NDLI: Modelling imperfect inspection and maintenance in defence aviation through bayesian analysis of the KIJIMA type I general renewal process (GRP)

Content Provider	IEEE Xplore Digital Library
Author	Jacopino, A. Groen, F. Mosleh, A.
Copyright Year	2006
Description	Author affiliation: Dept. of Defence, Defence Materiel Organ., Canberra, ACT (Jacopino, A.)
Abstract	To ensure the effective and efficient operation of Defence aviation equipment there is a clear need for a component life model that is representative of the true life of a component. However, the large and often sophisticated RAM models used to manage defence aviation platforms, through various engineering and logistics activities, use models that cannot accurately represent this life. The main difference is in the underlying repair assumption. Specifically, the Ordinary Renewal Process (ORP) uses an as-good-as-new repair assumption while the Non-Homogenous Poisson Process (NHPP) uses an as-bad-as-old repair assumption. However, it is highly unlikely that any component, typically referred to as a Repairable Item (RI), will readily fit into either repair assumption. Therefore, despite the best endeavours of both engineering and logistics staff given the underlying repair assumption and the limitation these impose on the model, any solution will be suboptimal. Accordingly, there is a need for a RI life model that can contend with imperfect maintenance, imperfect inspection and can adapt to the limitations in data and include a number of additional factors including aging of the component, number of repairs, effectiveness of the repair, skill of the technicians, etc. Eight cases were developed as part of the overall modelling scheme. These eight cases are further divided into 2 main types; the first type representing cases where failure times are known and the second type where failure times are unknown. The cases incrementally modify these types through the addition of factors including multiple failure modes and their inter-dependence, and imperfect inspection and maintenance, in order to achieve a more realistic representation. Each of these cases were then solved using utilising a Markov Chain Monte Carlo (MCMC) sampling procedure, concentrating only on the analysis of the KIJIMA Type I GRP model with an underlying Weibull Time-To- Failure (TTF) distribution. The MCMC was made possible through the use of a Slice Sampling and Auxiliary Variable techniques. The resulting models have the ability to accurately model, and specifically predict, the future failure trends. Furthermore, the model allows the analyst to compare the maintenance effectiveness either in isolation, or in comparison (benchmarking) of various maintenance activities/facilities
Starting Page	470
Ending Page	475
File Size	226930
Page Count	6
File Format	PDF
ISBN	1424400074
ISSN	0149144X
e-ISBN	1424400082
DOI	10.1109/RAMS.2006.1677418
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Inspection Bayesian methods Sampling methods Predictive models Logistics Engineering management Aircraft propulsion Cost function Australia Aging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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