FAULT DIAGNOSIS METHOD OF WIDN TURBINE GEARBOX BASED ON MULTI-CORE PARELLEL RFECV-GNB

Wang Jinhua; Yuan Shanqin; Cao Jie

doi:10.19912/j.0254-0096.tynxb.2023-2150

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 550-558. DOI: 10.19912/j.0254-0096.tynxb.2023-2150

FAULT DIAGNOSIS METHOD OF WIDN TURBINE GEARBOX BASED ON MULTI-CORE PARELLEL RFECV-GNB

Wang Jinhua¹, Yuan Shanqin¹, Cao Jie²

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Abstract

This paper addresses the critical challenges of poor robustness in noisy environments and low diagnostic accuracy when labelled samples are scarce in deep learning-based wind turbine gearbox fault diagnosis. This paper proposes a novel method that integrates RFECV and GNB for wind turbine gearbox fault diagnosis. This method integrates the capability of RFECV to effectively extract essential fault signatures from limited fault data with the high computational efficiency of GNB for wind turbine gearbox fault diagnosis. To mitigate the prolonged training time of RFECV, a CPU parallelized task “packaging” algorithm is introduced, enhancing modeling performance via the over-subscription of LCPUs and efficient load distribution among LCPUs. Extensive experiments on multiple fault datasets demonstrate that the proposed method outperforms traditional approaches with significantly superior diagnostic accuracy and modelling speed under the same number of faulty samples.

Key words

wind turbines / gearbox / fault diagnosis / Bayesian theorem / feature selection / CPU parallelism

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Wang Jinhua, Yuan Shanqin, Cao Jie. FAULT DIAGNOSIS METHOD OF WIDN TURBINE GEARBOX BASED ON MULTI-CORE PARELLEL RFECV-GNB[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 550-558 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2150

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