Multi-agent based collaborative fault detection and identification in chemical processes

Abstract

Fault detection and identification (FDI) has received significant attention in literature. Popular methods for FDI include principal component analysis, neural-networks, and signal processing methods. However, each of these methods inherit certain strengths and shortcomings. A method that works well under one circumstance might not work well under another when different features of the underlying process come to the fore. In this paper, we show that a collaborative FDI approach that combines the strengths of various heterogeneous FDI methods is able to maximize diagnostic performance. A multi-agent framework is proposed to realize such collaboration in practice where different FDI methods, i.e: principal component analysis, self-organizing maps, non-parametric approaches, or neural-networks are combined. Since the results produced by different FDI agents might be in conflict, we use decision fusion methods to combine FDI results. Two different methods – voting-based fusion and Bayesian probability fusion are studied here. Most monitoring and fault diagnosis algorithms are computationally complex, but their results are often needed in real-time. One advantage of the multi-agent framework is that it provides an efficient means for speeding up the execution time of the various FDI methods through seamless deployment in a large-scale grid. The proposed multi-agent approach is illustrated through fault diagnosis of the startup of a lab-scale distillation unit and the Tennessee Eastman Challenge problem. Extensive testing of the proposed method shows that combining diagnostic classifiers of different types can significantly improve diagnostic performance.