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.
Index Terms—Aging, assetmanagement (AM),Malawi, Markov
inferences, maximum likelihood estimation (MLE), power transformers,
reliability analysis, risk trending, South Africa, system
education, systems thinking,Weibull inferences.