Fault diagnosis of wind bearing based on multi-scale wavelet kernel extreme learning machine

被引：0

作者：

Zhu, Siwen ^{[1
]}

Jiao, Bin ^{[1
]}

机构：

[1] Shanghai Dian Ji Univ, Dept Elect Engn, Elect Coll, 300 Blooms Rd, Shanghai, Peoples R China

来源：

2ND ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2017) | 2017年 / 887卷

关键词：

D O I：

10.1088/1742-6596/887/1/012070

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The principle of kernel Extreme Learning Machine (ELM) is demonstrated. On this basis, a multi - scale wavelet kernel extreme learning machine is proposed. The multi-scale wavelet kernel is used as the kernel function of the extreme learning machine. The test shows that it is an achievable extreme learning machine. Experiments show that, using the multi-scale wavelet kernel extreme learning machine in the wind turbine bearing fault diagnosis has higher classification accuracy and speed than the support vector machine classification algorithm, and has excellent application value.

引用

页数：7

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