基于SSWPT边际谱特征信息提取的齿轮故障诊断

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摘要在噪声的影响下，齿轮的故障信息不易被识别。同步压缩小波包变换(Synchrosqueezed Wave Packet Transform，SSWPT)作为一种新的时频分析方法，具有良好的抗噪声能力。本文在其基础上提出了基于SSWPT边际谱特征信息提取的齿轮故障诊断方法。首先，对故障齿轮的振动信号进行SSWPT得到信号的能量矩阵，并对能量矩阵进行积分变换求取齿轮振动信号的边际谱；然后，根据边际谱提取啮合频率及其倍频，并选择对应的啮合调制频带对能量矩阵运用同步压缩小波包逆变换(Synchrosqueezed Wave Packet Inverse Transformation，ISSWPT)进行信号重构；最后，对重构信号进行解调分析，从而可以有效提取齿轮故障特征频率。仿真及实验分析结果表明，该方法可以准确地提取齿轮故障特征信息，且分析效果优于包络谱和基于快速谱峭度的共振解调方法，为齿轮的故障特征提取提供一种有效的方法。
关键词：同步压缩小波包变换；边际谱；齿轮；故障诊断

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	唐贵基1
	徐振丽1
	庞彬2
	白洁1

关键词 ：同步压缩小波包变换, 边际谱, 齿轮, 故障诊断

Abstract：Under the influence of noise, gear fault information is difficult to be identified. As a new time-frequency analysis method, Synchrosqueezed Wave Packet Transform (SSWPT), has good ability to restrain noise. A gear fault diagnosis method of feature extraction based on SSWPT marginal spectrum was proposed on the basis of SSWPT. Firstly, the vibration signal of gear fault was transformed into energy matrix by SSWPT and the marginal spectrum of gear vibration signals was obtained by integration for energy matrix. Secondly, the meshing frequency and its multipliers were extracted by the marginal spectrum of SSWPT and the energy matrixes were reconstructed through Synchrosqueezed Wave Packet Inverse Transformation (ISSWPT). Finally, the reconstructed signal was demodulated analysis, and the fault feature of gear can be effectively extracted. Simulated and experimental results show that the proposed method is better than envelope spectrum and resonance demodulation method based on fast kurtogram. It can accurately extract fault feature information of gear and provides an effective way for extracting fault feature of gear.
Key words: synchrosqueezed wave packet transform; marginal spectrum; gear; fault diagnosis

Key words： synchrosqueezed wave packet transform marginal spectrum gear fault diagnosis

收稿日期: 2020-12-14 出版日期: 2022-07-28

引用本文:

唐贵基1，徐振丽1，庞彬2，白洁1. 基于SSWPT边际谱特征信息提取的齿轮故障诊断[J]. 振动与冲击, 2022, 41(14): 50-57.
TANG Guiji1，XU Zhenli1，PANG Bin2，BAI Jie1. Gear fault diagnosis based on SSWPT marginal spectrum feature information extraction. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(14): 50-57.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I14/50

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