Transformer risk modelling by stochastic augmentation of reliability-centred maintenance

• Applies the profile of repair cost vs. MTTFF as a metric (KPI) of RCM programs.
• It is shown that the repair cost is inversely proportional to the MTTFF.
• Method of moments introduces failure probability comparative model to RCM approach.
• Markov process gives failure probabilities with limited data, unlike current models.
• The Markov derives complement of uptime-steady-state probability as input for FMECA.

This paper reviews the literature and then applies power transformer case studies to develop a risk assessment model that augments the probabilistic capabilities of Reliability-centred maintenance (RCM). It is usually very difficult to assess the effectiveness of the RCM at its inception, when data is inadequate or when it is implemented on a large population of assets, especially when most of them are small in size as in the distribution systems. Thus, the major contribution of the study rests on applying a key performance indicator (KPI) for assessing the effectiveness of the RCM programmes, obtained by trending the profile of the mean-time-to-first-failure (MTTFF) and the average annual repair costs. The MTTFF, determined using Markov analysis, is inversely proportional to the costs. Besides, the method of moments is applied to statistical, historical data to generate a failure probability distribution comparative model, which lacks in the current practices for conducting a failure mode effect and criticality analysis (FMECA). Finally, the Markov derives complement of uptime-steady-state probability as input for FMECA, using limited data; which is an improvement on the current approaches. The overall approach developed offers a cost effective risk-priority-screening model for transformers, which can be applied prior to rigorous testing and inspection procedures on individual items during the RCM application.