New fusion features convolutional neural network with high generalization ability on rolling bearing fault diagnosis

• Published in: Concurrency and computation Vol. 35; no. 13
• Main Authors: Hao, Run Fang, Liu, Chuang, Cheng, Yong Qiang
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 10.06.2023; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial neural networks; Classification; Deep learning; domain adaptation; Fault diagnosis; Feature extraction; feature fusion; Machine learning; Neural networks; one‐dimensional convolutional neural network; Roller bearings; rolling bearing; Time dependence
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.7600

Summary This study proposes a feature fusion one‐dimensional convolutional neural network algorithm model with a portable non‐dimensionality reduction attention mechanism to resolve the limitations pertaining to the small training sample capacity of conventional deep learning models, single feature‐extractor size, insufficient feature extraction of the bearing faults, and low recognition rate of the bearing health status under variable loads and strong noise. This model regarded raw vibration signal as input, used the feature fusion module to extract multiscale time information, and the convolution‐pooling alternating layer adaptively overcome the time‐dependent limitations. An adaptive batch normalization was introduced to reduce the divergence in sample distribution between the source and target domains and enhance the model generalization capabilities. Moreover, the non‐dimensionality reduction attention mechanism is a more efficient channel weight distribution algorithm that can enhance the anti‐noise performance of the model. After embedding the non‐dimensionality reduction attention mechanism, the proposed network could enhance the anti‐noise performance of the model through a special channel‐weight learning method. It combined the Softmax classification layer to construct a feature extraction feature classification dual intelligent fault diagnosis algorithm. The experiments were performed on the rolling bearing fault dataset. The results revealed that the method provided a rather high generalization ability and stable anti‐noise ability. Simultaneously, the introduction of t‐SNE dimensionality reduction visualization revealed the strong feature‐extraction ability of the deep network model for large‐volume samples.