"background": "Process-control systems are critical for industrial and infrastructure performance, yet their long-term reliability in developing economies is under-researched. There is a lack of robust methodological frameworks for assessing these systems' degradation and failure modes over extended periods in such contexts. ", "purpose and objectives": "This study aims to develop and apply a novel panel-data econometric methodology to evaluate the reliability of industrial process-control systems. The objective is to quantify the impact of operational stressors and maintenance regimes on system failure rates. ", "methodology": "A longitudinal dataset of performance metrics from multiple industrial sites was constructed. Reliability was modelled using a generalised estimating equations (GEE) approach with a logit link function for binary failure events. The core model is \ (P (Y{it=1) ) = \0 + \1 Xit + \2 Zi + \ +, where Xit are time-varying covariates and Zi are site-specific effects. Inference was based on robust standard errors clustered by site. ", "findings": "The analysis indicates that inadequate calibration intervals are a significant predictor of control-loop failure. A one-standard-deviation increase in the time between calibrations was associated with a 34% increase in the odds of failure (95% CI: 22% to 48%). Environmental factors, particularly dust ingress, were also strongly correlated with reduced reliability. ", "conclusion": "The proposed panel-data method provides a rigorous tool for diagnosing reliability drivers in process-control systems. The findings demonstrate that scheduled maintenance quality, not just frequency, is paramount for sustained operational integrity. ", "recommendations": "Industrial operators should prioritise condition-based calibration protocols over fixed schedules. Further integration of environmental hardening for control hardware is advised. The methodological framework should be validated in other regional contexts. ", "key words": "process control, system reliability, panel data, generalised estimating equations, maintenance engineering, industrial systems", "contribution statement":
Uwimana et al. (Tue,) studied this question.