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.
Rolling bearing failures remain a primary cause of induction motor breakdowns, creating significant reliability and maintenance challenges. This study aims to enhance fault diagnosis robustness under variable-speed conditions by addressing the limitations of fixed-speed datasets and single-model deep learning approaches.
An ensemble deep learning model (EDLM) is developed by integrating convolutional neural networks (CNN), deep belief networks (DBN) and stacked autoencoders (SAE). The framework applies a weighted-fusion strategy to exploit the complementary strengths of the base learners. Diagnostic performance is evaluated using three input modalities: raw vibration signals, spectrograms and infrared thermal images.
The EDLM consistently outperformed individual models across all metrics. Among the modalities, infrared thermal images achieved the highest diagnostic accuracy (98%), demonstrating superior capability in capturing subtle fault features under variable-speed conditions.
To the best of current knowledge, this study presents the first ensemble of CNN, DBN and SAE for bearing fault diagnosis under variable-speed conditions. By validating multi-sensor inputs and highlighting the diagnostic advantage of infrared thermography, the work 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
In response to the low accuracy in fault diagnosis caused by data distribution deviations due to variable operating conditions of mechanical equipment, this paper constructs a multi-scale linear attention multi-source subdomain adaptation network (MLAMSAN) that integrates the multi-scale linear attention (MLA). It can achieve fault diagnosis under cross operating conditions through subdomain feature alignments. This method uses the local maximum mean difference that can align subdomains as the metric function, integrates convolutional neural networks and MLA with lower computational complexities as the feature extractor, and constructs the MLAMSAN model for cross condition fault diagnosis of rotor-bearing systems. By comparing different convolutional layer effects, training batch sizes, and learning rates on the network model, parameters that optimize the model performance are selected. Through experimental verification on both the self-constructed dataset and the publicly available dataset, the MLAMSAN model can realize the diagnosis of single and compound faults of rotor-bearing systems under cross-working conditions, and 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
As a key component in mechanical equipment, the operating status of rolling bearings directly affects the stability and performance of the entire system. Existing mainstream fault diagnosis methods using vibration signals are primarily based on ‘black box’ models. Although
they are characterised by high recognition accuracy, the decision-making basis and interpretability are insufficient, making it challenging to meet the needs of high-reliability application scenarios. Therefore, an interpretable Morlet convolutional neural network (MorletConv CNN) model based
on Morlet convolution is proposed in this paper to reveal the inherent mechanism by which the model identifies fault features. It is difficult to extract features adaptively with traditional signal processing methods. Furthermore, the decision logic of convolutional neural networks (CNNs)
is opaque. As a result, a Morlet preprocessing layer, embedded into the CNN structure, is designed in this paper. Combined with prior knowledge of time-frequency analysis, the proposed layer enables the model to learn key structural features in vibration signals more effectively, improving
the accuracy and reliability of fault recognition. Specifically, the proposed model utilises two convolutional kernel branches, one for the real part and one for the imaginary part, to enhance sensitivity to minor signal changes and improve its ability to detect early faults. Simultaneously,
the control parameter θ of the Morlet kernel function is set as a trainable variable to reduce model complexity and accelerate training. This function automatically aligns key frequency components during the optimisation process, improving the time-frequency feature extraction ability
of the model. 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