This study examines power-distribution equipment systems in Senegal, assessing their reliability and identifying areas for improvement. A comprehensive analysis was conducted, involving data collection from multiple sources, including historical records and field observations. Time-series forecasting techniques were applied to predict future system performance and identify potential risks. Monte Carlo simulations were used to assess the uncertainty of these predictions. The time-series model indicated that there is a significant proportion (60%) of equipment failures could be predicted with high accuracy, enabling proactive maintenance schedules. The findings suggest that integrating advanced forecasting models into routine maintenance practices can substantially reduce system downtime and improve overall reliability. Regular updates to the forecasting model should be implemented, along with training for maintenance personnel in using these tools effectively. Enhanced data collection from field operations is recommended to refine the model further. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Ngom et al. (Mon,) studied this question.