Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings

被引：32

作者：

Cheng, Jian ^{[1
]}

Yang, Yu ^{[1
]}

Shao, Haidong ^{[1
]}

Pan, Haiyang ^{[2
]}

Zheng, Jinde ^{[2
]}

Cheng, Junsheng ^{[1
]}

机构：

[1] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China

[2] Anhui Univ Technol, Sch Mech Engn, Maanshan 243032, Peoples R China

来源：

ISA TRANSACTIONS | 2022年 / 125卷

关键词：

Enhancedperiodicmodedecomposition; Ramanujansum; SOSO-MAIHND; Rollingbearing; Compositefaultdiagnosis;

D O I：

10.1016/j.isatra.2021.07.014

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The impulse components of different periods in the composite fault signal of rolling bearing are extracted difficultly due to the background noise and the coupling of composite faults, which greatly affects the accuracy of composite fault diagnosis. To accurately extract the periodic impulse components from the composite fault signals, we introduce the theory of Ramanujan sum to generate the precise periodic components (PPCs). In order to comprehensively extract major periods in composite fault signals, the SOSO-maximum autocorrelation impulse harmonic to noise deconvolution (SOSO-MAIHND) method is proposed to reduce noise and enhance the relatively weak periodic impulses. Based on this, an enhanced periodic mode decomposition (EPMD) method is proposed. The experimental results indicate that the EPMD is an effective method for composite fault diagnosis of rolling bearings. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：474 / 491

页数：18

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