基于重构贡献分析的多变量故障检测方法  

Multi-variable Fault Detection Method Based on Reconstruction Contribution Analysis

作　　者：张泽宇 吕锋[2] 杜文霞[2] 翟坤 黄战平[2] ZHANG Ze-yu;LV Feng;DU Wen-xia;ZHAI kun;HUANG Zhan-ping(College of Physics and Information Engineering, Hebei Normal University, Shijiazhuang 050024, China;College of Career Technology, Hebei Normal University, Shijiazhuang 050024, China)

机构地区：[1]河北师范大学物理科学与信息工程学院,石家庄050024 [2]河北师范大学职业技术学院,石家庄050024

出　　处：《控制工程》2019年第7期1245-1249,共5页Control Engineering of China

基　　金：国家自然科学基金(No:60974063,61175059,61673160);河北省自然科学基金(No:F2014205115);河北省教育厅课题(No:ZD2016053)

摘　　要：针对复杂系统运行过程中,变量多、采集数据量大、数据变化多样的特点,提出一种基于重构贡献分析的多故障变量检测方法.该方法在建立 PCA 模型之后,通过改进后的重构方法,可以消除传统SPE贡献图方法对故障数据不敏感和诊断能力不足的缺点,并且在多变量同时发生故障时进行故障定位.通过对风力发电机系统的实验仿真表明,该方法在对多个变量同时发生故障时,无论是存在渐变的微小故障还是突变故障,均能实现准确的诊断.A multi-variable fault detection method based on reconstruction contribution analysis is proposed in view of the characteristics of multiple variables, large amount of collected data and varied data during the operation of complex systems. The improved reconstruction method can eliminate the shortcomings of the traditional SPE contribution graph method, such as insensitivity to fault data and insufficient diagnostic ability, and can conduct fault location when multi-variable faults occur simultaneously after the establishment of PCA model. The experimental simulation of the wind turbine system shows that this method can achieve accurate diagnosis when the faults of multiple variables occur simultaneously, no matter whether there are minor faults with gradual changes or abrupt changes.

关 键 词：多变量故障 故障诊断 主元分析 重构法 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]