Jul 2026· Signal, Image and Video Processing· Vol 20· 0 citations· 40 references
Computer Science
TL;DR
A rolling bearing fault diagnosis method combining the Gramian Angular Field with a dual-channel Convolutional Neural Network (CNN) and Least Squares Support Vector Machine (LSSVM) that demonstrated superior performance compared to several other fault diagnosis methods when dealing with limited training samples and noisy interference.
Experiments indicate that the proposed fault diagnosis approach that combines Continuous Wavelet Transform, Convolutional Neural Network, CNN, Black-winged Kite Algorithm, and Least Squares Support Vector Machine outperforms CNN, CNN-SVM, and CNN-BiGRU under small-sample conditions.
Shi-Yan Sun, Yujun Shi, Quan Li et al.· Italian National Conference...· 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...
Taoyang Zhan, Kang Han, Yuhan Huang et al.· Insight - Non-Destructive Te...· 0 citations
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (DSC) and a convolutional neural network-Transformer hybrid model (CNN-Transformer) is proposed to address the non-stationarity of fault signals and the difficulty of jointly capturing local and global features. First, continu...
Shuai Yang, Yan-Chao Chen, Yang Yu· Engineering Research Express· 0 citations
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 curren...
Y. Çekiç, Aydin Akan· Signal Processing and Commun...· 0 citations
Experimental results demonstrate that the proposed method effectively improves the separability and recognition accuracy of multi-fault features, exhibits strong robustness and generalization capability under complex operating conditions, and provides an effective solution for intelligent bearing fault diagnosis.
Lihai Chen, Zhenshui Li, Ao Tan et al.· Machines· 0 citations
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