多元指数加权移动平均主元分析的微小故障检测

Incipient fault detection of multivariate exponentially weighted moving average principal component analysis

主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA–PCA)方法用于解决PCA不能有效检出微小故障的问题.本文深入研究了MEWMA–PCA中EWMA影响主元分析进行故障检测的机制,导出了MEWMA–PCA可检出微小故障的原因.本文确定了MEWMA–PCA中遗忘因子λ、单传感器故障幅值和迟延时间三者的关系,并进行了数值仿真和火电厂磨煤机组运行状态的仿真实验.实验结果验证了MEWMA–PCA中EWMA提高PCA的监测性能的机制,并给出了根据系统实际要求来选取合适的遗忘因子值,从而在规定的时间内检出微小故障的实例.

The principal component analysis （PCA） is a useful tool for data analysis and has been widely used in fault de- tection and process monitoring. MEWMA-PCA （multivariate exponentially weighted moving average principal component analysis） is used to solve the problem where PCA cannot detect incipient faults properly. This paper further investigates the mechanism of the effect of EWMA on the fault detection of PCA in MEWMA-PCA. The reason that MEWMA-PCA can detect incipient faults is analyzed. The relationship among the forgetting factor, the detectable amplitude of a single sensor and the delay time introduced by EWMA is derived. Both numerical simulation results and historical data simulation result of a coal grinding unit in a power plant validate the mechanism of the improvement of fault detection by MEWMA-PCA. An example is given for showing the detection of an incipient fault within a specified time range satisfying the practice requirement, by setting appropriate forgetting factor.