Jul 2026· International Conference on Robotics and Sensor Networks· Vol 14254, pp. 1425402 - 1425402-7· 0 citations· 9 references
Engineering
Abstract
Inter-turn short-circuit faults are a common failure mode in permanent magnet synchronous motors, and their early diagnosis is essential for ensuring safe and reliable operation. In practical applications, however, training data are often contaminated with noisy labels, while incipient fault features are weak and exhibit limited inter-class separability, which further aggravates label misassignment and mixing. To enhance data usability and label reliability, this paper proposes a diagnostic framework integrating high-frequency signal injection with unsupervised learning. High-frequency signal injection is used to strengthen fault-related current features, followed by autoencoder-based feature extraction, UMAP dimensionality reduction, and HDBSCAN clustering to group samples, after which a majority voting strategy is applied for sample relabeling. Experimental results demonstrate that the proposed method can effectively improve noisy-label correction performance even under high noise ratios and low fault severity, thereby providing reliable data for subsequent fault diagnosis.
Squirrel Cage Induction Generators are widely used in renewable energy systems, but their reliability is threatened by stator short-circuit faults. Traditional steady-state diagnostic methods are often rendered ineffective in modern applications because closed-loop controllers actively mask fault signatures. To address this challenge, this work introduces an event-driven fault classification framework specifically designed to detect and classify masked stator faults, namely inter-turn and inter-winding short-circuits. Instead of computationally expensive continuous signal analysis, the proposed methodology isolates brief, high-frequency transient disturbances using an adaptive, derivative-enhanced event detection mechanism. Discriminative features are extracted from these localized transient intervals, with a focus on derivative-based voltage representations. Finally, a novel confidence-weighted aggregation strategy is introduced to combine event-level predictions into robust file-level decisions. Experimental validation using a high-resolution dataset demonstrates that the proposed framework significantly improves diagnostic accuracy. The results show that derivative-based voltage features provide superior class separability, achieving a file-level Area Under the Curve of approximately 0.89, demonstrating the effectiveness of the targeted transient evaluation strategy in closed-loop systems.
H. T. Canseven, Evin Şahin Sadık· European Conference on Artif...· 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
Inter-turn short circuits (ITSCs) in induction motor (IM) windings are among the most critical and frequent faults in industrial environments, as they can rapidly evolve into severe damage, leading to unplanned downtime and costly maintenance. To enhance the reliability of IMs, this paper proposes a machine learning–based diagnosis method dedicated to ITSC failures. The developed diagnostic tool combines a support vector machine (SVM) classifier with Fisher’s ratio (FR)-based feature selection. The proposed framework uses experimentally acquired current signals under healthy conditions and five ITSC fault severity levels (1%–5%), evaluated across four load conditions (25%, 50%, 75%, and 100%). Each signal is segmented into 200 non-overlapping segments (500 samples each), from which nine time-domain features are extracted to capture fault-related characteristics. These features are then used for training and testing a SVM classifier capable of distinguishing between healthy states and levels of severity of ITSC faults. To optimize the classification process, the Fisher’s ratio (FR) algorithm is employed to select the most informative features while discarding those with low relevance. Our findings unveiled that the proposed hybrid FR-SVM-based diagnosis achieves high diagnostic accuracy ranging from 99.54% to 100%. Furthermore, the outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
H. Zaimen, T. Thelaidjia, Makhlouf Chouki et al.· International Journal of Ele...· 0 citations
In renewable-powered distribution systems and microgrids, reliable cable condition monitoring is essential for operational security and early fault detection. Sheath circulating current signals provide valuable information for identifying grounding abnormalities and incipient faults, but their diagnosis is difficult because the signals exhibit strong inter-phase coupling, and fault samples are limited in practice. This study proposes a dual-view Mixup-ResNet framework for fault diagnosis of cable sheath circulating current signals. Specifically, physical-range normalization is employed to retain magnitude-related fault information and inter-phase proportional relationships, while sample-wise z-score normalization is used to emphasize waveform morphology. These two complementary views are concatenated to form a six-channel input for a lightweight one-dimensional residual network, which is trained with Mixup, label smoothing, dropout, and cosine annealing. On an ATP-EMTP-generated eight-class dataset, the proposed method achieves an average accuracy of 91.50%, a weighted F1-score of 91.69%, and a macro F1-score of 91.69% under a unified 5 × 5 repeated stratified cross-validation protocol. Additional tests under 12% relative Gaussian noise show that the method maintains competitive Gaussian-noise tolerance within the tested condition, although broader field disturbances remain to be further validated. These findings suggest that the proposed method has potential for small-sample fault diagnosis of cable sheath circulating current signals and provides a preliminary basis for intelligent cable condition monitoring.
Haiqi Yang, Jinwei Mao, Bo Zhang et al.· Energies· 0 citations
Rotor inter-turn short-circuit (ITSC) faults in synchronous generators introduce electromagnetic asymmetries that can lead to torque ripple, unbalanced magnetic pull, and progressive mechanical degradation. While most existing studies focus on binary classification and severe fault conditions, the assessment of incipient rotor ITSC severity using displacement-sensitive vibration measurements remains relatively underexplored. This paper proposes a vibration-based diagnostic framework for multi-class severity classification of rotor ITSC by integrating an eddy-current displacement sensor with physically motivated feature extraction. An 18-dimensional hybrid feature set is designed to characterize electromechanical modulations induced by rotor electromagnetic asymmetry. Using an XGBoost classifier with leave-one-out cross-validation, the proposed approach achieved 90.56% overall accuracy, including 99% recall for healthy operation and 87% recall for mild fault conditions. The results suggest that displacement-sensitive vibration analysis enables effective severity-aware diagnosis of rotor ITSC with minimal sensor requirements.
O. Rozhon, Serge Pacome Bosson, Š. Janouš et al.· 0 citations
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.
Masadi Prashanth Kumar, Srikanth Velpula, Chidurala Saiprakash· Transactions of the Institut...· 0 citations