A Real-Time Bearing Fault Diagnosis Model Based on Siamese Convolutional Autoencoder in Industrial Internet of Things

The extreme environment refers to the abnormal temperature, pressure or vibration in the environment within a certain period of time, which will cause the fault of bearing equipment. Bearing fault diagnosis model can accurately identify the health status of bearing equipment, which can deal with the influence of extreme environments on the normal operation of bearings in a timely manner. However, current bearing fault diagnosis models have the following challenge: the sample size of faulty data is too small, which makes the parameters in the bearing fault diagnosis model unable to be effectively learned. Therefore, in order to solve the above issue in the field of bearing fault diagnosis, we draw on the siamese network and convolutional autoencoder, and propose a real-time bearing fault diagnosis model based on siamese convolutional autoencoder (RBFDSCA) in this work. Firstly, we use an Industrial Internet of Things (IIoT) platform to collect, store and analyze bearing data. Secondly, to cope with the challenge of the small sample size of faulty data, RBFDSCA model constructs a siamese convolutional autoencoder. The siamese convolutional autoencoder contains a positive feature extraction network, a negative feature extraction network, and a prediction network. The four evaluation metrics of RBFDSCA model on the real bearing dataset are 0.9638, 0.9640, 0.9641 and 0.9639 respectively, which verifies its excellent performance.