wind turbines, gear reducers, and induction machines are attributable to bearing issues. Early fault detection and preventive maintenance are therefore imperative for ensuring the smooth operation of rotating machinery. This paper focuses on rolling bearings, delving deep into FD technology using machine learning principles. It analyses the structure and common failure modes of rolling bearings, discussing an FD method based on machine learning. Specifically, the SE-DRN (“squeeze-exclusion deep residual network”) approach is employed, leveraging “variational modal
Muin, Abdullah-Al, Khan, Shahrukh, Miah, Md Helal
Fault Detection in Machine Bearings Using Deep Learning2024-26-04736/1/2024
In the contemporary industrial landscape, machinery stands as the cornerstone of various sectors. Over time, these machines undergo wear and tear due to extensive use, leading to the introduction of subtle faults into the machine readings. Recognizing the pivotal role of machinery in diverse industries, the timely detection of these faults becomes imperative. Early fault detection is crucial for preventing costly downtimes, ensuring operational efficiency, and enhancing overall safety. This paper addresses the need for an effective condition monitoring and fault detection system, focusing specifically on the application of the Long Short-Term Memory (LSTM) deep learning model for fault detection in bearings using accelerometer data. The preprocessing phase involves extracting time domain features, encompassing normal, differentiated, integrated, and carefully selected signals, to create an informative dataset tailored for the LSTM model. This model is then meticulously trained on the
Vaishnavi, A., Sharma, Anju, Naidu, VPS
Deep Learning-Based Digital Twining Models for Inter System Behavior and Health Assessment of Combat Aircraft Systems2024-26-04786/1/2024
Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviors and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining those outputs during the model integration phase, an overall health assessment of the aircraft can be generated. This assessment enables advanced fault isolation at the system level by identifying subtle
Agrawal, Ankur, Farid, Fahad, Prabhu, Aniruddh, Sudhakar, Veluri, Vyas, Nalinaksh
Formal Technique for Fault Detection and Identification of Control Intensive Application of Stall Warning System Using System Theoretic Process Analysis2024-26-04716/1/2024
Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft. Traditional Functional Hazard and Fault Tree analyses are inadequate to capture all failures due to the complex hardware-software interactions for stall warning and protection system. Hence, an improved methodology for failure detection and identification is proposed. This paper discusses a hybrid formal method and model-based technique using System Theoretic Process Analysis (STPA) to identify and diagnose faults and provide monitors to process the identified faults to ensure robust design of the indigenous stall warning and protection system (SWPS). The
Kale, Alexander, Madhuranath, Ganesh, Shanmugham, Viswanathan, Nanda, Manju, Singh, Giresh, Durak, Umut
Machine Learning Approach for Open Circuit Fault Detection and Localization in EV Motor Drive Systems2024-01-27904/9/2024
Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network. This recurrent neural network (RNN)-based ML model as a multiclass classifier localizes the
Arsalan, Ali, Papari, Behnaz, Rahman, S M Imrat, Timilsina, Laxman, Moghassemi, Ali, Muriithi, Grace, Ozkan, Gokhan, Edrington, Christopher, Buraimoh, Elutunji
Improving CRC Fault Detection Probability in AUTOSAR E2E Based on Known Hamming Weights2024-01-19874/9/2024
To develop safe vehicles, system development must be performed in compliance with functional safety. Functional safety considers situations where failures could make a vehicle unsafe, and it requires the inclusion of mechanisms to detect and mitigate these failures, even though they may not always be detected with 100% certainty — referred as diagnostic coverage (DC). Therefore, some faults, called residual faults, might go undetected. In the realm of functional safety from a communication perspective, industry standards define nine distinct fault modes. The detection of these faults is crucial, especially in the widely used AUTOSAR automotive operating system. AUTOSAR E2E (End-to-End Communication Protection) serves as a communication fault detection mechanism utilizing three mechanisms: counters, timers, and Cyclic Redundancy Check (CRC) to address the nine fault modes. Especially, determining the DC for CRC can be challenging and often requires a conservative evaluation approach. In
Emi, Taichi, Aung, Han Nay, Yamasaki, Yasuhiro, Ohsaki, Hiroyuki
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