NDLI: Component Risk Trending Based on Systems Thinking Incorporating Markov and Weibull Inferences

Content Provider	IEEE Xplore Digital Library
Author	Mkandawire, B.O. Ijumba, N.M. Saha, A.K.
Copyright Year	2007
Abstract	This paper uses systems thinking to present a power utility asset management system as a system in spatial transition. Next, it integrates inferences from Markov processes, the Weibull distribution, and the bathtub curve analysis to develop a quantitative risk trending model. A risk factor (RF) is used to define a quantitative measure of risk. A set of failure data is applied to compute the maximum likelihood estimates of Weibull parameters that are fitted into the RF. MATLAB algorithms are used to simulate sensitivities of the RF to changes in the number of components renewed and the number admitted to or relieved from a high-operating-load regime during the life cycle. The model determines the impacts of renewal strategies on failure risk by trending risk profiles associated with these sensitivities. In addition, it provides modeling equations for the systems thinking approach which has, traditionally, used qualitative models. Furthermore, it is flexible since the computed parameters are unique to the set of data. These parameters thus generate equally unique plots of probability distribution functions that are required for analysis of reliability and risk. This is primarily intended to be used in risk management, but it can also be applied in performance-based compensation schemes for workers.
Sponsorship	IEEE Systems Council
Starting Page	1185
Ending Page	1196
Page Count	12
File Size	2189985
File Format	PDF
ISSN	19328184
Volume Number	9
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov processes Mathematical model Maintenance engineering Aging Maximum likelihood estimation Adaptation models Weibull inferences asset management (AM) Malawi Markov inferences maximum likelihood estimation (MLE) power transformers reliability analysis risk trending South Africa system education systems thinking
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Control and Systems Engineering Information Systems Electrical and Electronic Engineering Computer Science Applications

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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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