PROJECT TITLE :

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

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 bath curve analysis to develop a quantitative risk trending model. A risk issue (RF) is employed to define a quantitative live of risk. A group of failure information is applied to compute the maximum chance estimates of Weibull parameters that are fitted into the RF. MATLAB algorithms are used to simulate sensitivities of the RF to changes in the quantity of components renewed and the amount admitted to or relieved from a high-operating-load regime during the life cycle. The model determines the impacts of renewal methods on failure risk by trending risk profiles related to these sensitivities. Additionally, it provides modeling equations for the systems thinking approach that has, traditionally, used qualitative models. Furthermore, it's versatile since the computed parameters are unique to the set of knowledge. These parameters therefore generate equally unique plots of likelihood distribution functions that are required for analysis of reliability and risk. This is primarily intended to be employed in risk management, but it will conjointly be applied in performance-primarily based compensation schemes for workers.