摘要
针对齿轮故障发生早期信号微弱、难以诊断和现场干扰的问题,提出了一种基于相关性分析、峭度准则与小波变换包络谱分析相结合的齿轮早期故障诊断方法。该方法不仅有良好的降噪效果,而且能够实现故障频段的自动选择,提高诊断效率。仿真结果表明,该方法能有效去除噪声,对故障特征信号有很强的提取能力,为齿轮的故障诊断提供了一种快速有效的途径。
Aiming at the problem of weak signal, difficult to diagnose and field disturbance in the early stage of gear fault,a method for early fault diagnosis of gear ,which was based on correlation analysis, kurtosis criterion and wavelet envelope spectrum analysis combination is presented. The method not only has a good noise reduction effect, but also can realize the automatic selection of the fault frequency band, which can improve the diagnosis efficiency. Simulation results show that, the method can effectively remove the noise, and has a strong ability to extract the fault characteristic signals, which provides a fast and effective way for the fault diagnosis of gear.
出处
《煤矿机械》
2016年第2期183-185,共3页
Coal Mine Machinery
基金
国家自然科学基金(51275136)
关键词
齿轮
故障诊断
自相关分析
峭度
小波变换
包络谱分析
gear
fault diagnosis
autocorrelation analysis
kurtosis
wavelet transform
envelope spectrum analysis
