1.石家庄铁道大学 省部共建交通工程结构力学行为与系统安全国家重点实验室，石家庄 050043;2.中车唐山机车车辆有限公司，唐山 063500
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1.State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;2.CRRC Tangshan Co Ltd, Tangshan 063500, China
最优小波解调是一种常用的滚动轴承故障诊断方法,针对如何选择最优中心频率和带宽的问题,从故障振动信号的冲击性和循环平稳性出发,提出了一种基于负熵和多目标优化的复Morlet小波解调方法.利用遗传算法的泛优化能力,分别以窄带信号包络的负熵和包络谱的负熵设计两个目标函数,通过非支配排序和拥挤距离排序,结合选择、交叉和变异遗传操作对复Morlet小波参数进行优化,自适应地确定富含故障信息的最优共振频带进行包络解调.试验表明,该方法通过多目标优化可以统一表征轴承故障的冲击性和循环平稳性,可以准确识别轮对轴承的局部故障.
Optimal wavelet filter is one of the most useful methods in fault diagnosis of rolling element bearings. Aiming at finding out an optimal couple of center frequency and bandwidth of the wavelet filter, and considering the impulsiveness and cyclostationarity of faulty signals, a novel method based on negentropy and multi-objective optimized complex Morlet wavelet filter was proposed. The parameters of the wavelet filter were optimized by the improved non-dominated sorting genetic algorithm (NSGA-II), to maximize the negentropy of both the envelope and the envelope spectrum. And then, the resonance band rich in fault information was determined by the average negentropy of the Pareto set for demodulation. The experiment results validated the effectiveness in extracting repetitive transients with complex interferences and in identifying the faults of wheel-set bearings exactly.
顾晓辉,杨绍普,刘永强,侯丽娴.基于负熵与多目标优化的轮对轴承故障诊断方法*[J].动力学与控制学报,2020,18(3):93~99; Gu Xiaohui, Yang Shaopu, Liu Yongqiang, Hou Lixian. FAULT DIAGNOSIS OF WHEEL-SET BEARING BASED ON NEGENTROPY AND MULTI-OBJECTIVE OPTIMIZATION*[J]. Journal of Dynamics and Control,2020,18(3):93-99.
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