期刊
NEUROCOMPUTING
卷 157, 期 -, 页码 208-222出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2015.01.016
关键词
Vibration signal; Dimension reduction; Fault diagnosis; Supervised manifold learning; Least square support vector machine; Particle swarm optimization
资金
- National Science Foundation of China [51275546, 51375514]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130191130001]
- Fundamental Research Funds for the State Key Laboratory of Mechanical Transmission in Chongqing University [SKLMT-ZZKT-2012 MS 09]
In order to improve the accuracy of fault diagnosis, this article proposes a multi-fault diagnosis method for rotating machinery based on orthogonal supervised linear local tangent space alignment (OSLLTSA) and least square support vector machine (LS-SVM). First, the collected vibration signals are decomposed by empirical model decomposition (EMD), and a high-dimensional feature set is constructed by extracting statistical features, autoregressive (AR) coefficients and instantaneous amplitude Shannon entropy from those intrinsic model functions (IMFs) that contain most fault information. Then, the high-dimensional feature set is inputted into OSLLTSA for dimension reduction to yield more sensitive low-dimensional fault features, which not only achieves the combination of intrinsic structure information and class label information of dataset but also improves the discrimination of the low-dimensional fault features. Further, the low-dimensional fault features are inputted to LS-SVM to recognize machinery faults according to the LS-SVM parameters selected by enhanced particle swarm optimization (EPSO). Finally, the performance of the proposed method is verified by a fault diagnosis case in a rolling element bearing, and the results confirm the improved accuracy of fault diagnosis. (C) 2015 Elsevier B.V. All rights reserved.
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