139 / 2025-05-27 17:46:12

Fault Pattern Recognition for Bearings via Convolutional Sparse Representation of Multi-source Heterogeneous Information

Bearing Fault Diagnosis,Deep Learning,Convolutional Sparse Representation,Multi-source Information Fusion,Pattern Recognition

Special Sessions > SSL 01 Advanced signal processing enabled intelligent fault diagnosis and measurement

Bearing fault diagnosis is crucial for ensuring the operational reliability and safety of rotating machinery. However, traditional diagnostic methods face significant challenges: strong background noise often obscures weak fault signals, and reliance on single-sensor information provides an incomplete picture of complex fault states. These limitations can lead to decreased diagnostic accuracy and reliability. To address these issues,  this study proposes a novel convolutional sparse bearing fault pattern recognition method based on multi-source heterogeneous information from vibration and sound signals. Firstly, embedding a convolutional sparse representation layer within the deep learning framework, which adaptively extracts key quasi-periodic fault impulses from raw multi-source signals and suppresses noise and irrelevant background features through an end-to-end learned convolutional dictionary and sparse constraints; secondly, designing a novel fusion convolutional layer that integrates standard convolution and dilated convolution to capture local fine structures and broader contextual and multi-scale information in parallel, thereby achieving deep and effective fusion of purified feature streams from different sensors.  Finally, a classification stage utilizes these comprehensively fused features to perform the bearing fault diagnosis. Experimental results on multi-source heterogeneous datasets from two bearings types demonstrate that the proposed M-DSRN method achieves outstanding fault recognition performance, compared with traditional rivals including deep neural networks and Sparse Representation Classification. This fully validates its effectiveness and advancement in achieving high-precision fault diagnosis under conditions involving strong background noise and multiple fault types through enhanced feature extraction and multi-source information fusion.