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

Abstract

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.

Keywords

wind turbine generator gearbox / µ-singular value decomposition / local mean decomposition / weak characteristic information extraction / early fault warning

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Acknowledgments

This research was sponsored by the National Natural Science Foundation of China (Grant Nos. 51275052 and 51105041), and the Key Project Supported by Beijing Natural Science Foundation (Grant No. 3131002).

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