一种改进HVD信号特征提取方法及应用研究

doi:10.3969/j.issn.1000-1158.2022.07.13

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摘要希尔伯特振动分解(HVD)广泛应用于风电机组、齿轮箱等旋转机械的故障诊断,然而,它有2个亟待解决的问题:一是算法的参数需要经验设置或人工试定;二是如何避免模态混叠选择敏感的本征模态函数分量。针对上述2个问题,提出一种优化的HVD改进算法,有效解决了希尔伯特振动分解的参数设置和模态混叠问题。首先用粒子群优化算法(PSO)对HVD算法的2个参数进行优化。其次,提出了一种新的评估指标—最大包络峰度均值作为PSO优化算法的目标函数,并提出采用最大包络峰度自适应地选择敏感的IMF分量。最后,对选定的重构信号进行平方包络谱分析并提取故障特征频率,以识别风电机组设备故障类型。通过模拟信号、实验信号和风电机组应用实例分析,验证了所提改进HVD方法的有效性。

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	时培明
	范雅斐
	韩东颖

关键词 ：计量学, 改进HVD;旋转机械故障, 故障诊断, 风电机组, 粒子群优化, 包络峰度均值

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

收稿日期: 2021-07-19 发布日期: 2022-07-18

PACS:	TB936
	TB973

基金资助:国家自然科学基金(61973262);中央引导地方科技发展资金(216Z4301G);河北省自然科学基金(E2020203147);河北省引进留学人员资助项目(C20190371)

通讯作者: 时培明 E-mail: spm@ysu.edu.cn

作者简介: 时培明(1979-),男,黑龙江延寿人,燕山大学教授,博士,主要从事智能信息处理和设备监测诊断方面的研究。Email:spm@ysu.edu.cn

引用本文:

时培明,范雅斐,韩东颖. 一种改进HVD信号特征提取方法及应用研究[J]. 计量学报, 2022, 43(7): 920-926.
SHI Pei-ming,FAN Ya-fei,HAN Dong-ying. Study on an Improved HVD Signal Feature Extraction Method and Its Application. Acta Metrologica Sinica, 2022, 43(7): 920-926.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.07.13 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I7/920

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