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
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
收藏
页码:1007 / 1018
页数:12
相关论文
共 28 条
[1]   Integration of uniform design and quantum-behaved particle swarm optimization to the robust design for a railway vehicle suspension system under different wheel conicities and wheel rolling radii [J].
Cheng, Yung-Chang ;
Lee, Cheng-Kang .
ACTA MECHANICA SINICA, 2017, 33 (05) :963-980
[2]   Experimental study of high frequency stochastic resonance in Chua circuits [J].
Gomes, I ;
Mirasso, CR ;
Toral, R ;
Calvo, O .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2003, 327 (1-2) :115-119
[3]   Enhancement of weak-signal response based on stochastic resonance in carbon nanotube field-effect transistors [J].
Hakamata, Yasufumi ;
Ohno, Yasuhide ;
Maehashi, Kenzo ;
Kasai, Seiya ;
Inoue, Koichi ;
Matsumoto, Kazuhiko .
JOURNAL OF APPLIED PHYSICS, 2010, 108 (10)
[4]   Fault diagnosis for the gearbox of wind turbine combining ensemble intrinsic time-scale decomposition with Wigner bi-spectrum entropy [J].
Hu, Aijun ;
Xiang, Ling ;
Gao, Nan .
JOURNAL OF VIBROENGINEERING, 2017, 19 (03) :1759-1770
[5]   The prediction and diagnosis of wind turbine faults [J].
Kusiak, Andrew ;
Li, Wenyan .
RENEWABLE ENERGY, 2011, 36 (01) :16-23
[6]   Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis [J].
Lai, Zhi-hui ;
Leng, Yong-gang .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 81 :60-74
[7]  
Li T, 2016, 2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), P1051, DOI 10.1109/CGNCC.2016.7828932
[8]   Adaptive stochastic resonance method based on quantum particle swarm optimization [J].
Li Yi-Bo ;
Zhang Bo-Lin ;
Liu Zi-Xin ;
Zhang Zhen-Yu .
ACTA PHYSICA SINICA, 2014, 63 (16)
[9]   A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM [J].
Li, Yongjian ;
Zhang, Weihua ;
Xiong, Qing ;
Luo, Dabing ;
Mei, Guiming ;
Zhang, Tao .
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2017, 31 (06) :2711-2722
[10]   An adaptive stochastic resonance method for weak fault characteristic extraction in planetary gearbox [J].
Li, Zhixing ;
Shi, Boqiang .
JOURNAL OF VIBROENGINEERING, 2017, 19 (03) :1782-1792