Jul 2026· Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics· 1 citation· 12 references
TL;DR
An advanced diagnostic pipeline is proposed that transforms one-dimensional time-series current and voltage signals into informative two-dimensional spatial representations using Gramian angular field encoding and Coati optimization algorithm-optimized transfer learning framework provides an accurate, interpretable, and computationally feasible solution for induction motor fault diagnosis in electric vehicle applications.
Abstract
The aim of this study is to develop and evaluate a robust fault diagnosis framework for induction motors used in electric vehicle propulsion systems. Since induction motor faults may compromise operational safety, energy efficiency, and system reliability, an accurate and interpretable diagnostic approach is required for safety-critical electric vehicle applications.
This study proposes an advanced diagnostic pipeline that transforms one-dimensional time-series current and voltage signals into informative two-dimensional spatial representations using Gramian angular field encoding. This transformation preserves temporal correlations and enables effective deep feature extraction. Five pretrained convolutional neural network architectures, namely the visual geometry group 19-layer network, the 101-layer residual network, the 169-layer densely connected convolutional network, the extreme inception network, and the efficient network B5 variant, are comparatively evaluated through a transfer learning strategy. To enhance classification robustness, the nature-inspired Coati optimization algorithm is integrated to optimize deep feature selection and weighting.
The proposed framework was tested on a multi-fault induction motor dataset including normal operation, overload fault, overvoltage fault, phase-to-ground fault, phase-to-phase fault, and undervoltage fault conditions. The experimental results show that the DenseNet169–Coati optimization algorithm model achieves the best performance, with an accuracy of 97.22%, a precision of 97.62%, and a macro area under the curve of 98.80%. Furthermore, gradient-weighted class activation mapping analysis confirms that the model focuses on physically meaningful signal regions.
The proposed Gramian angular field-encoded and Coati optimization algorithm-optimized transfer learning framework provides an accurate, interpretable, and computationally feasible solution for induction motor fault diagnosis in electric vehicle applications.
To maintain the safety and reliability of industrial applications, it is important to analyse faults in three-phase induction motors. Traditional methods have difficulty in identifying the complex characteristics of errors in three-phase induction motors because the signals associated with three-phase induction motors are often noisy and exhibit non-linear dynamics. In this paper, a comprehensive method to analyse errors in three-phase induction motors are proposed that utilises advanced signal preprocessing, feature extraction, and Deep Learning (DL) methods to address these issues. Initially, an Entropy-Driven Empirical Wavelet Filter (EDEWF) are applied to clean the raw motor signal, and a Min-Max scaling is used to normalise the signal to create useful features. Next, an integrated hybrid model combining a Radial Basis Function Neural Network (RBFNN) with a Multi-Head Adaptive Transformer (MHAT) are used for capturing local non-linear characteristics and global context. The features extracted from both models are combined into one feature vector and passed through a fully connected layer for classification. The Starfish Optimization Algorithm (SFOA) is implemented to adjust the hyperparameter of the proposed integrated hybrid model to improve the overall performance. The results obtained from the proposed method showed that it achieved a greatest accuracy, precision, recall, F1-Score of 0.9960 and robustness in the diagnostic of three-phase induction motors compared to traditional diagnostic methods, with respect to classifying the motor states as normal, healthy, and Broken Rotor Bar (BRB) faulted.
Killamsetti Vijeta, Perumal B Sri Suyambulinga, R. T. Kumar et al.· ITEGAM- Journal of Engineeri...· 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
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
These findings confirm that careful data engineering is as important as model complexity and is key to achieving efficient ITSC fault diagnosis and are confirmed 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 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 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
