RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Abstract

Remaining useful life (RUL) prediction of rolling bearing plays an important role in maintaining the safety of the equipment. However, the data collected from industrial scene often contains noises, which affects the RUL prediction precision of rolling bearing. To overcome the above problem, a data-driven scheme for RUL prediction of rolling bearing is proposed based on convolutional denoising autoencoder (CDAE) and bidirectional long short-term memory network (Bi-LSTM). In the proposed method, the vibration signal is directly used as input of the prognostics network model. Then, a denoising network model based on CDAE is built to reduce the effect of noise. Through stacking the convolutional autoencoder, the noise component is automatically removed from the raw data. Finally, the network model based on Bi-LSTM is established to extract the high-dimensional degradation characteristics of bearing and estimate the RUL of the rolling bearing. The experimental results on the Xi’an Jiaotong University bearing dataset show that the proposed method has satisfied performance of RUL prediction.