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Arroyo-Figueroa et al. 1 Temporal Bayesian Network of Events for Fault Diagnosis and Prediction in Thermal Power Plants Gustavo Arroyo-Figueroa Instituto de Investigaciones Electricas P.O. Box 475-1, 62001 Cuernavaca, Mor., Mexico email: [email protected] L. Enrique Sucar ITESM-Campus Cuernavaca P.O. Box C-99 62020 Cuernavaca, Morelos, Mexico email: [email protected] Introduction The last 15 years the operating conditions of thermal power plants have changed. Today, the operation of thermal power plants must be optimal considering higher productions profits, safer operation and stringent environment regulation. An additional factor is the increment of the age of the plants. The reliability and performance of the plants is affected by its age. This means an increase in the number of equipment failures, thus increasing the number of diagnoses and control decisions which the human operator must make. Under this conditions, the complexity of the operation of thermal power plants have been increased significantly. As a result of these changes, computer and information technology have been extensively used in thermal plant process operation. Distributed control systems (DCS) and management information systems (MIS) have been playing an important role to shows the plant status. However, in non-routine operations such as equipment failures and extreme operation (star up phase, changes in the load, etc.), human operators have to rely on their own experience. During disturbances, the operator must determine the best recovery action according to the type and sequence of the signals received. In a major upset, the operator may be confronted with a large number of signals and alarms, but very limited help from the system, concerning the underlying plant condition. Faced with vast amount of raw process data, human operators find it hard to make timely and effective solutions. The process industry demands new computer integrated technologies that reduce the operator’s working burden, by providing operation support systems. Process operations are knowledgeintensive work task because thermal plants are large, complex and influenced by unexpected disturbances and events over the time. Artificial Intelligence (AI) has been considered promising to deal with problems that require human expertise and heuristic knowledge. The Electrical Research Institute of Mexico (IIE), and the Monterrey Institute of Technology (ITESM) have been working in various projects related to operational support of thermal power plants, such as sensor validation [Ibargüengoytia, 98], diagnostic systems for complex operations [Arroyo, 99], operational planning systems [Ibargüengoytia, 01], and real time advisory control systems [Arroyo, 00]. Diagnosis and Prediction System We are developing an intelligent system, based on network of probabilistic events in discrete time, for fault diagnosis and prediction in thermal power plants. The framework is based on a novel methodology for dealing with uncertainty and time called Temporal Nodes Bayesian Networks [Arroyo, 99]. In a TNBN each variable represents an event that can take place at most Arroyo-Figueroa et al. 2 once. Time is discretized by adopting the appropriate temporal unit for each case (seconds, minutes, etc); therefore, the temporal granularity depends on the particular problem. Temporal reasoning is based on probability propagation and gives the time occurrence of the events or state changes with some probability value. The main difference with dynamic Bayesian networks, is that representation is based on state changes instead of state values at different times. This makes the model much simpler in many applications in which there are few changes for each variable in the temporal range of interest. The proposed network of probabilistic events in discrete time is applied for fault diagnosis and prediction of events in the drum level control system of a thermal power plant. The following figure shows the TNBN that represents the events and their prior probabilities in this application. FWPF Occur=058 ¬Occur=0.42 FWVF Occur=0.57 ¬Occur=0.43 SVWF Occur=0.18 ¬Occur=0.82 FWPF FWVF SWV True,[28-41]=0.30 True,[41-66]=0.27 False,[28-66]=0.43 FWP FWV SVWF FWF SWV SWF True,[108-170]=0.75 True,[170-232]=0.21 False,[108-232]=0.04 LI Occur=0.88 ¬Occur=0.12 FWP True,[10-29]=0.36 True,[29-107]=0.57 LI False,[10-107]=0.07 SWF FWF True,[25-114]=0.77 True,[114-248]=0.18 False,[25-248]=0.05 DRP True,[30-70]=0.58 True,[70-96]=0.40 False,[30-96]=0.02 STV STF DHL DRP STT STV True,[0-18]=0.69 True,[18-29]=0.20 False,[0-29]=0.11 STF True,[52-72]=0.36 True,[72-105]=0.57 False,[52-105]=0.07 DHL Increment,[10-27]=0.49 Increment,[27-135]=0.09 Decrement,[22-37]=0.28 Decrement,[37-44]=0.12 False,[10-135]=0.02 STT Decrement,[10-42]=0.37 Decrement,[42-100]=0.14 Decrement,[100-272]=0.47 False,[10-272]=0.02 STV = steam valve opening increase STF = steam flow increase SWV = spray valve opening increase SWF = spray water flow increase DRL = drum level (DRL) DRP = drum pressure decrement STT = steam temperature. Preliminary Results The TNBN model of the drum level control system has been implemented and evaluated using a power plant simulator. The following table shows the result of the evaluation of 972 registers. In each test some of the variables are instantiated, and the other estimated, considering 3 cases: diagnosis, the bottom nodes are known, prediction, the top nodes are known, diagnosis and prediction, bottom and top nodes are known. The model was evaluated empirically using two scores: accuracy and a measure based on the Brier score (total square error, RBS) [Arroyo 99], and the results are shown in the following table. In general, the results are better for prediction than for diagnosis. Arroyo-Figueroa et al. 3 Parameter Prediction % of RBS % of Accuracy 87.37 84.48 9.19 14.98 Diagnosis % of RBS % of Accuracy 84.25 80.00. 8.09 11.85 Diagnosis and Prediction % of RBS % of Accuracy 95.85 94.92 . 4.71 8.59 Future Work An important problem is the construction of this type of models. For this experiment, the model and the time intervals for each node were defined by a an expert, but this is a very time consuming process. So an issue for future work is how to learn the model from data, including the structure and temporal intervals. Another issue that has to be consider to use this model for helping the operator, is how to translate the probabilities obtained for each event in advice to the operator. References G. Arroyo-Figueroa., L. E. Sucar, (1999). “Temporal Bayesian Network for diagnosis and prediction”. In Laskey K., Prade H.: Proc. 15th Conference on Uncertainty on Arti-ficial Intelligence, 13-20. G. Arroyo-Figueroa., L. E. Sucar and A. Villavicencio, (2000). “Fuzzy Intelligent system for the operation of fossil power plants”, Engineering Applications of Artificial Intelligence, 13, 431439. P. Ibargüengoytia, L.E. Sucar, S. Vadera, (1998). “Any-time probabilistic reasoning for sensor validation”, In Proc. 14th Conference on Uncertainty in Artificial Intelligence, Madison, Morgan-Kaufmann, San Mateo, CA, 266-273. P. Ibarguengoytia and Alberto Reyes (2001). “Continuous Planning for the Operation of Power Plants”, In Proc. 3er Encuentro Nacional de Computación, Aguascalientes, Mexico, 199-208.