Adaptive parameter-matching method of SR algorithm for fault diagnosis of wind turbine bearing

被引：14

作者：

Gu, Xiaojiao ^{[1
]}

Chen, Changzheng ^{[1
,2
]}

机构：

[1] Shenyang Univ Technol, Sch Mech Engn, Shenyang 110870, Liaoning, Peoples R China

[2] Liaoning Engn Ctr Vibrat & Noise Control, Shenyang 110870, Liaoning, Peoples R China

来源：

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2019年 / 33卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Adaptive stochastic resonance; Fault diagnosis; Frequency information exchange; Wind turbine bearing; STOCHASTIC RESONANCE; MODE DECOMPOSITION; GEARBOX;

D O I：

10.1007/s12206-019-0202-8

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The fault diagnosis of wind turbine bearings is challenging because of the heavy background noise and changes in wind speed. Stochastic resonance (SR) is an effective method of detecting fault signal from noise. However, the benefit of SR is seriously limited by the system parameters and the frequency of the input signal. A novel fault diagnosis method for wind turbine bearings, combining an adaptive SR algorithm that is based on quantum particle swarm optimization (QPSO) and frequency conversion based on frequency information exchange (FIE), is proposed. First, the frequency information of the fault characteristic signal is exchanged with the reference frequency by FIE, which can eliminate the limitation of the frequency band. Then, the SR system parameters are optimized by QPSO to avoid blind parameter selection. The signal after FIE is processed by the optimized SR system. The results of case study show that under the same input signal, the proposed method can achieve better signal-to-noise ratio and response amplitude than can the traditional double-side band modulation method and an SR method that is combined only with an optimization algorithm.

引用

页码：1007 / 1018

页数：12

共 28 条

[21] Time delay and non-Gaussian noise-induced stochastic stability and stochastic resonance for a metapopulation system subjected to a multiplicative periodic signal [J].

Wang, Kang-Kang ;

Ye, Hui ;

Wang, Ya-Jun ;

Wang, Ping-Xin .

MODERN PHYSICS LETTERS B, 2018, 32 (27)

[22]

Xie YouHao Xie YouHao, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P70

[23] The analysis of stochastic resonance of periodic signal with large parameters [J].

Yang Ding-Xin ;

Hu Zheng ;

Yang Yong-Min .

ACTA PHYSICA SINICA, 2012, 61 (08)

[24] A quantum particle swarm optimization [J].

Yang, SY ;

Wang, M ;

Jiao, LC .

CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2004, :320-324

[25] Fault diagnosis of planetary gearbox with incomplete information using assignment reduction and flexible naive Bayesian classifier [J].

Yu, Jun ;

Bai, Mingyou ;

Wang, Guannan ;

Shi, Xianjiang .

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2018, 32 (01) :37-47

[26] Realizing the empirical mode decomposition by the adaptive stochastic resonance in a new periodical model and its application in bearing fault diagnosis [J].

Zhang, Jingling ;

Huang, Dawen ;

Yang, Jianhua ;

Liu, Houguang ;

Liu, Xiaole .

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2017, 31 (10) :4599-4610

[27] Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator [J].

Zhao, Hongshan ;

Li, Lang .

IET RENEWABLE POWER GENERATION, 2017, 11 (04) :453-460

[28]

Zou HL, 2013, 2013 6TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), VOLS 1-3, P1090, DOI 10.1109/CISP.2013.6745218

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