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.
Abstract
This study aims to enhance the effectiveness and reliability of stator winding fault detection in squirrel-cage induction machines. It specifically investigates the influence of signal preprocessing methods, including the Fast Fourier Transform and the Wavelet Transform, on the extraction of fault-related information and the subsequent performance of Deep Neural Networks (DNNs) in classifying inter-turn faults.
The study uses a quantitative mixed-method approach, combining field-circuit modeling with experimental validation. A field-circuit model was developed to simulate both healthy and faulty motor conditions and generate training data. Diagnostic features derived from measured phase current waveforms were processed and used as inputs to a deep convolutional neural network. The framework was rigorously tested using laboratory experiments to verify the classification of inter-turn short-circuits.
The results reveal that appropriate signal preprocessing enhances feature representation quality, indicating that the choice of transform method directly impacts diagnostic accuracy. These findings provide evidence that the developed framework achieves higher precision, sensitivity and robustness, confirming its capability to detect early-stage multi-phase inter-turn short-circuits effectively.
This research offers a novel perspective on noninvasive diagnostics by integrating advanced signal processing with deep learning classifiers. The study provides valuable insights into optimizing input data for DNN fault detection, contributing to the advancement of reliable condition monitoring systems for industrial induction motors.
Inter-turn short-circuit (ITSC) faults are among the most frequent stator winding faults in induction motors, often leading to irreversible damage. In this context, this work proposes a Machine Learning (ML) based framework for classifying ITSC faults using experimental stator current data comprising 13 categories. The framework employs Direct-Quadrature (dq) transformation, signal windowing, and feature extraction for data processing, followed by the training of multiple ML classifiers, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forests, XGBoost, and LightGBM. For benchmarking, a Convolutional Neural Network (CNN) was also trained directly on raw signals. The hyperparameters of all the models were tuned using Particle Swarm Optimization (PSO), Optuna, and random search. The results show that the proposed framework achieves high classification performance, with XGBoost tuned using random search reaching up to 99.60% accuracy across 13 classes. The CNN, relying on end-to-end learning, achieved lower performance compared to the developed ML classifiers, highlighting the importance of data representation for accurate fault classification under limited data. For hyperparameter tuning, random search achieved performance comparable to complex methods with a lower processing burden, making it a viable option for hyperparameter optimization. These findings confirm that careful data engineering is as important as model complexity and is key to achieving efficient ITSC fault diagnosis.
Omar Abdelaziz Bengharbi, Karim Beddek, Ahmed Yacine Lacheheb et al.· Measurement and control (Lon...· 0 citations
Motor Current Signature Analysis (MCSA) is a non-invasive technique that enables the detection of bearing faults in rotating electrical machines without the need for additional sensors. In this study, the Paderborn University bearing dataset was utilized to perform a two-stage analysis. In the first stage, motor current data were processed directly using 1-Dimensional Convolutional Neural Networks (1D-CNN). In the second stage, scalogram images obtained via Continuous Wavelet Transform (CWT) were used to train five different deep learning models, with the ResNet18-based 2D-CNN model providing the best performance. To prevent data leakage, the training and testing sets were partitioned based on individual bearings to ensure complete isolation. The experimental results demonstrated that both 1D-CNN and ResNet18-based 2D-CNN methods achieved 100% accuracy in detecting outer race faults. However, it was observed that the impact of inner race faults on the stator current remains weak due to the complex physical transmission path of the fault signal, resulting in significantly lower detection rates.
Y. Çekiç, Aydin Akan· Signal Processing and Commun...· 0 citations
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.
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
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