基于MAF的传感器故障检测与诊断  

Sensor fault detection and diagnosis using MAF

作　　者：付克昌[1] 袁世辉[2] 蒋世奇[1] 朱明[1] 沈艳[1] 

机构地区：[1]成都信息工程学院控制工程学院,四川成都610025 [2]中国燃气涡轮研究院,四川江油621703

出　　处：《化工学报》2015年第5期1831-1837,共7页CIESC Journal

基　　金：航空科学基金项目(201210P8003);四川省应用基础研究项目(2014JY0257);四川省科技厅科技支撑计划项目(2014GZ0009);四川省教育厅自然科学重点项目(14ZA0171);四川省教育厅青年基金项目(11ZB087)~~

摘　　要：针对工业控制系统中变量之间既存在线性相关性,且在时间结构上呈现自相关的特点,提出了一种基于最小/最大自相关因子(min/max autocorrelation factors,MAF)分析的传感器故障检测与诊断方法。首先,利用正常工况下的历史数据进行自相关因子分析,获得强自相关因子和弱自相关因子;在此基础上构造故障检测统计量,由核密度估计方法获得故障检测控制限,根据贡献图进行传感器故障定位。将所提出的方法应用于连续反应釜仿真过程的传感器故障检测与诊断,与经典的多变量统计方法——主元分析方法相比,所提出的方法能避免虚警,更快地检测缓变故障,并能更好地诊断和解释复杂故障。For industrial processes, there are not only correlations among variables, but also autocorrelation in temporal structure of these variables, therfore, a new sensor fault detection and diagnosis method based on min/max autocorrelation factors （MAF） was proposed in this work. Firstly, MAF analysis of historical normal data was made. Then, strong autocorrelation factors and weak autocorrelation factors were obtained. Based on these factors, the statistics for fault detection were constructed and corresponding contribution plots were derived. The proposed method was applied to the continuous stirred tank reactor （CSTR） and compared with the principal component analysis method. Simulation results demonstrated that the proposed method could detect sensor faults with slow variation more quickly with less false-alarm. The contribution plots based on MAF can explain complicated sensor fault more reasonably than principal component analysis （PCA）, which is a classical multivariate statistical method for process monitoring.

关 键 词：最小/最大子自相关因子 主元分析 过程系统 传感器故障诊断 算法 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]