Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 11 references
Abstract
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.
This study presents the first ensemble of CNN, DBN and SAE for bearing fault diagnosis under variable-speed conditions, and provides a reliable and scalable solution for real-world machinery health monitoring.
P. Ong, Wen-Jiun Yap, W. Lee et al.· Journal of Quality in Mainte...· 0 citations
Electrical current signals provide essential information about the health and performance of electrical systems. Identifying distorted signals is critical for the early detection of faults in electrical systems, and in turn, helps prevent damage, instability, and loss of efficiency. This paper presents a study on classifying healthy and faulty sine-wave signals using convolutional neural networks. A dataset of 200 images was constructed, to provide diverse waveform variations, and used to train and test a customized Convolutional Neural Networks (CNN) in addition to three pretrained CNNs: SqueezeNet, GoogLeNet, and ResNet-50. Each pretrained network was fine-tuned through transfer learning, and data augmentation was applied to improve generalization. Experimental results show that ResNet-50 achieved the highest validation accuracy of 98.33%, while SqueezeNet and GoogLeNet reached 96.67%. Testing on unseen current signal images confirmed that deeper models were more effective in detecting small waveform distortions. The results demonstrate the suitability of CNN-based approaches for waveform classification and highlight the importance of model depth and dataset variation. This study contributes to the field of predictive maintenance by providing an exploration of a simple, cost-effective, and accurate method for fault detection in single phase induction motors. It opens the door for further research into machine learning applications in fault diagnosis in other types of motors and electrical systems.
M. Shatnawi, Mariam Alsaqqaf, Salihah Almenhali et al.· 2026 6th International Confe...· 0 citations
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.
Kamal Hamani, M. Kuchař, Martin Sobek et al.· Scientific Reports· 0 citations
A multi-scale linear attention multi-source subdomain adaptation network (MLAMSAN) that integrates the multi-scale linear attention (MLA) that can achieve fault diagnosis under cross operating conditions through subdomain feature alignments that exhibits the superior diagnostic performance and the strong generalization ability.
Zheng Han, Yuqi Fan, Yaping Wang et al.· Engineering Research Express· 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
The validation results on multiple typical bearing fault datasets show that the proposed MorletConv CNN model is characterised by enhanced physical interpretability and generalisation ability while maintaining high diagnostic accuracy, providing new ideas and method support for achieving highly reliable rolling bearing fault diagnosis.
Taoyang Zhan, Kang Han, Yuhan Huang et al.· Insight - Non-Destructive Te...· 0 citations