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

• Published in: Mathematical Problems in Engineering Vol. 2010; no. 1; pp. 507 - 528-028
• Main Author: Dong, Ming
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limiteds 01.01.2010; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Algorithms; Asset management; Behavior; Confidence intervals; Data mining; Data processing; Decision making; Engineering; Environmental management; Fault diagnosis; Fourier transforms; Health; Inventory control; Management; Markov chains; Mathematical models; Methods; Nonlinearity; Parameter uncertainty; Performance prediction; Prognosis; Reliability; Studies; Time series; Variables
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2010/175936

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.