一种用于故障监测的优化核主元分析方法  

Fault detection based on an optimized kernel principal component analysis

作　　者：肖应旺 姚美银 刘军 张绪红 陈贞丰 XIAO Yingwang;YAO Meiyin;LIU Jun;ZHANG Xuhong;CHEN Zhenfeng(School of Automation,Guangdong Polytechnic Normal University,Guangzhou 510665,Guangdong,China;Equipment and Laboratory Management Office,Guangdong Polytechnic Normal University,Guangzhou 510665,Guangdong,China)

机构地区：[1]广东技术师范大学自动化学院,广东省广州市510665 [2]广东技术师范大学院设备与实验室管理处,广东省广州市510665

出　　处：《计算机与应用化学》2019年第4期434-438,共5页Computers and Applied Chemistry

基　　金：广东省自然科学基金资助项目(2017A030313364)

摘　　要：提出了一种基于混沌粒子群的优化核主元分析故障监测方法(Kernel Principal Component Analysis based on Chaotic Particle Swarm Optimization, CPSO-KPCA)。该方法充分利用了正常数据和故障数据的特征,通过混沌粒子群优化算法对KPCA的核函数参数进行优化,以发现最优的非线性特征,并能准确地监测出非线性故障。利用特征空间监测统计图,将该方法应用于轧钢过程的非线性监测,实际应用结果表明,该方法具有很高的故障监测精度。An optimized kernel principal component analysis based on chaotic particle swarm optimization method(CPSO-KPCA) is put forward in this paper. The improved method adequately makes use of the characteristics of normal data and fault data to optimize the parameters of the mixture kernel function through chaotic particle swarm optimization so that the optimal nonlinear feature can be discovered and nonlinear fault can be detected accurately. Based on monitoring statistics charts in the feature space, this method is applied to fault detection in rolling process which is a nonlinear process. Practical application shows that the presented method has higher accuracy in fault detection.

关 键 词：KPCA PSO 非线性特征 故障监测 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]