Weak characteristic information extraction from early fault of wind turbine generator gearbox

• Published in: Frontiers of Mechanical Engineering Vol. 12; no. 3; pp. 357 - 366
• Main Authors: XU, Xiaoli, LIU, Xiuli
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.09.2017
• Subjects: Condition Monitoring and Diagnosis; early fault warning; Engineering; local mean decomposition; Mechanical Engineering; Research Article; weak characteristic information extraction; Wind Turbine Dynamic Modeling; wind turbine generator gearbox; µ-singular value decomposition; wind turbine generator gearbox; singular value decomposition; weak characteristic information extraction; early fault warning; local mean decomposition
• ISSN: 2095-0233; 2095-0241
• DOI: 10.1007/s11465-017-0423-4

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on µ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and µ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.