Aug 2026· Transactions of the Institute of Measurement and Control· 0 citations· 12 references
TL;DR
An ensemble subspace k-nearest neighbour model for fault detection and classification of switch open-circuit and short-circuit faults and indicates that the proposed model provides an effective and computationally efficient solution for reliable fault diagnosis in electric vehicle motor drive systems.
Abstract
Fault diagnosis in electric vehicle motor drive systems is essential to ensure reliable and safe operation. Especially in voltage source inverter-fed permanent magnet synchronous motor drives, switch faults need attention as they are the most fault-prone. Furthermore, the power switch faults, including open-circuit and short-circuit conditions, can significantly affect system performance. However, existing model- and signal-based fault diagnosis techniques often face limitations such as dependency on precise modelling and often give limited accuracy under dynamic operating conditions. On the contrary, existing data-driven approaches experience difficulties from feature redundancy and reduced generalisation in high-dimensional datasets. To address these challenges, this paper proposes an ensemble subspace k-nearest neighbour model for fault detection and classification of switch open-circuit and short-circuit faults. From a developed simulation model, the datasets under normal and multiple fault conditions (F0–F8), incorporating variables such as three-phase currents, are collected. Furthermore, the statistical features are extracted and subsequently reduced using principal component analysis to enhance feature representation and computational efficiency. The proposed model is evaluated using fivefold cross-validation and compared with other machine learning approaches, including bagged trees, quadratic support vector machine, boosted trees, and a neural network. The results reveal that the proposed subspace k-nearest neighbour model attains a testing classification accuracy of 98.49%, with an average precision of 97.5%, a recall of 98.3%, and an F1 score of 97.8%, surpassing the comparative models. These results denote that the proposed subspace k-nearest neighbour provides an effective and computationally efficient solution for reliable fault diagnosis in electric vehicle motor drive systems.
Accurate and reliable fault detection and isolation (FDI) in lithium-ion battery (LIB) systems is essential for ensuring safety, optimal performance, and lifespan, particularly as LIBs power critical applications such as electric vehicles and renewable energy storage. Traditional model-based diagnostics face limitations in handling sudden and compound faults due to modeling uncertainties and transient dynamics. This study proposes an experimental/synthetic hybrid, data-driven FDI framework that leverages supervised machine learning (ML) integrated with an experimentally validated second-order electro-thermal battery model to generate a mixed experimental–synthetic dataset covering healthy operation and diverse fault conditions, including cell, connection, and sensor faults. Time-domain statistical features are extracted from current, voltage, and temperature measurements, and nature-inspired wrapper-based algorithms are employed for feature selection, while multiple ML classifiers—Random Forest (RF), Decision Trees (DTs), K-Nearest Neighbors (KNN), and Naive Bayes (NB)—are comparatively evaluated on a six-class diagnosis problem. The best-performing configurations achieve near-perfect multi-class accuracy of approximately 99.80–99.87% with low variance across repeated train–test splits, demonstrating high robustness and stability overall. Rather than identifying a single universally optimal model, the results indicate a practical trade-off between diagnostic performance and implementation cost. Within this group, the RF classifier combined with Particle Swarm Optimization (PSO) for feature selection (RF–PSO) attains slightly higher accuracy and stability than the other top combinations, while DT–PSO and KNN–MPA (Marine Predators Algorithm) yield much smaller models, shorter inference times, and more compact feature subsets, which are attractive for constrained embedded implementations and large pack deployments. The proposed framework thus enables fast, real-time diagnosis of complex multi-fault scenarios at the cell or module level in series–parallel LIB pack architectures and allows practitioners to select the most suitable classifier–optimizer pair according to application-specific priorities on accuracy, robustness, computational cost, and diagnostic granularity.
