Umudike Journal of Engineering and Technology

The primary aim of this study is the development of an optimal maintenance policy for industrial equipment using carbo-cooler equipment as a case study. The study predicted the future condition state of the equipment and thereby reducing unscheduled downtime. The Markov Chain methodology was used for prediction of future states and quantification of changes in condition over time. The current limited availability and inconsistency of performance assessment data, required to populate the Markovian transitional probability matrix, however, limits the application of the Markovian model. The matrices were populated through the model results in uncertainties in predicted performance and cost results. It illustrated that the limited availability of past performance data on downtime of the equipment could be supplemented with expert knowledge, translated into likelihood values, to develop transition probability matrices for the Markov Chain. The results of the status state extend the useful life cycle of equipment and decreasing the need for capital replacements. The result reduced the total operating cost of the equipment. The result concludes the development of an optimal maintenance policy to curb the excessive downtime caused by the carbo-cooler equipment by proffering that the carbo-cooler equipment should be overhauled every six months which implies twice in a year as against the annual overhauling of the carbo-cooler being practised presently in the company.