一种基于改进核Fisher的故障诊断方法  

A fault diagnosis method based on improved kernel Fisher

作　　者：马立玲[1] 徐发富 王军政[1] 

机构地区：[1]北京理工大学自动化学院,北京100081

出　　处：《化工学报》2017年第3期1041-1048,共8页CIESC Journal

基　　金：国家自然科学基金项目(61503027)~~

摘　　要：针对化工过程故障数据呈非线性分布,数据类别复杂,难以进行故障诊断的问题,提出一种改进核Fisher的故障诊断方法。对于原始样本数据投影后,样本出现因类间距离存在很大差异性而导致部分类别样本存在混叠的现象,以及不同类别的边界数据归类模糊问题,给出了统一的解决办法。该方法首先采用改进类间距的方法来改变样本投影空间的分布,使得样本具有较好的投影效果,然后通过定义阈值参数来筛选出边界数据,对于边界数据,采用改进的K近邻(K-NN)算法来分类,对于非边界数据,采用马氏距离来分类。最后在TE过程中进行了仿真验证,结果表明方法在兼顾了故障诊断时间的同时,有效提高了故障诊断精度。A fault diagnosis method of kernel Fisher projection was proposed to solve issues of nonlinear data, complex classes, and difficult fault-diagnosing in chemical processes. The proposed method provided a uniform solution for partial sample mix-up induced by a large difference in category distances and nebulous classification of different category＇s boundary data after projection of the original data sample. First, an improved category distance was used to change sample distribution in the projection space so that the sample had a good projection. Then, boundary data was screened out by a defined threshold parameter and classified by improved K-Nearest Neighbor（K-NN） algorithm, which none-boundary data was classified by Mahalanobis distance. Simulation in a TE process showed that training accuracy was increased by 2.25% and testing accuracy was increased by 2.41% in the first experiment, whereas training accuracy was increased by 4.75% and testing accuracy was increased by 6.75% in the second experiment. Therefore, the method improved both fault diagnosis time and accuracy.

关 键 词：核FISHER 故障诊断 改进K-NN算法 实验验证 优化 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]