特征尺度谱算法及其在轴承故障诊断中的应用

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摘要针对基于包络谱的方法在轴承故障诊断中存在故障特征谐波衰减过快、并受较强的转频分量干扰，导致对故障程度判断的可靠性降低等问题，提出了一种全新的特征尺度谱轴承故障诊断方法。通过对振动信号进行分帧处理，采用特征算子提取其波动特征，利用二值化阈值处理方式对特征帧信号进行分类，获得信号中的故障区间并对故障区间进行压缩，将故障区间转化成特征脉冲信号，实现故障信号能量增强，并利用仿真信号和轴承故障实验台采集的数据对所提方法进行验证。结果表明：相较基于包络谱的方法，所提方法解决了频谱中故障特征谐波衰减过快的缺点，消除了转频分量的干扰，在低信噪比情况下具有更强的诊断效果。
关键词：轴承故障诊断; 故障特征谐波; 特征尺度谱; 波动特征; 故障区间

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	王贡献
	赵博琨
	胡志辉
	向磊
	张淼

关键词 ：轴承故障诊断, 故障特征谐波, 特征尺度谱, 波动特征, 故障区间

Abstract：To solve the problems that envelope spectrum-based method in bearing fault diagnosis has heavy attenuation of fault harmonic, and is interfered by strong rotational frequency., which reduces the reliability of the the fault degree judgment, a novel bearing fault diagnosis method called feature scale spectrum(FSS) is proposed in the paper. First, the vibration signal is segmented, and the fluctuation feature is extracted through the feature operator. Then the binary threshold processing is used to classify the feature frame signal and extract the fault interval. The fault interval is compressed and transformed into feature pulse to realize fault signal energy enhancement. Simulation and experimental data are used to verify the proposed method. The results show that: compared with the envelope spectrum-based method, the proposed method solves the shortcomings of heavy attenuation of fault harmonic in the spectrum, eliminates the interference of rotational frequency, and has a stronger diagnostic performance in low signal-to-noise ratio.
Key words：bearing fault diagnosis; fault harmonic; feature scale spectrum; fluctuation feature; fault interval

Key words： bearing fault diagnosis fault harmonic feature scale spectrum fluctuation feature fault interval

收稿日期: 2021-06-15 出版日期: 2022-11-15

引用本文:

王贡献,赵博琨,胡志辉,向磊,张淼. 特征尺度谱算法及其在轴承故障诊断中的应用[J]. 振动与冲击, 2022, 41(21): 286-291.
WANG Gongxian, ZHAO Bokun, HU Zhihui, XIANG Lei, ZHANG Miao. Feature scale spectrum algorithm and its application in bearing fault diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(21): 286-291.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I21/286

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