Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation
Kim, Hack-Eun, Hwang, Sung-Soo, Tan, Andy C. C., Mathew, Joseph, & Choi, Byeong-Keun (2012) Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation. Journal of Mechanical Science and Technology, 26(11), pp. 3571-3585.
Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.
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|Item Type:||Journal Article|
|Keywords:||Diagnostics, Prognostics, Remaining useful life (RUL), High pressure LNG pump|
|ISSN:||1976-3824 (online) 1738-494X (print)|
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Institutes > Institute of Health and Biomedical Innovation
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 KSME & Springer|
|Copyright Statement:||Author's Pre-print: author can archive pre-print (ie pre-refereeing)
Author's Post-print: author can archive post-print (ie final draft post-refereeing)
Publisher's Version/PDF: author cannot archive publisher's version/PDF
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|Deposited On:||29 Apr 2014 23:44|
|Last Modified:||29 Apr 2014 23:44|
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