Between-class difference analysis based multidimensional RBC for multivariate fault isolation of industrial processes

Abstract

In industrial process, fault isolation technology will identify the major variables leading to faults. The existing fault isolation methods usually have ”smearing effect”, cannot identify the sensitivity of the fault variables, or require too much calculation. To solve the above problems, a multivariate fault isolation method based on between-class difference analysis and multidimensional reconstruction-based contribution (RBC) is proposed in this work. First, two kind of strategies, i.e.,principle component analysis (PCA) and fisher discriminant analysis (FDA), are adopted to obtain the sensitivity indicators of the variables to the faults respectively, which is taken as the basis for determining the optimal reconstruction direction. Then, multidimensional RBC is used to determine the number of fault variables based on the selected reconstruction direction. Finally, the primary and secondary fault variables are isolated according to their fault sensitivities. The feasibility of the proposed method is illustrated by a numerical simulation example and TE process. To sum up, the proposed method provide sensitivity information of each fault variables and high diagnosis rate. Moreover, it also has an advantage in reducing the calculation time because of the preliminary selection of fault variables.