Asset health prediction using condition indicators and operating environment indicators - A case study

, , , , & (2011) Asset health prediction using condition indicators and operating environment indicators - A case study. In Rao, R B K N, Singh, M, & Liyanage, J P (Eds.) Proceedings of the 24th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2011): Advances in Industrial Asset Integrity Management. COMADEM International, United Kingdom, pp. 238-245.

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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.

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ID Code: 40334
Item Type: Chapter in Book, Report or Conference volume (Conference contribution)
ORCID iD:
Yarlagadda, Prasadorcid.org/0000-0002-7026-4795
Measurements or Duration: 8 pages
ISBN: 0-9541307-2-3
Pure ID: 32015531
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Past > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Copyright Owner: Copyright 2011 [please consult the authors]
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Deposited On: 27 Feb 2011 22:40
Last Modified: 04 Mar 2024 18:58