Machine learning based fault diagnosis method for asynchronous motors
Aiming at the problem of poor diagnostic effect due to insufficient feature extraction capability in previous asynchronous motor fault diagnosis,a machine learning-based asynchronous motor fault diagnosis method is proposed,which uses a multi-scale convolutional neural network(MSCNN)-bidirectional long and short-term memory network(BiLSTM)asynchronous motor fault diagnosis model of the attention mechanism(AM).We improve the learning mechanism by adding channel attention mechanism,and use three different scales to extract data features,use BiLSTM to extract temporal features of periodic fault vibration signals,add the self-attention mechanism to focus on the key fault features,introduce the residual module to reduce the influence of noise and redundant data,and finally,output the diagnostic results through Softmax classification.The experimental results show that the model can effectively extract the fault features in the dataset,and compared with the other four common models,reflecting its stability and high diagnostic performance,and the accuracy rate for asynchronous motor fault diagnosis reaches 98.5%.
asynchronous motorfault diagnosisconvolutional neural networkbidirectional long and short-term memory networkattention mechanism
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