Dynamic Bhattacharyya Bound-Based Approach for Fault Classification in Industrial Processes

Abstract

Data-driven fault diagnosis has attracted increasing research interest with a recent trend of aiming at large-scale and complex systems. In this article, we propose a method under a probabilistic framework, named dynamic Bhattacharyya bound (DBB), to extract features for fault diagnosis. An information criterion is adopted to determine the order of dimensionality reduction and time lags when applying the proposed approach. Compared with conventional diagnostic approaches, the proposed DBB approach has several advantageous features. First, the DBB approach minimizes an upper bound of the Bayes error which is a direct manifestation of the misclassification rate. Second, pairwise Bhattacharyya bounds between different faults are summed up in the objective function, enabling it to address the fault diagnosis of multiple faults that may have large overlaps. The proposed method is validated through the Tennessee Eastman process and it shows advantageous performance than other methods such as Fisher discriminant analysis (FDA), dynamic FDA, and LP-DFDA.