2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· 0 citations
Abstract
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.
The fault diagnostics in Brushless Direct Current (BLDC) motor drive system is critical for operational safety and system lifespan in propulsion system applications. However, signature parameters such as currents, voltages, speed, and torque have provided nonlinear behavior, which limits the usefulness of traditional model-based approaches. This research provides a deep learning based intelligent system to monitor the failures in marine propulsion system. Each signal feature is represented as a structured token, with a specific class token used to collect global contextual information. The proposed model captures both local temporal dynamics and global inter-feature interdependence multi-layer self-attention processes, allowing for the thorough modeling of complex fault patterns. The framework is tested on datasets including healthy conditions and fault conditions, such as motor faults and drive switch failures. The impact of signal-to-noise ratio in the different case structure on the various signals are investigated. With clean signal conditions (SNR = 100 dB), the proposed model achieves a motor fault classification accuracy of 89%, with a weighted precision of 0.91, recall of 0.89, and F1-score of 0.89 on the hardware testbed. Drive switch fault classification under clean simulation conditions achieves an accuracy of 97%, with a macro-averaged F1-score of 0.98 including perfect classification (F $1=1.0$ ) for four out of six switch fault types. Robustness evaluation under additive white Gaussian noise reveals motor fault accuracies of 89%, 80%, and 27% at SNR levels of 100 dB, 50 dB, and 10 dB respectively, and drive fault accuracies of 93%, 51%, and 56% at the corresponding SNR levels. Compared to CNN and LSTM based baselines, the proposed model improves diagnostic accuracy by (4–8%) with a significantly reduced false positive rate, confirming its suitability for intelligent real-time fault diagnosis in marine propulsion systems.
Pratik Anand Deshpande, J. Preetha Roselyn, P. Sundaravadivel· IEEE Access· 1 citation
Deep learning frameworks, such as Simple Recurrent Neural Network, Long Short-Term Memory, Long Short-Term Memory, Bidirectional LSTM, Bidirectional LSTM, and Gated Recurrent Unit, are employed for the detection and categorization of IM.
R. Sooraj, S. Ramu, R. Sitharthan et al.· Scientific Reports· 0 citations
To address the challenges of difficult fault feature extraction and feature aliasing in aero-engine inter-shaft bearings under strong noise conditions, this paper proposes a fusion diagnostic method that integrates a dual-scale one-dimensional convolutional neural network, a multi-head self-attention Transformer, and a bidirectional gated recurrent unit. The method employs a three-stage progressive network architecture for end-to-end fault diagnosis. The dual-scale 1DCNN extracts local temporal features from vibration signals, and batch normalization and dropout are applied to stabilize training and reduce potential overfitting. The Transformer encoder models dependencies among the extracted feature representations, supporting the representation of fault-sensitive features. The BiGRU captures bidirectional temporal dependencies in the fault evolution process. Experimental validation on the Harbin Institute of Technology aero-engine inter-shaft bearing dataset shows that the proposed model achieves 97% diagnostic accuracy under extreme noise conditions (SNR = -5 dB). Compared with existing methods, these results indicate that the proposed network effectively maintains diagnostic performance under controlled noise conditions.
Yang Wang, Boliang Zhang· Scientific Reports· 0 citations
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.
Yıldırım Özüpak, Emrah Aslan· Proceedings of the Instituti...· 0 citations
The stable detection of faults in smart power grids is essential when focusing on the stable operation and the reduction of the downtime. In this paper, the author suggests a hybrid deep learning-based model that combines convolutional neural networks (CNN) and long short-term memory (LSTM) with explainable artificial intelligence (XAI) to detect and classify faults accurately and interpretably. The model aims at capturing the spatial and time-varying attributes of multivariate electrical signatures such as voltage, current, and frequency changes. A combination of real time sensor measurements and simulated fault conditions are used which includes several fault classes including LG, LL, LLG, and three phase faults. Experimental evaluation demonstrates that the proposed model achieves a classification accuracy of 97.84%, with an F1-score of 97.08%, outperforming conventional CNN and LSTM models by more than 3%. The framework is also able to work in a noisy environment with a stable performance of less than 2% performance decrease, and inference latency of 18 ms, which can be deployed in real-time. Besides, the combination of SHAP and attention processes improves the interpretability through the detection of the crucial features that lead to fault prediction. The findings suggest that the suggested solution is a powerful, scalable, and clear solution when it comes to intelligent fault management in a contemporary smart grid.
Udit Mamodiya, Divyanshu Sinha, I. Kishor et al.· Scientific Reports· 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