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
A hybrid deep learning-based model that combines convolutional neural networks and long short-term memory with explainable artificial intelligence to detect and classify faults accurately and interpretably to intelligent fault management in a contemporary smart grid is suggested.
Udit Mamodiya, Divyanshu Sinha, I. Kishor et al.· Scientific Reports· 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
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).
Jize Zhang, Prasun Sanki, V. Vignesh et al.· Frontiers in Artificial Inte...· 0 citations