Abstract
In this paper we present an approach to detect and diagnose multiple faults in industrial processes with a hybrid multiagent diag- nostic system. We integrate Artificial intelligence model-based diagnosis with control systems Fault Detection and Isolation (FDI)techniques. We adapt a probabilistic logic framework to perform fault detection and diagnosis tasks. The whole diagnosis task is performed by agents and is executed in two phases. In first phase, the Alarm Processor (AP)agent processes the discrete observations and alarms, and outputs a set of most likely faulted components. In second phase, Fault Detection (FD)agents discard the components not participating in the failure, by analyzing a set of continuous signals, that have a different behavior in normal and in faulty state. The FD agents include dynamic probabilistic models, able to deal with noise, nonlinear behavior and missing data. The output of the diagnostic system includes the components with abnormal behavior and the type of faults. We have tested our approach by diagnosing faults in a simulated electrical power network.
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References
Basseville M., Benveniste A., and Tromp L. 1998. Diagnosing Hybrid Dynamical Systems: Fault Graphs, statistical residuals and Viterbi Algorithms. In Proc. of the 37th IEEE CDC, Tampa, Fla.1998, pp. 3757–3762.
Dagum P., Galper A., Horvitz E., and Seiver A. 1995. Uncertain Reasoning and Forecasting, Technical Report, Stanford University School of Medicine, Stanford, Cal. June 29.
Dean T., and Wellman M.1991. Planning and Control.San Mateo, CA: Morgan Kaufmann.
Isermann R. and Ballé P.1997. Trends in the Application of Model-Based Fault Detection and Diagnosis of Technical Processes. Control Eng. Practice,Vol. 5, No. 5, pp.709–719.
Isermann R. 1997. On Fuzzy Logic Applications for Automatic Control, Supervision, and Fault Diagnosis. IEEE Trans. on SMC-Part A: Systems and Humans, Vol. 28, No. 2, pp.221–235.
Lerner U., Parr R., Koller, D., and Biswas G. 2000. Bayesian Fault Detection and Diagnosis in Dynamic Systems. In Proc. of the 17th National Conference on AI 2000, Austin, TX, August 2000, pp. 531–537.
Microtran Power Systems Analysis Corporation. Microtran Reference Manual, Vancouver, BC Canada 1997.
Momoh J., Dias L., and Laird D.1997. An Implementation of a Hybrid Intelligent Tool for Distribution System Fault Diagnosis. IEEE Trans. on Power Delivery, Vol. 12, No. 2, pp. 1035–1040.
Nicholson A. E., and Brady J. M. 1994. Dynamic Belief Networks for Discrete Monitoring. IEEE Trans. On SMC 24(11): 1593–1610.
Poole D., Logic programming for robot control. In Proc.14th IJCAI, Montreal, August,1995,150–157.
Poole D., The Independent Choice Logic for Modeling Multiple Agents under Uncertainty, Artificial Intelligence,Vol. 94, No. 1–2, special issue on economic principles of multi-agent systems, pp.7–56,1997.
Provan G. and Elsley D.2000. Software Toolkit for Aerospace Systems Diagnostics. In IEEE Aerospace Conf. Proc. 2000,Vol.6, pp. 327–335.
Reliability Test System Task Force, Application of Probability Methods Subcomitee. IEEE Reliability Test System. IEEE Trans. on Power Apparatus and Systems, Vol. 98, No. 6, pp. 2047–2054, 1979.
Rayudu R., Samarasinghe S., and Maharaj A., A Co-operative Hybrid Algorithm for Fault Diagnosis in Power Transmission, IEEE Trans. on Power Delivery,Vol. 12, No. 1, pp.1939–1944, 2000.
Sampath M.2001. A Hybrid Approach to Failure Diagnosis in Industrial Systems. In Proc. of the ACC 2001, pp. 2077–2082.
Yan L., and Miller D., General statistical inference for discrete and mixed spaces by an approximate application of the maximum entropy principle. IEEE Trans. On Neural Networks, 11 (3): 558–573.
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Luis E., G., Francisco J., C., Salvador, A. (2002). Faults Diagnosis in Industrial Processes with a Hybrid Diagnostic System. In: Coello Coello, C.A., de Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science(), vol 2313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46016-0_56
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DOI: https://doi.org/10.1007/3-540-46016-0_56
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