For the modulation and energy concentration feature of fault ball bearing vibration signals, a fault diagnosis study based on wavelet packet energy and Hilbert transform was put forward. The vibration signals of ball bearing were decomposed and reconstructed using wavelet packet transform. And energy of every frequency band was calculated, then the signals of the frequency band with maximal energy were analyzed by applying Hilbert transform. Finally, the characteristic frequency of fault signals was extracted. The computation of fault features was accomplished artificially. A new method which can select fault features automatically was presented herein. Through processing and analyzing the practical ball bearing experimental data, it is shown that the fault diagnosis study can diagnose different running states of ball bearings due to surface damage accurately and quickly.

针对故障轴承振动信号能量集中与调制的特点,提出了一种基于小波包能量法与Hilbert变换的滚动轴承故障诊断方法。使用小波包变换对振动信号进行分解、重构及能量计算,并应用Hilbert变换对能量集中频段的重构信号进行解调和频谱分析,提取故障特征频率。同时针对诊断过程中故障特征参数依靠人工计算的问题,提出故障特征参数自动提取方法。实际的滚动轴承实验数据的处理和分析结果表明,该诊断方法能够准确、快速地识别滚动轴承表面损伤的故障模式。

滚动轴承, 小波包变换, Hilbert变换, 故障诊断

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