A Tutorial on Nonlinear Time-Series Data Mining in Engineering Asset Health and Reliability Prediction: Concepts, Models, and AlgorithmsReportar como inadecuado




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Mathematical Problems in EngineeringVolume 2010 2010, Article ID 175936, 22 pages

Review ArticleCollege of Economics and Management, Shanghai Jiao Tong University, 200052 Shanghai, China

Received 24 January 2010; Accepted 24 March 2010

Academic Editor: Ming Li

Copyright © 2010 Ming Dong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The primary objective of engineering asset management is to optimize assets service delivery potential and to minimize the related risks and costs over their entire life through the development and application of asset health and usage management in which the health and reliability prediction plays an important role. In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset is generally described as monitored nonlinear time-series data and subject to high levels of uncertainty and unpredictability. It has been proved that application of data mining techniques is very useful for extracting relevant features which can be used as parameters for assets diagnosis and prognosis. In this paper, a tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction is given. Besides that an overview on health and reliability prediction techniques for engineering assets is covered, this tutorial will focus on concepts, models, algorithms, and applications of hidden Markov models HMMs and hidden semi-Markov models HSMMs in engineering asset health prognosis, which are representatives of recent engineering asset health prediction techniques.





Autor: Ming Dong

Fuente: https://www.hindawi.com/



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