PSO-VMD-MCKD Based Fault Diagnosis for Incipient Damage in Wind Turbine Rolling Bearing

被引：0

作者：

Zhang J. ^{[1
]}

Zhong M. ^{[1
]}

Zheng J. ^{[2
]}

Li X. ^{[1
]}

机构：

[1] School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou

[2] School of Mechanical Engineering, Anhui University of Technology, Maanshan

来源：

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2020年 / 40卷 / 02期

关键词：

Fault diagnosis; Maximum correlated kurtosis deconvolution; Particle swarm optimization; Rolling bearing; Variational mode decomposition;

D O I：

10.16450/j.cnki.issn.1004-6801.2020.02.011

中图分类号：

学科分类号：

摘要：

The incipient damageof wind turbine rolling bearingsis very difficult to be detected, because the fault signalsare nonlinear, nonstationary, and likely to be buried by strong background noise. In light of this problem, a comprehensive methodology that combines variational modal decomposition (VMD) and maximum correlated kurtosis deconvolution (MCKD) is presented. The parameters of VMD and MCKD are selected automatically by the particle swarm optimization algorithm (PSO). First, the optimal α and K in VMD are calculated by PSO, and the most sensitive modal is selected according to the VMD decomposition of incipient fault signals. Then, theoptimal L and T in MCKD algorithm are calculated by PSO so as to boost the fault shock in the modal. Finally, the incipient fault feature is extractedfrom the envelope demodulation of the faults. Simulation results as well as experimental tests both validate that the proposed method can adaptively enhance the weak fault component of rolling bearing, thus can effectively extract incipient fault features of rolling bearings from strong background noise. © 2020, Editorial Department of JVMD. All right reserved.

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页码：287 / 296

页数：9

共 20 条

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