Authors: Asmaa Boughrara; Belhadri Messabih
Addresses: Faculty of Mathematics and Computer Science, Department of Computer Science, University of Science and Technology of Oran-Mohamed Boudiaf (U.S.T.O), B.P El 1505-El M'Naouar, Oran, Algeria ' Faculty of Mathematics and Computer Science, Department of Computer Science, University of Science and Technology of Oran-Mohamed Boudiaf (U.S.T.O), B.P El 1505-El M'Naouar, Oran, Algeria
Abstract: The diagnosis of failures, if done properly and enabling early degradation detection, represents a means to optimise the production unit and to reduce the costs by avoiding failures. This challenge can be addressed through hidden Markov models (HMMs) that can estimate the probability of a future failure based on observation system. However, sudden changes in system behaviour due to either system malfunction or one of its components will affect the operation process. Thus, previous errors have an impact on the current system state and a regular HMM does not meet this requirement unlike partly hidden Markov models (PHMMs), which combines the power of conditioning the state transition probability to the previous observation. In this paper and for the first time, we propose to use PHMM as a mechanism to identify a future failure of industrial furnace. The obtained results prove that using PHMM seems to be particularly effective, efficient and outperforms the HMM.
Keywords: failure avoidance; HMM; PHMM; partly hidden Markov models; industrial furnaces; thermal signature; modelling; failure diagnosis.
International Journal of Computer Applications in Technology, 2014 Vol.49 No.3/4, pp.372 - 377
Available online: 05 Jun 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article