FAULT DIAGNOSIS METHOD OF WIND TURBINE GEARBOX BASED ON S-SLLE

Wang Xiang; Wang Jinping; Xu Wanjun

doi:10.19912/j.0254-0096.tynxb.2020-0601

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (3) : 343-349. DOI: 10.19912/j.0254-0096.tynxb.2020-0601

FAULT DIAGNOSIS METHOD OF WIND TURBINE GEARBOX BASED ON S-SLLE

Wang Xiang, Wang Jinping, Xu Wanjun

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Abstract

Because of the complicated structure of wind turbine gearbox, it is easy to be shut down due to the influence of alternating load and harsh working environment. In order to improve the recognition rate of fault diagnosis model, the feature dimension reduction method of the statistical supervised locally linear embedding manifold learning（S-SLLE） based on K-means classification theory was proposed. Firstly, the time-frequency domain fault features of gearbox vibration signals are extracted, and the redundancy feature vector are taken out, so the complexity and calculation amount of the diagnosis model are reduced,then the diagnosis model based on the RBF kernel support vector machine classifier is used to establish to diagnose and identify the feature vector extracted by S-SLLE. Finally, the Machinery Fault Simulator was used to simulate multiple vibration fault experiments on the gearbox. Through the analysis and processing of the experimental fault signals, the results verify that the proposed S-SLLE RBF-SVM diagnosis model can identify the wind turbine gearbox fault effectively and accurately.

Key words

wind turbines / feature extraction / support vector machines / manifold learning / gearbox vibration fault

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Wang Xiang, Wang Jinping, Xu Wanjun. FAULT DIAGNOSIS METHOD OF WIND TURBINE GEARBOX BASED ON S-SLLE[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 343-349 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0601

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