Taha Mohamed Abdelatif Maaradji, Saïd Alem, Emanuele Gravante et al.· Transactions of the Institut...· 0 citations
In this paper, a fault diagnosis method based on temperature rise detection is proposed for power converters in switched reluctance motor drive systems used in electrical transportation equipment. First, the total power losses of all power devices are calculated and recorded under different operating conditions in both healthy and faulty states. A finite-element electrothermal model is then established to characterize the relationship between fault-induced power-loss redistribution and variations in the temperature rise of the converter devices. Based on the power-loss analysis, temperature rise is used as a key characteristic, and a corresponding fault diagnosis method is proposed. To account for the influence of operating conditions on the diagnostic criterion, three independent backpropagation neural network (BPNN) models are developed to predict fault-specific temperature-rise thresholds using rotor speed, load torque, and ambient temperature as inputs. During diagnosis, the real-time temperature evolution of the power diodes is compared with the selected thresholds to detect power converter faults. Finally, experimental results demonstrate the validity of the proposed fault diagnosis method.
Xiangsu Wang, Zhijie Zhang, Qing Wang et al.· Machines· 0 citations
Permanent magnet synchronous motors (PMSMs) are broadly used in diverse applications due to their inherent advantages. Open-circuit faults (OCFs) are among the major fault classifications in PMSMs, posing significant concerns due to their contribution to torque ripples, vibrations, and efficiency degradation. Therefore, accurate and real-time OCF diagnosis is essential for reliable operation and predictive maintenance practices. This underscores the importance of a robust diagnostic framework that enables early fault detection and localization, supports embedded integration, and requires no additional dedicated sensors. However, existing studies rarely address these requirements together. To overcome these limitations, this article proposes a novel OCF diagnostic framework that fuses features derived from multiple strategies, including wavelet energy-based features, frequency-domain features extracted from current waveforms, and speed measurement data. The extracted feature vector is used as input to a lightweight deep neural network. The proposed approach enhances interpretability and enables seamless embedded integration compared to conventional raw-data-driven machine learning models. In addition, an extended refinement layer is incorporated to enable integrated fault detection and classification for OCF while enhancing diagnostic transparency. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulations using the PLECS Blockset and further validated in real-time with an RTBox-based hardware-in-the-loop setup using a C2000 launchpad. Furthermore, experimental validation is conducted using a domain-adaptation strategy based on transfer learning. Performance evaluation confirms diagnostic accuracy exceeding 99% across varying operating conditions. The validation process achieves fault detection within 22% of a fundamental electrical cycle, with fault localization occurring within 40% of an average, demonstrating the robustness and adaptability of the proposed method. A sensitivity analysis of the proposed algorithm’s feature vector validates the effectiveness of high-frequency features. Furthermore, the risk distribution matrix provides insights supporting informed maintenance decisions.
Nimesh Jayasena, Battur Batkhishig, B. Nahid-Mobarakeh et al.· IEEE Open Journal of Industr...· 0 citations
Permanent magnet synchronous motors (PMSMs) are widely utilized in electric vehicle and industrial drive applications due to their exceptional efficiency and power density. Nevertheless, the irreversible demagnetization of permanent magnets poses a significant degradation mechanism that negatively impacts torque capability, efficiency, and long-term reliability. A physics-guided hybrid diagnostic architecture has been developed for assessing demagnetization in PMSM. This architecture integrates statistical process control (SPC), Isolation Forest for anomaly validation, and K-Nearest Neighbors (KNNs) for severity estimation, thereby forming a unified sequential monitoring framework. Instead of depending solely on an isolated algorithm, this proposed methodology establishes a structured, hierarchical diagnostic pipeline. This pipeline systematically combines initial statistical screening with subsequent unsupervised anomaly confirmation, ultimately leading to a quantitative degradation assessment, all designed for continuous, real-time monitoring of motor health. Integrated multi-sensor data, encompassing temperature, magnetic flux density, stator currents, and rotor speed, forms the basis for facilitating non-invasive, real-time health monitoring. The analytical process involves three distinct methods, which are executed in a strict sequential pipeline. Initially, SPC is applied to continuously