Context-Driven Framework for Maintenance Decision Optimization in a Power Network

In the power domain, effective maintenance decision-making is crucial for asset reliability, availability, and maintainability (RAM). This paper proposes a framework that integrates existing practices with data-driven methods, considering factors like maintenance maturity and business case justification. It navigates two strategies: business pull, that aligns with market needs, and technology push, that focuses on data-driven predictive methods to optimize maintenance planning by early failure detection. Combining business pull and technology push aligns solutions with market needs while leveraging innovations to create new opportunities. This integration helps organizations optimize operations, enhance decision-making, and stay competitive while meeting RAM metrics. An example application is detailed in separate work.