基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断

PDF(2839 KB)

振动与冲击 ›› 2012, Vol. 31 ›› Issue (8) : 96-101,.

论文

基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断

邵忍平, 曹精明，李永龙

作者信息 +

Gear fault pattern identification and diagnosis using Time - Frequency Analysis and wavelet de-noising based on EMD

SHAO Ren-ping, CAO Jing-ming, LI Yong-long

Author information +

文章历史 +

摘要

建立了齿轮故障系统试验装置，对齿轮传动系统在各种转速与故障状态下进行测试分析，获取了有关振动信号，对齿轮系统的无故障、齿根裂纹、分度圆裂纹、齿面磨损四种状态信号进行特征提取，并对提取的信号进行基于经验模态EMD分解的小波阈值去噪处理，然后对预处理后的信号进行时频分析与诊断。结果表明，采用基于EMD的小波阈值去噪方法比单纯采用小波阈值去噪对测试信号进行预处理，能提高信噪比，并更加有效的提取出故障特征，而在EMD的小波阈值去噪的基础上，再与时频分析方法相结合能够较好的识别不同运转状况下不同种类的故障，如齿根裂纹、分度圆裂纹、齿面磨损等，可用于对实际工程工作的齿轮系统进行故障诊断。

Abstract

The testing equipment of fault gear system was established. By measuring the vibration signals of the gear system at different rotating speed for different faults, the testing signals were obtained. The features were extracted from four kinds of signals: signal with no fault, signal with tooth root crack, signal with pitch circle crack and signal with tooth face abrasion. As the signals of the transmission system are often corrupted by noise, so it take wavelet de-noising by threshold with Empirical Mode Decomposition (EMD) as a signal preprocessing, and the preprocessed signals are investigated by using Time- Frequency Analysis. The results show that wavelet de-noising by threshold with EMD is better than using wavelet de-noising by threshold , and it can improve the SNR（Signal-to-Noise Ratio）to extract fault features better. After signal preprocessing with EMD, the results of time-frequency analysis show that the proposed method is effective for diagnosis of different kinds of fault, such as tooth root crack, pitch circle crack and tooth face abrasion.

导出引用

邵忍平;曹精明;李永龙. 基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J]. 振动与冲击, 2012, 31(8): 96-101,

SHAO Ren-ping;CAO Jing-ming;LI Yong-long. Gear fault pattern identification and diagnosis using Time - Frequency Analysis and wavelet de-noising based on EMD [J]. Journal of Vibration and Shock, 2012, 31(8): 96-101,