Aug 2026· Electronics· Vol 15, pp. 3424· 0 citations· 36 references
TL;DR
Current and voltage measurements proved to be crucial for identifying and localizing specific faults, while current and voltage provided valuable insights into motor behavior, such as changes in control strategy, even when they are not directly correlated with fault occurrence.
Abstract
The growing need to assess the health of components has made fault detection an increasingly important research topic in recent years. While traditional techniques remain widely used, the expansion of artificial intelligence (AI) has introduced innovative approaches. Among these, autoencoders have demonstrated significant potential for detecting anomalies in electric motors. The aim of this paper is to identify which signals are most suitable for AI-based fault detection in permanent magnet synchronous motors (PMSMs). To achieve this, in addition to analyzing acceleration signals, which are commonly studied in the literature, this work broadens the investigation by including current, voltage, and temperature signals acquired from different positions. The proposed method is tested during endurance tests, where motors operate under highly variable and demanding operating conditions. The collected signals are then analyzed using a 1D convolutional neural network autoencoder (1D CNN AE) to detect possible faults. The results highlight the importance of considering not only acceleration but also alternative monitoring signals. In particular, temperature measurements proved to be crucial for identifying and localizing specific faults, while current and voltage provided valuable insights into motor behavior, such as changes in control strategy, even when they are not directly correlated with fault occurrence.
Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.
The diagnosis of faults in power transformers (PT) plays a crucial role in ensuring the reliability and stability of power systems. However, traditional fault detection techniques are prone to low detection accuracy and are not stable enough under complex operating conditions. To address these challenges, this paper proposes a novel hybrid Variational Mode Decomposition (VMD), Graph Attention Network (GAN) with Adaptive Extreme Learning Machine (AELM) method for a transformer fault diagnosis framework. VMD can efficiently extract discriminative frequency bands of the transformer signals, GAN can dynamically learn the importance and relationship of the extracted feature, and AELM can classify rapidly and accurately with low complexity. The dataset was obtained from simulations of various fault conditions in the Matlab/Simulink. The experimental results demonstrate that the proposed method has an accuracy of 99.5%, a precision of 99.66%, a recall of 99.33%, and an F1-score of 99.5% compared to existing methods. The proposed hybrid framework enables efficient classification capability, feature learning with attention, and adaptive feature extraction, which contributes to improved performance.
Accurate defect detection of traction motors is essential for preserving the performance and safety of electric cars and industrial gear. This research presents a onedimensional convolutional neural network (1D-CNN) architecture for the automated identification of faults using vibration and current information obtained from a 150 kW traction motor operating under varying load and speed circumstances. The proposed technique concurrently analyses time-series vibration and current data, allowing the model to detect both mechanical and electrical irregularities. The dataset includes several defect kinds and healthy operating settings, offering a realistic basis for training and assessment. Experimental findings indicate that the 1D-CNN model attains a classification accuracy of 98.7% with just vibration signals, 97.5% with only current inputs, and 99.4% when both modalities are integrated. The precision, recall and F1-score of the integrated signal model are above 99 percent in all types of faults, which shows good performance even in varying operations. The findings underscore the efficacy of multi-signal 1D-CNN architectures for the prompt and precise detection of traction motor faults, reducing dependence on human feature extraction and facilitating predictive maintenance tactics. The proposed method provides a scalable and generalizable solution for practical traction systems, enhancing operating dependability and decreasing maintenance expenses.
M. Indhumathi, R. Deepa, M. Rubinabegam et al.· International Conference on...· 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
The effective and dependable functioning of high-speed permanent-magnet brushless DC motors used in aerospace and industry relies on motor fault classification and optimisation of efficiency. Accurate problem detection and diagnosis are critical for preserving system stability and performance, while attaining entirely fault-free devices is impossible according to dependability theory. This research proposes a state-of-the-art hybrid framework for motor fault classification that makes use of mutual information from current signals to effectively extract features. The representation is built on top of statistical characteristics, and to uncover hidden patterns in the data, deep features are retrieved using an adaptively trained DNN employing t-SNE visualisation. Afterwards, the Extreme Gradient Boosting (XGBoost) technique is used to integrate and classify these features. Particle Swarm Optimisation (PSO) is then used to automatically tweak the model parameters and improve performance. The results show that the suggested PSO-XGB-DNN model improves diagnostic accuracy by surpassing traditional methods, with a high classification accuracy of 97.15 percent. Finally, motor fault categorisation is made much more efficient, reliable, and operationally efficient by combining statistical and deep learning algorithms. This also improves predictive maintenance capabilities.
B. M. Reddy, G. Meghana, R. N. Sri et al.· 2026 7th International Confe...· 0 citations
A novel perspective on noninvasive diagnostics by integrating advanced signal processing with deep learning classifiers is offered, indicating that appropriate signal preprocessing enhances feature representation quality, indicating that the choice of transform method directly impacts diagnostic accuracy.
Konrad Górny, Wojciech Pietrowski· Compel· 0 citations
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