Elevating Performance with Dynamic Bayesian Networks: A Novel Framework for RCM Implementation

The paper introduces a novel framework for implementing Reliability-Centered Maintenance (RCM), exhibited through a case study in a steel industry. This paradigm seamlessly blends the Dynamic Bayesian Networks (DBNs) approach with the traditional RCM methodology while incorporating data mining modalities. It lays the foundation for a dynamic RCM program adept at addressing a diverse range of tasks, including failure diagnostics, reasoning, causal modeling, decision-making under uncertainty, automated insights, and predicting the behavior of assets. The methods and tools offered are meticulously crafted to accommodate various reliability requirements, facilitating continuous enhancement of the reliability of complex systems. Based on performance monitoring, this framework proactively prevents or mitigates failures and optimizes maintenance planning and scheduling, ultimately boosting plant wrench time.