@article{moghaddass_integrated_2014,
	title = {An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process},
	volume = {124},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832013003037},
	doi = {10.1016/j.ress.2013.11.006},
	abstract = {Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.},
	language = {en},
	urldate = {2022-02-09},
	journal = {Reliability Engineering \& System Safety},
	author = {Moghaddass, Ramin and Zuo, Ming J.},
	month = apr,
	year = {2014},
	keywords = {Condition monitoring, Model selection, Multistate degradation process, Online diagnostics and prognostics, Reliability analysis, ecml},
	pages = {92--104},
}

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Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.","language":"en","urldate":"2022-02-09","journal":"Reliability Engineering & System Safety","author":[{"propositions":[],"lastnames":["Moghaddass"],"firstnames":["Ramin"],"suffixes":[]},{"propositions":[],"lastnames":["Zuo"],"firstnames":["Ming","J."],"suffixes":[]}],"month":"April","year":"2014","keywords":"Condition monitoring, Model selection, Multistate degradation process, Online diagnostics and prognostics, Reliability analysis, ecml","pages":"92–104","bibtex":"@article{moghaddass_integrated_2014,\n\ttitle = {An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process},\n\tvolume = {124},\n\tissn = {0951-8320},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0951832013003037},\n\tdoi = {10.1016/j.ress.2013.11.006},\n\tabstract = {Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. 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