Online health management for complex nonlinear systems based on hidden semi-Markov model using sequential Monte Carlo methods
type: Item
instance of: scholarly article
title: Online health management for complex nonlinear systems based on hidden semi-Markov model using sequential Monte Carlo methods
zbMATH Open document ID: 1264.90112
DOI: 10.1155/2012/951584
author: Qinming Liu
author: Ming Dong
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM) with sequential Monte Carlo (SMC) methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems.
Mathematics Subject Classification ID: 90B90
Mathematics Subject Classification ID: 90B25
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 6173721
zbMATH Keywords: complex nonlinear system
zbMATH Keywords: online health management
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/951584
OpenAlex ID: W2013359620
Wikidata QID: Q58912749
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description: scientific article; zbMATH DE number 6173721