Study on an Improved HVD Signal Feature Extraction Method and Its Application

SHI Pei-ming; FAN Ya-fei; HAN Dong-ying

doi:10.3969/j.issn.1000-1158.2022.07.13

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Acta Metrologica Sinica ›› 2022, Vol. 43 ›› Issue (7) : 920-926. DOI: 10.3969/j.issn.1000-1158.2022.07.13

Study on an Improved HVD Signal Feature Extraction Method and Its Application

SHI Pei-ming¹,FAN Ya-fei¹,HAN Dong-ying²

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Abstract

Hilbert vibration decomposition (HVD) is widely used in fault diagnosis of rotating machinery such as fans and gearboxes.However, it has two problems to be solved: (1) the parameters of the algorithm need to be set empirically or determined manually; (2) how to avoid modal mixing by selecting sensitive intrinsic mode function (IMF) components.In view of the above two problems, an improved HVD algorithm is proposed, which effectively solves the problem of parameter setting and model aliasing of Hilbert vibration decomposition.The proposed method is described as follows: firstly, two parameters of HVD algorithm are optimized by particle swarm optimization (PSO).In addition, a new evaluation metric-Max envelope kurtosis mean (MEKM) is proposed as the objective function of the PSO optimization algorithm, and Max envelope kurtosis (MEK) is introduced to adaptively select the sensitive IMF component. Finally, the selected reconstructed signals are analyzed by square envelope spectrum and fault characteristic frequencies are extracted to identify wind turbine equipment fault types.The effectiveness of the proposed improved HVD method is verified by a simulated signal, experimental signal and wind turbine applied example analysis.

Key words

metrology / improved HVD / rotating machinery fault / fault diagnosis / wind turbine / particle swarm optimization / envelope kurtosis mean

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SHI Pei-ming,FAN Ya-fei,HAN Dong-ying. Study on an Improved HVD Signal Feature Extraction Method and Its Application[J]. Acta Metrologica Sinica. 2022, 43(7): 920-926 https://doi.org/10.3969/j.issn.1000-1158.2022.07.13

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