基于WPD-KVI-Hilbert变换相结合的滚动轴承早期故障特征精准识别

Accurate Identification for Early Fault Features of Rolling Bearings Based on WPD-KVI-Hilbert Transform

关键词:  最优小波包基  峭度值  滚珠轴承  滚棒轴承  早期故障诊断

Key words:  Optimal wavelet packet base  Kurtosis value  Ball bearing  Roller bearing  Early fault diagnosis

摘要:

为了能够有效地从轴承早期故障激励的高频振动信号中提取出故障特征信息，基于最优小波包基选取方法和峭度值最大筛选原则，提出了一种改进的小波包分解（WPD）、峭度值指标（KVI）与Hilbert变换相结合的滚动轴承早期故障特征识别方法。计算选取最优小波包基，确定分解层数；采用WPD方法对轴承故障振动信号进行分解，获得若干个Node分量；基于峭度值指标最大原则筛选出有效的Node分量进行信号重构；对重构信号进行包络解调分析，提取出故障特征频率对轴承故障进行诊断。采用建立的方法对凯斯西储大学滚珠轴承外圈、内圈故障实验数据和自行开展的滚棒轴承外圈、滚动体故障实验数据进行了分析与诊断。研究结果表明：该方法能够有效提高故障信号高频分辨率、保留周期性冲击成分，并能准确有效提取出滚珠和滚棒轴承故障特征频率的1~7倍频及其与轴转频调制的系列边频带频率，实现对滚动轴承故障特征的精准识别与故障诊断。

Abstract:

In order to effectively extract the bearing fault information from the high frequency vibration signal generated by the bearing early fault excitation， the optimal wavelet packet basis selection method and the kurtosis value maximum screening principle are applied. A recognition method of the rolling bearing early fault is proposed based on the combination of improved wavelet packet decomposition （WPD）， kurtosis value index （KVI） and Hilbert transform. Firstly， the optimal wavelet packet basis is calculated and the decomposition layer number is determined. Secondly， the WPD method is used to decompose the bearing fault vibration signals and several node components are obtained. Then， effective node components are selected for signal reconstruction based on the maximum principle of kurtosis index. Finally， the envelope demodulation analysis is carried out on the reconstructed signals， and the fault characteristic frequency is extracted to diagnose the bearing fault. The established method is used to analyze and diagnose the experimental data of outer ring and inner ring faults of ball bearing in Case Western Reserve University and the self-developed experimental data of outer ring and rolling body faults of roller bearing. The results show that the method can effectively improve the high-frequency resolution of fault signals and retain periodic impact components. In addition， this method can also accurately and effectively extract the 1~7 frequency doubling of the faults characteristic frequencies of ball bearing and roller bearing， and a series of their side band frequency modulated with the rotating frequency of shaft， which realizes accurate identification and effective diagnosis of rolling bearing faults.