Fault Diagnosis of Main Reducer Based on Hybrid Kernel Learning Support Vector Machine

被引：0

作者：

Zhang, Huawei ^{[1
]}

Di, Aihua ^{[1
]}

Zuo, Xuyan ^{[1
]}

Wang, Fei ^{[1
]}

机构：

[1] Wuhan Univ Technol, Coll Comp Sci & Technol, Wuhan, Hubei, Peoples R China

来源：

2017 IEEE 9TH INTERNATIONAL CONFERENCE ON COMMUNICATION SOFTWARE AND NETWORKS (ICCSN) | 2017年

关键词：

noise pollution; EMD; wavelet threshold; FEATURE-EXTRACTION; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In complex conditions, the vibration signal of mechanical equipment is inevitably affected by noise pollution, and the extraction of information from a valid signal mixed with noise vibration signal is the key point of influence subsequent fault diagnosis accuracy. By using the empirical mode decomposition (EMD) function and wavelet threshold to achieve the vibration signal de-noising, and the signal noise after pretreatment reduces interference for subsequent diagnosis.

引用

页码：1549 / 1553

页数：5

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