Accurate fault detection in induction motors (IMs) under varying load conditions remains a critical challenge in industrial condition monitoring (CM). Inspired by the foundational work, which highlighted the impact of mechanical load on fault signature detectability. This study proposes a multi-modal signal analysis approach to bearing fault diagnosis using stator current, rotor speed, and flux-induced voltage signals. A custom fifteen-class dataset was collected, comprising healthy and faulty motor states at 0%, 50%, and 100% load levels. Unlike conventional approaches that rely on extensive preprocessing and handcrafted feature extraction, the proposed framework operates directly on raw signals, enabling a lightweight, computationally efficient, and easily deployable solution. This design significantly reduces implementation complexity while maintaining high diagnostic performance, making it suitable for real-time and industrial applications. Two types of models were evaluated in this study: traditional machine learning models and deep learning models. Experimental results demonstrate significant performance gains compared to single-sensor models, highlighting the benefits of cross-domain signal fusion. Models specifically designed to process time-series data, such as the Temporal Convolutional Network (TCN) and particularly the Long Short-Term Memory (LSTM), exhibit outstanding performance. During the training and validation phases, the LSTM model achieved perfect classification accuracy (100%), outperforming all other evaluated models. However, during the deployment-oriented evaluation on unseen test data, the SVM and TCN models demonstrated the most consistent generalization performance, achieving perfect prediction results across all tested samples. Recent architectures, such as the Transformer, also demonstrate strong potential; with careful hyperparameter tuning, their performance especially in terms of generalization can be further enhanced.
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
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.
R. Sooraj, S. Ramu, R. Sitharthan et al.· Scientific Reports· 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 diagnosis of faults in power transformers (PT) plays a crucial role in ensuring the reliability and stability of power systems. However, traditional fault detection techniques are prone to low detection accuracy and are not stable enough under complex operating conditions. To address these challenges, this paper proposes a novel hybrid Variational Mode Decomposition (VMD), Graph Attention Network (GAN) with Adaptive Extreme Learning Machine (AELM) method for a transformer fault diagnosis framework. VMD can efficiently extract discriminative frequency bands of the transformer signals, GAN can dynamically learn the importance and relationship of the extracted feature, and AELM can classify rapidly and accurately with low complexity. The dataset was obtained from simulations of various fault conditions in the Matlab/Simulink. The experimental results demonstrate that the proposed method has an accuracy of 99.5%, a precision of 99.66%, a recall of 99.33%, and an F1-score of 99.5% compared to existing methods. The proposed hybrid framework enables efficient classification capability, feature learning with attention, and adaptive feature extraction, which contributes to improved performance.
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
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