A Multi-agent Model with Dynamic Leadership for Fault Diagnosis in Chemical Plants
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Timely fault detection and diagnosis are critical matters for modern chemical plants and refineries. Traditional approaches to fault detection and diagnosis of those complex systems produce centralized models that are very difficult to maintain. In this article, we introduce a biologically inspired multi-agent model which exploits the concept of leadership; that is, when a fault is detected one agent emerges as leader and coordinates the fault classification process. The proposed model is flexible, modular, decentralized, and portable. Our experimental results show that even using simple detection and diagnosis methods, the model can achieve comparable results to those from sophisticated centralized approaches.
KeywordsMulti-agent systems modeling distributed data fusion fault diagnosis collective consensus
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