指数加权动态核主元分析法及其在故障诊断中应用

Exponentially Weighted Dynamic Kernel Principal Component Analysis Algorithm and Its Application in Fault Diagnosis

核主元分析法能充分利用核函数来解决非线性问题,具有很好的非线性逼近能力,但传统的核主元分析不能处理动态问题。在分析核主元分析法的基础上,提出一种新的指数加权核主元分析算法,建立一个多变量加权自回归统计核主元模型,选择Q统计量来判断系统是否发生故障,给出指数加权核主元分析法诊断故障的具体计算步骤。对液压泵进行了试验,利用小波包对液压泵端盖的振动信号进行处理,提取由13个时域和时频域特征量构成的故障特征矢量。试验结果表明,与传统的核主元分析法相比,新方法能实时更新主元模型和控制限Qa,合理地利用实时动态信息,能较好地处理动态问题,通过计算比较选择合适的加权因子,能获得良好的故障诊断效果,该方法是可行而有效的。

Kernel principal component analysis（KPCA） can use kernel function to solve nonlinear problem,and it has excellent nonlinear approximation ability,but traditional KPCA cannot deal with dynamic problems.A new method is proposed on the basis of exponentially weighted dynamic kernel principal component analysis algorithm,a multi-variable weighted autoregressive statistic kernel principal component model is built,Q statistics are selected to judge whether the system has fault or not,the concrete calculation steps of fault diagnosis are given.The new method is tested on the hydraulic pump,the end-cover vibration signal is processed by using wavelet packet,the fault feature vector composed of 13 time and time-frequency domain features is extracted.Test results show that the new method can renew the principal component model and control limit Qa,rationally utilize real-time dynamic information,better deal with dynamic problem,and through calculation and comparison,can select appropriate weighted factor and obtain good effect of fault diagnosis,so this method is feasible and effective.