Fault diagnosis of motor bearing based on deep learning

Abstract

The effective fault diagnosis of the motor bearings not only can ensure the smooth and efficient operation of equipment but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this article constructs a stacked auto-encoder network. The input data are compressed and reduced by introducing sparsity constraint, so that the network can accurately extract the fault characteristics of the input data, and the fault recognition ability of the network can be improved by introducing random noise. The simulation result shows that the stacked auto-encoder network can not only overcome the shortcomings of traditional fault diagnosis method that requires to distinguish fault samples manually and needs a large number of prior knowledge but also realize the self-learning of fault signal feature. The accuracy rate of fault identification reaches 98%, 94%, 96%, and 95.5% in four different working conditions. What’s more, the network can exhibit strong robustness under different working conditions. Finally, the new research ideas of fault diagnosis in thermal power plant are put forward by copying the idea of fault diagnosis of motor bearing.