monitor magnetic flux density using both Shewhart and Exponentially Weighted Moving Average control charts. This step identifies observations that exceed the three-sigma control limits, classifying them as statistically deviant. These flagged observations, along with all incoming multi-feature vectors, are subsequently transferred to the Isolation Forest algorithm. This algorithm assigns an anomaly score to each sample by measuring the mean path length necessary to isolate it within an ensemble of randomized trees. Samples with scores above a predefined contamination threshold are then designated as anomalies. Finally, only these labeled anomalous samples are directed to the KNN regression model. The KNN model retrieves the k most similar historical degradation records in the feature space, utilizing Euclidean distance, and then calculates the predicted demagnetization percentage as the average of their respective target values. Validation of the approach was performed on an experimental 1 kW PMSM setup exposed to varying thermal and electrical loading in order to simulate sensor-based degradation markers associated with the early stages of demagnetization-related degradation. The proposed framework was validated using experimentally acquired multi-sensor degradation indicators rather than direct measurements of irreversible permanent magnet remanence. The ability to consistently identify the degradation pattern and agree with the physics-guided demagnetization indicators computed using multi-sensor data is evident from this result. Severity estimation is based on experimentally obtained degradation indicators compared with other indicators, not on the actual magnet degradation levels themselves. The proposed framework provides a scalable, interpretable, and cost-effective solution for predictive maintenance and real-time fault diagnosis of PMSMs.
Jayant N Ramesh, Alex V, Sureshkumar P et al.· Engineering Research Express· 0 citations
Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission fault datasets are frequently imbalanced, and certain fault types produce electrical signatures that fall within the normal operating envelope, causing single-model classifiers to fail on safety-critical cases. This paper proposes a hybrid two-stage machine learning pipeline that decouples detection from classification. Stage 1 combines an Isolation Forest anomaly detector with an optional supervised binary detector through an OR-fusion rule; the supervised branch is allocated automatically during training for any fault class the anomaly detector cannot resolve, and is omitted when no such class exists. Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering is expressed as a per-measurement-point operator mapping six raw channels to eighteen features, including zero-sequence symmetrical components derived from Fortescue's theorem, yielding 18L features for L measurement points. On the TLFaultDataset, the pipeline raises Line-fault end-to-end accuracy from 31.3% to 95.8%. On an independent single-point dataset, the same framework attains 97.25% end-to-end accuracy across all classes including normal operation, exceeding the TLFed federated benchmark of 94.84% without GPU or federated infrastructure, at 0.05 ms per sample on CPU. Ablation on both datasets shows zero-sequence features resolving the three-phase versus three-phase-to-ground ambiguity, raising the F1-score of that class pair from 0.39 to 0.997. The direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.
Sahil Manikshete, A. Gujarathi, Thanh Long Vu et al.· 0 citations
The safety and reliability of inverters are crucial for the stable operation of the entire system, making accurate and effective OC (open-circuit) fault diagnosis methods particularly essential. This paper proposes an OC fault diagnosis strategy for three-level T-type inverters based on voltage vector trajectory centroid deviation characteristics. By analyzing the spatial distribution characteristics of the output voltage vector trajectory after an OC fault, it is found that the trajectory centroid undergoes varying degrees of displacement when a single-switch OC fault occurs. The magnitude and phase characteristics of the vector from the origin to the displaced centroid can be utilized to identify different single-switch OC faults. For double-switch combined faults, the centroid deviation vector can be equivalently characterized by the linear superposition of single-switch fault vectors. The centroid deviation vector features enable the identification of fault types where the vector is non-zero. Furthermore, the centroid vector characteristics of the first-quadrant trajectory can distinguish double-switch faults where the centroid vector is ac zero vector. The proposed method can diagnose single-switch faults and arbitrary double-switch combined faults in any phase. The diagnostic algorithm features low computational complexity and fast diagnosis speed, effectively addressing the issue of load sensitivity in conventional diagnosis methods.
Minxuan Liao, Rongkun Wang, Xinhua Guo et al.· IEEE Transactions on Circuit...· 0 citations