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《机械工程前沿(英文)》 >> 2017年 第12卷 第3期 doi: 10.1007/s11465-017-0423-4

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

Key Laboratory of Modern Measurement & Control Technology (Ministry of Education), Beijing Information Science and Technology University, Beijing 100192, China

录用日期: 2017-04-06 发布日期: 2017-08-04

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摘要

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.

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