Bearing Fault Diagnosis Based on Optimal Time-Frequency Representation Method

被引：19

作者：

Ruiz Quinde, Israel ^{[1
]}

Chuya Sumba, Jorge ^{[1
]}

Escajeda Ochoa, Luis ^{[1
]}

Antonio, Jr. ^{[1
]}

Guevara, Vallejo ^{[1
]}

Morales-Menendez, Ruben ^{[1
]}

机构：

[1] Tecnol Monterrey, Monterrey 64489, NL, Mexico

来源：

IFAC PAPERSONLINE | 2019年 / 52卷 / 11期

关键词：

Wigner-Ville Distribution; Fault Diagnosis; Bearing Spindles; DECOMPOSITION;

D O I：

10.1016/j.ifacol.2019.09.140

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wigner-Ville Distribution (WVD) is probably the most used non-linear time-frequency distribution for signal processing in fault diagnosis, due to the advantages of excellent resolution and localization in time-frequency domain. However, the presence of cross terms when they are applied to multicomponent signals can give misleading interpretations. A methodology based on Local Mean Decomposition (LMD) and WVD is proposed to get more reliable bearing fault diagnosis based on vibration signals. Kullback-Leibler Divergence (KLD) guides the selection of the optimal frequency band with the most relevant information about the fault. Early results based on experimental data show successful diagnosis. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：194 / 199

页数：6

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