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.
[1]
Lili Ding,et al.
Gear Fault Diagnosis Using Dual Channel Data Fusion and EEMD Method
,
2017
.
[2]
Kun Yu,et al.
A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering
,
2017
.
[3]
Long Zhang,et al.
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
,
2010,
Expert Syst. Appl..
[4]
Jian Ma,et al.
Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine
,
2015
.
[5]
Ai Yanting,et al.
Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance
,
2017
.
[6]
Diego Cabrera,et al.
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
,
2016
.