Asset health prediction using condition indicators and operating environment indicators : a case study
Gorjian, Nima, Mittinty, Murthy, Sun, Yong, Yarlagadda, Prasad K.D.V., & Ma, Lin (2011) Asset health prediction using condition indicators and operating environment indicators : a case study. In Proceedings of 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, COMADEM International, Clarion Hotel, Stavanger, Norway. (In Press)
Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Hazard, Residual life, Condition indicator, Operating environment indicator, Explicit hazard model, Weibull model, Aalen linear regression model|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Infrastructure Engineering and Asset Management (090505)|
|Divisions:||Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)|
Current > Schools > School of Curriculum
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2011 [please consult the authors]|
|Deposited On:||28 Feb 2011 08:40|
|Last Modified:||25 Mar 2013 18:11|
Repository Staff Only: item control page