基于变元统计分析的微小故障检测

微小故障检测对于预防重大事故的发生具有重要的意义.针对微小故障的检测问题,提出了一种变元统计分析算法(Transformed Component Statistical Analysis,TCSA).该算法对滑动时间窗口内的数据进行处理并提取变元(Transformed Component,TC),进而对变元的统计特性(均值,方差,偏度,峰度等)进行监控,以实现对微小故障的检测.该方法所提取的变元即标准化后数据的线性组合,其统计特性能反映出系统运行在正常工况下的某些不变量,而某些微小故障会打破这些平衡,进而实现对故障的检测.通过数值仿真和田纳西伊斯曼过程案例的研究,表明TCSA能够对微小传感器故障和过程故障实现有效检测.

Incipient Fault Detection Using Transformed Component Statistical Analysis

Abstract： Incipient fault detection is of significant importance for preventing the occurrence of accidents. A multivariate analysis method named transformed component statistical analysis (TCSA) was proposed to solve the incipient fault detection problem. The algorithm processes the data in the sliding time window to extract transformed components. Statistics (mean, variance, skewness, kurtosis) of transformed components were monitored to realize the detection of incipient faults. The transformed components extracted by the approach are linear combinations of the normalized data. Statistics of transformed components can reflect some invariants under normal condition. Some incipient faults break the balance and therefore can be detected. Numerical simulation and Tennessee Eastman process (TEP) simulation indicate that TCSA is able to detect both incipient sensor faults and process faults effectively.