A Smart RCM Implementation

Abstract

This project aims to implement Reliability Centred Maintenance (RCM) in a smart and effective manner by integrating Dynamic Bayesian Network (DBN) modelling and prediction techniques. By analysing historical data and utilizing machine learning algorithms, a DBN model is trained to predict future system behaviour and estimate the likelihood of different states and outcomes. These predictions are then incorporated into the maintenance strategy to enable proactive measures, such as preventing failures, optimizing repair schedules, and minimizing downtime. The objective is to develop an advanced maintenance strategy that acknowledges the multi-state nature of systems and leverages predictive analytics for improved decision-making. The project will demonstrate the effectiveness of this approach through two case studies conducted in different industries.