Application of the state deterioration evolution based on bi-spectrum in wind turbine. (August 2014)

• Record Type: Journal Article
• Authors: Xiaoli, Xu; Zhanglei, Jiang; Hongjun, Wang; Guoxin, Wu; Yunbo, Zuo; Peng, Chen; Liyong, Wang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 11(2014:Nov.)
• Page Start: 1958
• Page End: 1967
• Publication Date: 2014-08
• Subjects: Non-stationary -- bi-spectrum -- feature extraction -- normal distribution -- state deterioration; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406213511964
• Languages: English
• ISSNs: 0954-4062

Abstract

Concerning the problem of large rotating machinery like wind turbine which runs in low speed and non-stationary state, this research mainly focuses on separating fault trend feature from non-fault feature and the method of state deterioration evolution based on bi-spectrum. Firstly, the experimental signal such as low-speed startup vibration signal of rotor test rig in the normal state and a plurality of unbalanced state have been collected. Bi-spectrum method is applied to extract fault feature which submerged in complex background noise. On the basis of bi-spectrum analysis, the fault feature evolutionary matrix is defined to represent the state of equipment deterioration. The eigenvalues of fault feature evolutionary matrix are computed and fitted to a normal distribution curve, from which the mean value and variance are taken as fault feature parameters. Fault feature parameters are verified effectively by experiments. Finally, depending on fault feature parameters, graphical representation of state deterioration evolution is established. It is beneficial to provide guidelines for equipment deterioration trend. This method is applied to analyze the real vibration signal of wind turbine with the type of WD646/600 KW, and actual equipment condition verified the effectiveness of the proposed method.