Bearing Fault Diagnosis Based on Improved Stacked Recurrent Neural Network

Abstract:

A bearing fault diagnosis model based on improved stacked recurrent neural network was proposed, which takes advantage of great nonlinear fitting capability and the characteristics of propagation though time. Gated recurrent unit was used to deal with the vanishing gradient problem, which contributes to classify the bearing health condition. The data set from Bearing Data Center of Case Western Reserve University was used to carry out the bearing fault diagnosis test. Support vector machine, particle swarm optimization-support vector machine, back-propagation network, AlexNet, and recurrent neural network were tested as well for comparison. The results show that the proposed model has exceptional reliability and generalization.