基于CVA-ICA与CSM的故障诊断方法

针对工业过程的故障诊断问题,提出了一种基于规范变量分析与独立元分析(CVA-ICA)的动态过程故障检测方法,在此基础上,结合连续字符串匹配(CSM)算法,提出了一种改进的基于完备故障库的故障诊断算法.该算法首先用CVA方法求出观测数据的规范变量,然后对规范变量进行ICA分解,最后运用CSM算法对ICA分解后的数据进行故障诊断.通过对TE过程的仿真研究,验证了所提出的改进算法的可行性与有效性.

Fault Diagnosis Based on CVA-ICA and CSM

In order to handle the problem of fault diagnosis for industrial processes, an improved fault detection method was proposed based on canonical variable analysis (CVA) and independent component analysis (ICA). At the same time, combined with continuous string matching (CSM), a new fault diagnosis method based on the library of complete faults was proposed. First, the CVA algorithm was used to calculate the canonical variable of the data, and then, the ICA algorithm was used to decompose the canonical variable. Finally, the CSM algorithm was used to diagnose the faults. A case study of Tennessee Eastman (TE) process showed that the proposed algorithm is feasible and efficient.