an approach of diagnosis based on the hidden markov chains model

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ID: 137234
2008
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摘要
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.
参考键
bouamrane2008computeran 使用此键在稿件中自动引用，同时使用 SciMatic 稿件管理器或论文管理器
作者 ;Karim Bouamrane;Amel Djebbar;Baghdad Atmani
期刊 integrative medicine research
年份 2008
DOI
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URL
http://www.math.md/files/csjm/v16-n2/v16-n2-(pp256-268).pdf
关键词
markov chain diagnosis computer science

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