an approach of diagnosis based on the hidden markov chains model

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ID: 137234
2008
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Abstract
Diagnosis is a key element in industrial system maintenance process performance. A diagnosis tool is proposed allowing the maintenance operators capitalizing on the knowledge of their trade and subdividing it for better performance improvement and intervention effectiveness within the maintenance process service. The Tool is based on the Markov Chain Model and more precisely the Hidden Markov Chains (HMC) which has the system failures determination advantage, taking into account the causal relations, stochastic context modeling of their dynamics and providing a relevant diagnosis help by their ability of dubious information use. Since the FMEA method is a well adapted artificial intelligence field, the modeling with Markov Chains is carried out with its assistance. Recently, a dynamic programming recursive algorithm, called 'Viterbi Algorithm', is being used in the Hidden Markov Chains field. This algorithm provides as input to the HMC a set of system observed effects and generates at exit the various causes having caused the loss from one or several system functions.
Reference Key
bouamrane2008computeran Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Karim Bouamrane;Amel Djebbar;Baghdad Atmani
Journal integrative medicine research
Year 2008
DOI
DOI not found
URL
http://www.math.md/files/csjm/v16-n2/v16-n2-(pp256-268).pdf
Keywords
markov chain diagnosis computer science

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