WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON ENHANCED MORPHOLOGICAL FILTERING AND THIRD-ORDER CUMULANT DIAGONAL SLICE SPECTRUM

Luo Yuanqing; Chen Changzheng; Zhao Siyu

doi:10.19912/j.0254-0096.tynxb.2020-0577

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (3) : 373-381. DOI: 10.19912/j.0254-0096.tynxb.2020-0577

WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON ENHANCED MORPHOLOGICAL FILTERING AND THIRD-ORDER CUMULANT DIAGONAL SLICE SPECTRUM

Luo Yuanqing¹, Chen Changzheng^1,2, Zhao Siyu¹

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Abstract

To solve the problem that the fault signal of the wind turbine rolling bearing is difficult to be identified by the strong background noise, a fault detection method based on the enhanced morphological filtering and the third-order cumulant diagonal slice spectrum is proposed. Firstly, this method constructs a new enhanced morphology operator （EMDO） based on the basic morphology operators. Then, the feature energy factor is used to select the optimal structural element scale of the EMDO operator. Finally, the third-order cumulant diagonal slice spectrum de-noising performance is used to further enhance the feature extraction ability of the EMDO operator. The results of simulation and comparison experiments show that the method proposed in this paper can effectively eliminate the interference caused by Gaussian white noise and enhance the extraction of fault feature information of the wind turbine rolling bearing.

Key words

wind turbines / morphological filtering / third-order cumulant diagonal slice spectrum / rolling bearing / fault diagnosis

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Luo Yuanqing, Chen Changzheng, Zhao Siyu. WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON ENHANCED MORPHOLOGICAL FILTERING AND THIRD-ORDER CUMULANT DIAGONAL SLICE SPECTRUM[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 373-381 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0577

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