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.
Abstract
Induction Motors (IMs) are a very important group of electrical appliances used in industry, due to their strength, versatility, high reliability, and cost-effectiveness. However, due to various faults, there can be many issues, such as production downtime, energy losses, and increased maintenance costs. This paper employs Deep learning (DL) frameworks, such as Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for the detection and categorization of IM. The novelty of the proposed work lies in the unified multi-sensor, multi-class, and multi-model DL framework with comprehensive cross-architecture evaluation for robust IM fault diagnosis under eight different operating scenarios, including startup transients and phase-removal faults, load-based faulty and healthy conditions. The results show that the DL is effective in electrical fault identification, using the classification accuracies of RNN—85.14%, LSTM −90.19%, BiLSTM—92.24%, and GRU—94.19%. The proposed approach can be used to differentiate between healthy and faulty operating conditions, which aim to highlight the effectiveness of DL for intelligent condition monitoring in induction motor drive-based machines.
Industrial motor fault diagnosis is significantly affected by environmental noise and sensor degradation, which reduce the reliability of conventional deep learning models. This paper proposes a multi-sensor fault diagnosis framework based on Multi-Head Attention and LSTM networks enhanced with a Reliability-Gated fusion mechanism. The proposed framework dynamically evaluates the reliability of current, vibration, and stray-flux signals before feature fusion. Experimental results demonstrate superior diagnostic performance compared with conventional LSTM-Attention models, with an accuracy of 97.67% when the motor is operating under full load. Furthermore, under severe noise conditions (20 dB SNR), the proposed model maintains 91.5% accuracy. The reliability-gating strategy preserves robust diagnostic performance during total sensor failure, achieving an accuracy of 92.4%, while maintaining sensitivity to incipient winding faults with only 2% severity. The proposed framework provides a fault-tolerant solution for predictive maintenance in industrial environments.
Jaafar Falih Hasan, A. R. Hussein· European Conference on Artif...· 0 citations
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
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
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
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
Photovoltaic (PV) systems have proven themselves to be a viable alternative energy source; however, there are multiple faults related to PV systems which cause energy losses and low efficiencies. Manual or rule-based algorithms are traditionally used for fault diagnosis, which are not efficient and unsuitable for real-time applications. In this paper, a novel hybrid intelligent classification system for PV fault detection is proposed by integrating Fuzzy C-Means (FCM) clustering and Deep Learning (DL) techniques such as Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU). The dataset consists of 102,400 samples collected from a real-time 10 kW solar PV system operating under varying irradiance conditions ranging from 600 W/m2 to 1,000 W/m2 and temperature conditions ranging from 25 °C to 40 °C. The FCM technique is used to enhance the extracted features by clustering the membership functions, and the obtained features are used for model training. The performance of proposed models is evaluated using the classification metrics and confusion matrices. The proposed FCM + GRU model achieved 91.13% accuracy 0.79 precision, 0.76 recall, and F1-score of 0.78. The obtained results confirm the effectiveness of the proposed hybrid framework by improving fault classification performance under various operating environments.
Jize Zhang, Prasun Sanki, V. Vignesh et al.· Frontiers in Artificial Inte...· 0 citations