摘要
针对多传感器的相关时序测量数据,在假设只存在传感器故障的前提下,提出了一种基于动态主成分分析(DPCA)的传感器故障检测方法。根据测量数据建立传感器的DPCA模型,在该模型基础上利用T2和SPE统计量进行传感器的故障检测。同时,将基于主成分分析(PCA)模型的传感器有效度指标SVI推广应用于DPCA模型中。通过对污水处理系统中重要传感器的故障诊断仿真实验表明:该方法能有效地检测和识别出故障传感器。
In order to deal with the time series data from multiple sensors, a fault detection approach based on dynamic principal component analysis （DPCA）is proposed. With this approach, normal samples are used as training data to develop a DPCA model. T2 statistic and SPE statistic are used to detect fault. SVI statistic performed as indexes of fauh sensor diagnosis. Several simulations in sewage disposal system are used to validate the DPCA. The results show that the DPCA model can effectively extract the dynamic relations among process variables and sensor fault detection.
出处
《传感器与微系统》
CSCD
北大核心
2009年第12期35-38,共4页
Transducer and Microsystem Technologies
关键词
传感器
动态主成分分析
故障诊断
污水处理系统
sensor
dynamic principal component analysis（DPCA）
fault detection
sewage disposal system
