结合非线性频谱和核主元分析的机器人用RV减速器故障诊断

Fault Diagnosis for Robot RV Reducer Using Nonlinear Frequency Spectrum and Kernel Principal Component Analysis

针对机器人用RV减速器故障诊断准确率低问题,采用基于非线性输出频率响应函数频谱与核主元分析(KPCA)相结合的方法诊断RV减速器故障。利用RV减速器性能测试平台采集减速器在正常状态和故障状态下的输入和输出数据;采用批量估计算法得到每种状态下的前4阶频谱值,将其作为故障特征送入KPCA进行压缩,通过设置主元累计贡献率将400维数据压缩至5维;将KPCA生成的低维数据送入支持向量机分类器进行训练和测试。试验结果表明:与仅把振动信号时域或频域作为数据集进行故障诊断的方法相比,所提方法的故障诊断准确率分别提升了27.50%和8.34%,达到了96.67%,所提方法在RV减速器的故障诊断上有效。

Aiming at the low accuracy of fault diagnosis of RV reducer for robot,a method based on the combination of nonlinear output frequency response(NOFRF)spectrum and kernel principal component analysis(KPCA)is adopted to diagnose the fault of RV reducer.The input and output data of RV reducer in normal state and fault state are collected by the performance testing equipment.Then,the first four-order NOFRF spectrum values,which are sent to KPCA as system fault feature for compression are estimated with batch estimation algorithm.Setting the cumulative contribution rate of principal components,400-dimension data are compressed to 5-dimension ones.The low-dimension data generated by KPCA are sent to support vector machine classifier for training.Experimental results show that compared with the method only using time domain or frequency domain of vibration signal as data sets,the fault recognition rate of proposed method achieved 96.67%with 27.50%and 8.34%increasement,respectively,the effectiveness of the proposed method in fault diagnosis of RV reducer is thus verified.