工业机器人RV齿轮箱早期故障改进OSF方法识别

Improved OSF Method for Early Fault Identification of RV Gear Box of Industrial Robot

为了进一步提高工业机器人用RV齿轮箱的故障诊断能力,设计了一种基于自适应Autogram改进顺序统计滤波(OSF)的振动信号识别方法。对振动信号实施OSF计算后,采用移动平均法实现数据的平滑处理,获得更优的包络数组。研究结果表明:与自适应Autogram方法相比,并未观察到显著的故障特征频率,表明采用本方法测试佳解调频带的效果比滤波器快速谱峭度方法的性能更优。相对所提方法,对应二倍与三倍频没有形成明显特征频率,受到其他分量干扰,综合分析可知所提自适应Autogram方法具备明显优越性。该研究有助于提高工业机器人RV齿轮箱排除隐藏故障的能力,也可拓展到其他的机械传动机构上。

In order to further improve the fault diagnosis capability of RV gear box for industrial robots,a vibration signal recognition method based on adaptive Autogram improved sequential statistical filter(OSF)was designed.After OSF calculation of vibration signals,the moving average method is used to smooth the data and obtain a better envelope array.The results show that,compared with the adaptive Autogram method,no significant fault characteristic frequency is observed,which indicates that the performance of the proposed method is better than that of the filter fast spectral kurtosis method.Compared with the proposed method,the corresponding double and triple frequencies do not form obvious characteristic frequencies,and are interfered by other components.Comprehensive analysis shows that the proposed adaptive Autogram method has obvious advantages.This research is helpful to improve the ability of RV gear box of industrial robot to remove hidden faults,and can also be extended to other mechanical transmission mechanisms.