Rolling bearing fault diagnosis under small sample conditions based on WDCNN-BiLSTM Siamese network

Rolling bearing fault diagnosis under small sample conditions based on WDCNN-BiLSTM Siamese network

Abstract Rolling bearings are a crucial component in rotating machinery, essential for ensuring the smooth functioning of the entire system. However, their vulnerability to damage necessitates the implementation of effective fault diagnosis. Traditional deep learning methods often struggle due to th...

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Bibliographic Details
Main Authors: Chenxu Bian, Chunni Jia, Jibo Li, Xiangjun Chen, Pei Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Rolling bearing
Small sample
Fault diagnosis
Siamese neural network
Deep learning
Online Access:https://doi.org/10.1038/s41598-025-12370-3
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Summary:Abstract Rolling bearings are a crucial component in rotating machinery, essential for ensuring the smooth functioning of the entire system. However, their vulnerability to damage necessitates the implementation of effective fault diagnosis. Traditional deep learning methods often struggle due to the scarcity of fault samples, leading to issues like overfitting and inadequate generalization. To address this problem, a novel Siamese Neural Network (SNN) model, integrating Deep Convolutional Neural Networks with Wide First-layer Kernel (WDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. This model constructs a feature extraction system that combines WDCNN and BiLSTM to extract local spatial features and global temporal dependencies from vibration signals. Additionally, the SNN framework is introduced to build a feature space under small sample conditions through metric learning, enhancing the ability of model to discern sample similarities. Experiments on the CWRU and HUST datasets indicate that with only 90 training samples, the model achieves diagnostic accuracy of 83.47% and 61.48%, respectively, significantly surpassing CNN, BiLSTM, and their combined models. Furthermore, the model also shows robustness against severe noise interference, making it a viable tool for efficient fault diagnosis in rolling bearings with limited data.
ISSN:2045-2322

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