Feature Extraction of Bearing Fault Based on Improved Switching Kalman Filter

doi:10.3901/JME.2019.07.044

Abstract

Abstract: A new method of fault feature extraction for rolling bearings based on improved switching Kalman filter is proposed. Compared with the traditional Kalman filter algorithm, this method only needs the current measurement value and the optimal estimation of the previous moment in each iteration, so it has high computational efficiency and strong real-time performance. Firstly, the vibration signals of fault bearings are divided into two parts:Fault impulse vibration and normal vibration. Secondly, the Kalman filter model based on the dynamic impulse response of the bearing mass-spring-damper system and the linear Kalman filter model are established respectively for the fault impulse vibration and the normal vibration. Then, the state estimation of vibration signals is carried out by using the switching Kalman filter algorithm based on Bayesian estimation. Finally, the bearing fault feature extraction is realized by filtering noise and identifying fault impulse components through time domain iteration filtering. The simulation and experimental results show the feasibility and effectiveness of the proposed method.

Key words: dynamic model, feature extraction, rolling bearing, switching Kalman filter

CLC Number:

TH17

CUI Lingli, WANG Xin, WANG Huaqing, XU Yonggang, ZHANG Jianyu. Feature Extraction of Bearing Fault Based on Improved Switching Kalman Filter[J]. Journal of Mechanical Engineering, 2019, 55(7): 44-51.

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