一种改进HVD信号特征提取方法及应用研究  

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

作　　者：时培明[1] 范雅斐 韩东颖[2] SHI Pei-ming;FAN Ya-fei;HAN Dong-ying(Key Laboratory of Measurement Technology and Instrument of Hebei Province,Yanshan University,Qinhuangdao,Hebei 066004,China;School of Vehicles and Energy,Yanshan University,Qinhuangdao,Hebei 066004,China)

机构地区：[1]燕山大学河北省测试计量技术及仪器重点实验室,河北秦皇岛066004 [2]燕山大学车辆与能源学院,河北秦皇岛066004

出　　处：《计量学报》2022年第7期920-926,共7页Acta Metrologica Sinica

基　　金：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)。

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。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.

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 

分 类 号：TB936[一般工业技术—计量学] TB973[机械工程—测试计量技术及仪器]