A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

doi:10.1007/s11633-021-1305-0

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines
	Qiang Li 1,3
刊名	International Journal of Automation and Computing
	2021
卷号	18 期号:6 页码:993-1006
关键词	Fault diagnosis structural element multi-scale mathematical morphology rolling bearing correlation analysis
ISSN号	1476-8186
DOI	10.1007/s11633-021-1305-0
英文摘要	Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed, which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a “W” structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect. Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference, and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46105]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Institute of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China 2.Faculty of Information, Beijing University of Technology, Beijing 100124, China 3.Inner Mongolia Key Laboratory of Electrical and Mechanical Control, Hohhot 010051, China
推荐引用方式 GB/T 7714	Qiang Li. A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines[J]. International Journal of Automation and Computing,2021,18(6):993-1006.
APA	Qiang Li.(2021).A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines.International Journal of Automation and Computing,18(6),993-1006.
MLA	Qiang Li."A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines".International Journal of Automation and Computing 18.6(2021):993-1006.