Fault diagnosis method of deep sparse least squares support vector machine

被引：0
|
作者：
Zhang R. [1 ,2 ]
Li K. [1 ,2 ]
Su L. [1 ,2 ]
Li W.-R. [3 ]
机构：
[1] Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, Wuxi
[2] School of Mechanical Engineering, Jiangnan University, Wuxi
[3] School of Mechanical Engineering, Donghua University, Shanghai
来源：
Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2019年 / 32卷 / 06期
关键词：
Fault diagnosis; Multi-layer structure; Rolling bearing; Sparsification; SVM;
D O I：
10.16385/j.cnki.issn.1004-4523.2019.06.020
中图分类号：
学科分类号：
摘要：
A mechanical fault diagnosis method combining multi-layer structure and sparse least squares support vector machine is presented. This method constructs a multi-layer support vector machine structure. Firstly, the fault signal is trained and the shallow features are learned by support vector machine at the input layer. And then a new representation of samples is generated through the "dimension reduction formula" and used as the input for the hidden layers. The hidden layer support vector machine trains new samples and extracts the deep features of the signal layer by layer. Finally the diagnostic results are output at the last layer. Considering the increase of the running time and algorithm complexity caused by the multi-layer, this paper combines the sparsification theory with least squares support vector machine technology. The approximate linear independent vector set as sparse representation of the samples in feature space is searched and thus the discriminative function can be constructed. Lack of sparseness is effectively solved and the validity of this method is verified by the fault diagnosis experiments of the rolling bearing. © 2019, Nanjing Univ. of Aeronautics an Astronautics. All right reserved.
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页码：1104 / 1113
页数：9
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