Abstract
Motor is widely used in industrial production, but the frequent motor bearing fault brings great safety hazard to the production. Traditional fault diagnosis methods often require prior signal processing knowledge and are inefficient. In order to solve this problem, the artificial intelligence fault diagnosis method has been applied in motor bearing fault diagnosis. With the help of the original motor running state signal collected by the sensors, non-invasive real-time detection of motor bearing fault can be realized. This paper presents an improved CNN-LSTM network based on hierarchical attention mechanism(CALSTM) for motor bearing fault diagnosis. In this artificial intelligence method, the fault characteristics of the original data can be learned by convolutional neural network, and then the importance of the features can be obtained by using hierarchical attention mechanism. Finally, the weighted results are sent to the LSTM network for time dimension selection. This method does not need signal processing and adaptively weights the features of each sample learned by the neural network, which enhances the explanatory ability of the learning process of the neural network. When carry out experiments on CWRU data set, and the experimental results indicate that, compared with several common models, CALSTM method has a better diagnosis effect, and the overall accuracy of the model reached 99.22%.
A Neural Network Based Motor Bearing Fault Diagnosis Algorithm and its Implementation on Programmable Logic Controller