Tran, Van Tung (2010) Development of intelligent techniques for machine prognostics. In: International Symposium on Advanced Mechanical and Power Engineering, 11-13 Nov. 2010, Fukui, Japan.
- Accepted Version
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||T Technology > TJ Mechanical engineering and machinery|
|Schools:||School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies
|Depositing User:||Van Tran|
|Date Deposited:||07 Feb 2013 13:56|
|Last Modified:||30 Nov 2016 17:46|
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