A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

被引:36
|
作者
Jiang, Fan [1 ,2 ]
Zhu, Zhencai [1 ,2 ]
Li, Wei [1 ,2 ]
Ren, Yong [1 ,2 ]
Zhou, Gongbo [1 ,2 ]
Chang, Yonggen [1 ]
机构
[1] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Jiangsu, Peoples R China
[2] China Univ Min & Technol, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou 221116, Jiangsu, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2018年 / 8卷 / 09期
基金
中国国家自然科学基金;
关键词
feature fusion; sensitive IMFs selection; fault diagnosis; EEMD; SVM; EMPIRICAL MODE DECOMPOSITION; EMD METHOD; IDENTIFICATION; TRANSFORM; CLASSIFICATION; ALGORITHM; SPECTRUM; SYSTEM;
D O I
10.3390/app8091621
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.
引用
收藏
页数:18
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