Physics-Informed Diagnostics for Energy Economics Crossover Assets in Kenya: Out-of-Distribution Fault Detection and Lifecycle Cost Trade-offs

Energy assets in Kenya increasingly operate at the intersection of physical infrastructure and economic optimisation, yet conventional diagnostic frameworks treat these domains separately. The analysis argues that purely data-driven fault detection methods fail when operational conditions shift beyond training distributions, while purely physics-based models cannot capture the economic incentives that shape maintenance decisions. By integrating governing physical equations with economic objective functions, the proposed framework enables early fault identification while making explicit the cost implications of diagnostic timing. The article formalises the out-of-distribution problem, examines how physics-based priors constrain the hypothesis space, and develops a lifecycle cost model that treats diagnostic sensitivity as an economic decision variable rather than a fixed technical parameter. The framework is situated within Kenya's evolving energy landscape, where distributed generation, grid interconnections and private investment create conditions that amplify the consequences of undetected faults. The contribution is a diagnostic architecture that aligns engineering reliability with economic efficiency, offering a basis for future empirical validation.