Accident identi®cation in nuclear power plants using hidden Markov models
نویسندگان
چکیده
The identi®cation of the type of accident during the early stages of an accident in a nuclear power plant is crucial for the selection of the appropriate response actions. A plant accident can be identi®ed by its time-dependent patterns, related to the principal variables. The Hidden Markov Model (HMM) can be applied to accident identi®cation, which is a spatial and temporal pattern-recognition problem. The HMM is created for each accident from a set of training data by the maximumlikelihood estimation method, which uses an algorithm that employs both forward and backward chaining, and a Baum±Welch re-estimation algorithm. The accident identi®cation is decided by calculating which model has the highest probability for the given test data. The optimal path for each model at the given observation is found by the Viterbi algorithm, and the probability of the optimal path is then calculated. The system uses a left-to-right HMM, including six states and 22 input variables, to classify eight types of accidents and a normal state. The simulation results show that the proposed system identi®es the accident types correctly. It is also shown that the identi®cation is performed well for incomplete input observations caused by sensor faults or by the malfunctioning of certain equipment. # 1999 Elsevier Science Ltd. All rights reserved.
منابع مشابه
Language identi cation of web documents using discrete HMMs
Automatic language identi cation in written text documents is an issue which deserves signi cant attention in the context of the ever-growing volume of web documents. This paper deals with language identi cation in the domain of electronic texts related to tourism. The proposed system is built on Hidden Markov Models (HMMs) that enable the modeling of character sequences. For this purpose, a pa...
متن کاملالگوهای پراکندگی مواد پرتوزای رهاشده در اثر وقوع سوانح در نیروگاه های هسته ای اطراف ایران
Released radioactive materials from a nuclear power plant due to an accident can be transported to far regions by wind. Some models are available to predict the dispersion. This article reports some parts of the results of a research in which using one of the models, named HYSPLIT, to predict the dispersion of some isotopes of iodine and cesium released from ten nuclear power plant to the atmos...
متن کاملOn - Line Identi cation of Hidden Markov Models via RecursivePrediction Error Techniques 1 IAIN
متن کامل
Face Segmentation For Identi cation Using Hidden Markov Models
This paper details work done on face processing using a novel approach involving Hidden Markov Models. Experimental results from earlier work [14 ] indicated that left-to-right models with use of structural information yield better feature extraction than ergodic models. This paper illustrates how these hybrid models can be used to extract facial bands and automatically segment a face image int...
متن کاملSECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کامل