<![CDATA[Ensuring the operational integrity of hydropower infrastructure is critical for maintaining energy security and grid stability in Nigeria. However, conventional predictive maintenance frameworks are hindered by inconsistent data availability, poor sensor coverage, and a lack of physical interpretability. This study presents a robust Physics-Informed Neural Network (PINN) architecture tailored for predictive maintenance in Nigerian hydropower systems. By embedding domain-specific physical laws—namely Bernoulli’s principle, the turbine power equation, and Fourier’s law of heat conduction—directly into the model’s loss function, the proposed PINN integrates physical reasoning with deep learning to produce accurate and explainable degradation forecasts. Simulated operational data reflective of real-world hydropower conditions were used to train and evaluate the model. Comparative analysis against Long Short-Term Memory (LSTM) networks and Random Forest (RF) regressors demonstrated the superior performance of the PINN, which achieved an RMSE of 4.75 days and an R² value of 0.88. Furthermore, physics residuals across all governing constraints were consistently below 0.04, indicating strong physical consistency. The model accurately predicted failure in three fault scenarios—runner blade erosion, stator insulation decay, and penstock pressure surges—with lead times ranging from 7.5 to 11 days, thereby enabling actionable intervention before catastrophic breakdown. A real-time monitoring interface was developed to visualize model outputs, risk thresholds, and residual dynamics, facilitating operator trust and integration into existing maintenance workflows. This research establishes the PINN as a scalable and domain-aware solution, well-suited for advancing predictive maintenance capabilities in Nigeria’s evolving hydropower infrastructure.]]>
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