First Department, Ordnance Engineering College, Shi Jia-zhuang, He Bei province, PR China.
ISA Trans. 2011 Oct;50(4):599-608. doi: 10.1016/j.isatra.2011.06.003. Epub 2011 Jul 1.
This paper presents a novel signal processing scheme, named the weighted multi-scale morphological gradient filter (WMMG), for rolling element bearing fault detection. The WMMG can depress the noise at large scale and preserve the impulsive shape details at small scale. Both a simulated signal and vibration signals from a bearing test rig are employed to evaluate the performance of the proposed technique. The traditional envelope analysis and a multi-scale enveloping spectrogram algorithm combining continuous wavelet transform and envelope analysis (WT-EA) are also studied and compared with the presented WMMG. Experimental results have demonstrated the effectiveness of the WMMG to extract the impulsive components from the raw vibration signal with strong background noise. We also investigated the classification performance on identifying bearing faults based on the WMMG and statistical parameters with varied noise levels. Application results reveal that the WMMG achieves the same or better performance as EA and WT-EA. Meanwhile, the WMMG requires low computation cost and is very suitable for on-line condition monitoring of bearing operating states.
本文提出了一种新的信号处理方案，称为加权多尺度形态梯度滤波器（WMMG），用于滚动轴承故障检测。WMMG 可以在大尺度上抑制噪声，同时在小尺度上保留脉冲形状细节。模拟信号和轴承试验台的振动信号都被用来评估所提出技术的性能。传统的包络分析和一种结合连续小波变换和包络分析的多尺度包络频谱算法（WT-EA）也进行了研究，并与所提出的 WMMG 进行了比较。实验结果表明，WMMG 可以有效地从具有强背景噪声的原始振动信号中提取脉冲分量。我们还研究了基于 WMMG 和统计参数的在不同噪声水平下识别轴承故障的分类性能。应用结果表明，WMMG 的性能与 EA 和 WT-EA 相同或更好。同时，WMMG 需要的计算成本低，非常适合滚动轴承运行状态的在线状态监测。
