Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

5th Asia Conference on Mechanical and Materials Engineering

机械制造;材料科学

Jiao, Jing^1 ; Yue, Jianhai^1 ; Pei, Di^1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijng, China^1

关键词: Bearing fault diagnosis;    Decision level fusion;    Dempster-Shafer evidence theory;    Electric multiple unit;    Feature level fusion;    Least Square Support Vector Machine (LS-SVM);    Multi-sensor information fusion;    Vibration fault diagnosis;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/241/1/012034/pdf
DOI  :  10.1088/1757-899X/241/1/012034

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.