滚动轴承故障特征信息的自动提取方法研究

提出基于小波包分析和包络检测的滚动轴承故障特征信息的自动提取力法。根据滚动轴承的故障冲击能激起轴承座或其他机械零部件产生共振的特性，对轴承振动信号进行快速傅里叶变换FFT分析，在频谱图中自动识别高频共振频带。然后利用小波包分析可以在全频带内把信号分解到相邻的不同频带上的特性，对滚动轴承的振动信号进行小波包分解，自动提取共振频带上的信号并进行重构。最后，对重构后的信号进行包络检波，实现滚动轴承故障特征信息的自动提取。通过对实际滚动轴承振动信号的分析，发现这种方法能非常有效地检测和诊断滚动轴承的故障．

AUTO-EXTRACTION OF FAULT FEATURES IN ROLLING ELEMENT BEARING FAULT DIAGNOSIS

A method of feature auto-extraction in the rolling element bearing fault diagnosis based on wavelet packet analysis and envelope detection is presented. Firstly, on the basis of the nature that the fault impacts in the rolling element bearing can cause the machine structure to ring at its resonant frequencies, the high-frequency resonance frequency-band is auto-identified from the vibration spectrum computed using an FFT algorithm. Secondly, through wavelet packet analysis, the bearing vibration signals are decomposed into a series of time-domain signals, each of which covers a specific octave frequency band. The signals covering the high-frequency resonance frequency-band are automatically extracted and reconstructed. Finally, envelope rectification is applied to the reconstructed signals in order to realize feature auto-extraction for rolling element bearing fault diagnosis. From the analysis results of the actual vibration signals, the proposed method is proven to be efficient in detecting and diagnosing the rolling element bearing faults.