MARKOV REPLACEMENT MODEL OF HYDROELECTRIC TURBINES: A MACHINE LEARNING APPROACH

ABSTRACT

This research explores the application of machine learning techniques to enhance the efficiency and reliability of dam turbine maintenance through the development of Markov Replacement Models for 6 number of turbines. Traditional approaches to turbine maintenance planning often rely on deterministic models that may not capture the dynamic and stochastic nature of turbine performance. In contrast, this study leverages machine learning algorithms to construct Markov Replacement Models and provides machine indicators to alert users on the state of the turbines using the data obtained from the transition states model for the turbines. The suggested approach makes use of past data on maintenance logs, failure trends, and turbine operating parameters. To glean insights and relationships from the data, supervised and unsupervised machine learning approaches are combined. The sequential nature of turbine states and the decision-making process for maintenance activities are then modeled by incorporating these insights into a Markov decision process framework. The approach proffers us optimum policies at each point of the iterations which converges at the 15th iteration. The machine indicator sends optimal prices and an alert based on the state of the machine. This advances Mohammed (2022) above the limitations spelled out in their work.