基于增强熵权峭度图的滚动轴承最优频带解调的故障诊断

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摘要针对采用峭度指标选取滚动轴承最优共振解调频带时存在精确性差和易受随机冲击干扰的不足，提出了基于增强熵权峭度图的滚动轴承最优频带解调的故障诊断。首先，选取峭度指标、变异系数、裕度指标和平滑因子等评价指标分别从不同角度表征故障轴承瞬态冲击特征，增强在实际监测的运行环境中单指标抗干扰鲁棒性差的缺陷；其次，采用熵权法对多个评价指标计算综合属性客观权重，为了增强轴承瞬态冲击表征和抑制其它信息干扰，对评价指标值进行增强处理并融合形成新评价指标，以精确选取共振解调中心和带宽；最后，采用1/3-二叉树策略生成的增强熵权峭度图自适应识别故障轴承最优解调频带。通过实测信号和仿真信号测试分析，并与快速峭度图在不同干扰下对比表明，本文所提方法可以更为精准的识别轴承共振频带及具有更高的鲁棒性。

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	李红贤1
	2
	汤宝平1
	韩延1
	邓蕾1

关键词 ：滚动轴承, 峭度图, 熵权法, 最优频带, 故障诊断

Abstract：Aiming at shortcomings of poor accuracy and easy to be interfered by random impacts during selecting faulty rolling bearing’s optimal resonance demodulation frequency band using kurtosis index, the enhanced entropy weight Kurtosis graph was proposed to select optimal demodulation frequency band for rolling bearings’ fault diagnosis.Firstly, kurtosis index, variation coefficient, margin index and smoothness factor etc.were chosen to characterize a faulty bearing’s transient impact features from different angles to avoid defects of poor robustness and lower anti-disturbance ability of a single index under the actual monitoring and operating environment.Then, the entropy weight method was used to calculate the comprehensive attribute objective weight for multiple evaluation indexes.In order to enhance bearing transient impact characterization and suppress other information interference, evaluation indexes were enhanced and merged to form a new evaluation index, and the resonant demodulation center and bandwidth were accurately chosen.Finally, the enhanced entropy weight Kurtosis graph was generated with the 1/3-binary tree strategy to adaptively identify a faulty bearing’s optimal demodulation frequency band.Through test analysis for actual measured signals and simulated ones and compared with fast speed kurtosis graphs under different interferences, it was shown that the proposed method can be used to more accurately identify faulty bearing resonant frequency band, and it has a higher robustness.

Key words： rolling bearing kurtogram entropy weight method optimal frequency band fault diagnosis

收稿日期: 2018-03-20 出版日期: 2019-08-28

引用本文:

李红贤1，2，汤宝平1，韩延1，邓蕾1. 基于增强熵权峭度图的滚动轴承最优频带解调的故障诊断[J]. 振动与冲击, 2019, 38(17): 24-31.
LI Hongxian1,2, TANG Baoping1, HAN Yan1, DENG Lei1. Fault diagnosis of rolling bearings using optimal demodulation frequency band based on enhanced entropy weight kurtosis graph. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(17): 24-31.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I17/24

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