KEYWORDS: Feature extraction, Data modeling, Diagnostics, Visual process modeling, Education and training, Convolution, Visualization, Resistance, Process modeling, Neural networks
For various bearing states, the traditonal model has insufficient ability to extract important information, low accuracy and poor generalization ability of the model in complex environment.Therefore, a bearing fault diagnosis method based on CNN-BiLSTM-Attention is proposed. This method combines convolutional neural network (CNN) and bidirectional long and short term memory network (BiLSTM), adds the Dropout mechanism to the BiLSTM network to suppress the overfitting problem, then introduces the attention mechanism to automatically assign different weights to the BiLSTM network to improve the sensitivity and the ability to grasp different fault information. In order to verify the effect of this model, the Case Western Reserve University(CWRU) bearing fault dataset was used for experimental verification, the results show that the proposed model has higher accuracy and stronger generalization ability than other models in the multitask problem under complex working conditions.
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