Chunming Wu1, Shupeng Zheng2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665
Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >
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