Development of intelligent techniques for machine prognostics

Van Tung Tran, Bo-Suk Yang

Research output: Contribution to conferencePaperpeer-review

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The prognostic system plays a crucial role in estimating the remaining useful life of machine components and forecasting of the future states of machines. The techniques related to prognostics consist of statistical-based, model-based, and data driven or intelligence-based. Among these, artificial intelligence is commonly used due to its flexibility in generating appropriate models for the forecasting purpose. This paper presents the development of intelligent techniques for machine health prognostic system in Intelligent Mechanics Laboratory (IML) of Pukyong National University (PKNU), South Korea. These developed techniques include support vector machine, relevance vector machine, Dempster-Shafer theory, decision tree, neuro-fuzzy inference systems. Additionally, they are also combined with other model-based techniques such as autoregressive moving average, proportional hazard model, logistic regression, etc. to fulfill the final goal of prognostic system. Case studies of machine health prognostics are also presented in this paper to show the plausibility of the developed systems.
Original languageEnglish
Number of pages7
Publication statusPublished - 2010
Externally publishedYes
EventInternational Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2010 - University of Fukui, Fukui, Japan
Duration: 11 Nov 201014 Nov 2010


ConferenceInternational Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2010
Abbreviated titleISAMPE


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