Research on Abnormal Feature Extraction and Early Fault Alarm Method of Rolling Bearing's Based on CDAE and KLD

Abstract

A rolling bearing is an important part of rotating machinery, and it is widely used in the petrochemical industry, aerospace industry and other industries. Hence, it is of great significance to carry out condition monitoring and fault alarms for rolling bearings. Aiming at the problem of the rolling bearing fault, a method of an improved deep convolutional denoising auto encoder abnormal feature extraction and the Kullback-Leibler divergence threshold alarm is proposed. The experiment verification is carried out on the rotor bearing experiment platform. The experiment results show that the proposed method has good denoising performance and micro fault feature extraction ability under the condition of no fault data training and no frequency domain transformation. High accuracy, good efficiency and strong robustness of the proposed method for an early fault alarm are demonstrated by the experiment as well.