Automatica (Journal of IFAC)
Planning and control
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Faults Diagnosis in Industrial Processes with a Hybrid Diagnostic System
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
ISADS '99 Proceedings of the The Fourth International Symposium on Autonomous Decentralized Systems
Logic programming for robot control
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IEEE Transactions on Neural Networks
Fault diagnosis in power networks with hybrid Bayesian networks and wavelets
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
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In this paper we formalize an approach to detect and diagnose faults in dynamic industrial processes using a probabilistic and logic multiagent framework. We use and adapt the Dynamic Independent Choice Logic (DICL) for detection and diagnosis tasks. We specialize DICL by introducing two types of agents: the alarm processor agent, that is a logic program that provides reasoning about discrete observations, and the fault detection agent that allows the diagnostic system to reason about continuous data. In our framework we integrate artificial intelligence model-based diagnosis with fault detection and isolation, a technique used by the control systems community. The whole diagnosis task is performed in two phases: in first phase, the alarm processor agent reasons with definite symptoms and produces a subset of suspicious components. In second phase, fault detection agents analyze continuous data of suspicious components, in order to discriminate between faulty and non-faulty components. Our approach is suitable to diagnose large processes with discrete and continuous observations, nonlinear dynamics, noise and missing information.