Browse Topic: Fault detection
The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
This paper addresses the determinacy issue of multi-task execution in the Remote Data Conversion Unit of an integrated modular avionics (IMA) system in a non - operating system environment. A three - level hierarchical static scheduling table architecture for the Remote Data Conversion Unit is proposed. In this architecture, the maximum execution cycle of functional parameters is used as the device scheduling table cycle, the minimum execution cycle is used as the scheduling block cycle, and the worst - case execution time is used as the functional execution time. Through hierarchical design, the orderly connection of functions, scheduling blocks, and devices is achieved. A periodic interrupt mechanism is adopted between scheduling blocks to ensure time alignment at the scheduling block cycle level. Inside the scheduling block, a polling mechanism is used to perform static sorting according to the worst - case execution time of functions, and a wait function is introduced to achieve time alignment and fault isolation. For fault - tolerant faults, a delayed response strategy is adopted to avoid violating atomicity. For non - fault - tolerant faults, rapid detection and restart processing are achieved relying on periodic interrupts. A Simulink model is used to conduct a comparative simulation of the Remote Data Conversion Unit using a competition mechanism and the scheduling table mechanism. The results show that under normal and fault conditions, this architecture significantly reduces data transmission jitter, improves system determinacy and fault - tolerance ability, meets the requirements of the DO - 297 standard for functional independence and safety, and provides an effective solution for improving the determinacy of the civil aviation Remote Data Conversion Unit.
The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes refined class-level alignment, strengthens fault states representation, and shows notable superiority in both accuracy and robustness.
Conveyor belt fault detection is critical for ensuring the safety and efficiency of industrial material transportation. In this study, a screen-printed flexible strain sensor based on a thermoplastic polyurethane (TPU) substrate and graphene conductive ink was fabricated. The sensor exhibited excellent flexibility, mechanical robustness, and stable electromechanical performance. Comprehensive evaluations were conducted, including microstructural analysis, strain sensitivity, hysteresis, dynamic response, and long-term cycling stability. The results demonstrated that a two-layer graphene configuration achieved an optimal balance between sensitivity and structural stability, showing high gauge factor, fast response, and reliable cyclic performance. Furthermore, the sensor was applied to conveyor belt fault monitoring. Experiments validated its ability to detect both halting faults and foreign object intrusions, with distinctive resistance signal features enabling not only fault occurrence detection but also identification of fault location, type, and severity. These findings highlight the potential of the proposed flexible sensor system as a promising solution for intelligent conveyor belt monitoring in harsh industrial environments.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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