Modelling physical asset risk profile using systems thinking augmented by stochastic and probabilistic inferences.
| dc.contributor.advisor | Saha, Akshay Kumar. | |
| dc.contributor.advisor | Ijumba, Nelson Mutatina. | |
| dc.contributor.author | Mkandawire, Burnet O'Brien. | |
| dc.date.accessioned | 2015-11-20T07:09:53Z | |
| dc.date.available | 2015-11-20T07:09:53Z | |
| dc.date.created | 2015 | |
| dc.date.issued | 2015 | |
| dc.description | Ph. D. University of KwaZulu-Natal, Durban 2015. | en |
| dc.description.abstract | Current quantitative approaches to power asset management risk modelling have focused on financial aspects such as net present value. These approaches can neither determine nor trend the impact of technologies or renewal strategies on failure risk. As a result of this, combined with the fact that benefits of renewal strategies are hard to determine as renewal does not add additional capacity that is needed for revenue generation, the value of the strategies is not appreciated. In addition, it is currently difficult to measure the effectiveness of risk assessment activities in Reliability-centered maintenance (RCM) programs when the number of equipment is large and not adequate data is available. Thus, the main objective of this research was to develop a failure risk trend monitoring model and to improve performance measurement in the RCM activities. This could be useful for the management of power infrastructure assets such as transformers. The risk trending model was developed by integrating systems thinking and system dynamics concepts with Markov processes, Weibull distribution and bathtub curve analysis to produce a quantitative measure of risk, called the risk factor. A set of 12 MVA substation transformer failure data was applied to compute the maximum likelihood estimates (MLE) of the Weibull parameters which were fitted into the risk factor, which was in turn trended with respect to changes in the number of components renewed during the asset life cycle. The risk trending model quantitatively determined the impacts of the renewal strategies on the transformer failure risk profile which can be used to provide strategic direction to asset managers regarding the most appropriate timing of renewal strategies to maximize financial benefits. Besides, the Markov analysis was applied to trend the profile of mean-time-to-first-failure (MTTFF) and average annual repair costs which was used as a measure of the effectiveness of the RCM programs. It was shown that the MTTFF is inversely proportional to the annual repair costs. Furthermore, the systems approach revealed that: the best and sustainable metrics are those that indicate the loss margin and the run-to-failure strategy is a quick-fix, but very unsustainable in the long run. The model developed can be used in risk assessment and in planning and development of asset management strategies in power utilities and in physical asset management firms in general. | en |
| dc.identifier.uri | http://hdl.handle.net/10413/12600 | |
| dc.language.iso | en_ZA | en |
| dc.subject | Reliability (Engineering) | en |
| dc.subject | Maintainability (Engineering) | en |
| dc.subject | Markov processes. | en |
| dc.subject | Theses--Electrical engineering. | en |
| dc.subject | Power asset management. | en |
| dc.title | Modelling physical asset risk profile using systems thinking augmented by stochastic and probabilistic inferences. | en |
| dc.type | Thesis | en |
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