Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods

Abstract

Numerous methodologies for fault detection and identification (FDI) in chemical processes have been proposed in literature. However, it is extremely difficult to design a perfect FDI method to efficiently monitor an industrial-scale process. In this work, we seek to overcome this difficulty by using multiple heterogeneous FDI methods and fusing their results so that the strengths of the individual FDI methods are combined and their shortcomings overcome. Several decision fusion strategies can be used for this purpose. In this paper, we study the relative benefits of utility-based and evidence-based decision fusion strategies. Our results from a lab-scale distillation column and the popular Tennessee Eastman challenge problem show that in situations where no single FDI method offers adequate performance, evidence-based fusion strategies such as weighted voting, Bayesian, and Dempster–Shafer based fusion can provide (i) complete fault coverage, (ii) more than 40% increase in overall fault recognition rate, (iii) significant improvement in monitoring performance, and (iv) reduction in fault detection and diagnosis delays.