RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON IMPROVED MW-KECA ALGORITHM

Liu Xuan; Huo Zhongtang; Li Bing

doi:10.19912/j.0254-0096.tynxb.2024-1641

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 766-773. DOI: 10.19912/j.0254-0096.tynxb.2024-1641

RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON IMPROVED MW-KECA ALGORITHM

Liu Xuan¹, Huo Zhongtang¹, Li Bing²

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Abstract

A Moving Window-Kernel Entropy Component Analysis （MW-KECA） approach was used to monitor the vibration data related to the units during the gearbox degradation process and diagnose gearbox faults in response to the frequent failures in gearboxes during the production process of wind power generation.This was done while considering the characteristics of industrial processes, such as multiple variables, high dimensionality, non-linearity, high data correlation, and slow deterioration of abnormal data.This method improves sample overfitting.Additionally,the Whale Optimization Algorithm （WOA） was introduced to address the issues of stochasticity and randomness in kernel parameter selection inherent in the algorithm.Simulation studies were conducted using gearbox datasets, and accurate identification and verification of different fault types were achieved in fault diagnosis on a rolling bearing test rig.Comparative analyses reveals that the adoption of MW-KECA combined with WOA results in more efficient and accurate fault time prediction, with a reduced false alarm rate.

Key words

wind turbines / gearbox / fault diagnosis / sliding window / principal component analysis / predictive analysis

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Liu Xuan, Huo Zhongtang, Li Bing. RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON IMPROVED MW-KECA ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 766-773 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1641

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