Bearing performance degradation assessment using long short-term memory recurrent network

Abstract

Bearing is commonly used in rotating machinery, and it is significant to monitor bearing running states to ensure machine safety. Performance degradation assessment is an important work in bearing condition-based maintenance (CBM) and predictive maintenance. In this paper, a data-driven bearing performance degradation assessment method based on long short-term memory (LSTM) recurrent neural network (RNN) is proposed to comprehensively utilize the fault propagation information. Firstly, universal degradation simulation model based on vibration response mechanism is constructed for feature verification. A new proposed indicator "waveform entropy (WFE)" is developed and validated by this simulation model. Then waveform entropy and other conventional indicators are input into the LSTM RNN to identify the bearing running state, while particle swarm optimization method is applied to optimize the network structure parameters simultaneously. Experimental results demonstrate that LSTM RNN can effectively identify the bearing degradation states and accurately predict the remaining useful life.