Abstract
{ "background": "Process-control systems are critical for industrial safety and efficiency in developing economies, yet rigorous, long-term evaluations of their operational reliability are scarce. This gap is particularly pronounced in regions with challenging infrastructural and environmental conditions.", "purpose and objectives": "This case study aims to methodologically evaluate the long-term reliability of industrial process-control systems using a quasi-experimental design. The primary objective is to quantify reliability degradation and identify key failure-mode influences under real-world operational stresses.", "methodology": "A longitudinal, quasi-experimental design was employed, comparing a treatment group of systems subjected to a structured maintenance intervention against a control group under conventional maintenance. System reliability was modelled as a function of operational time and environmental covariates using a Weibull proportional hazards model: $h(t|X) = \\frac{\\beta}{\\eta} \\left( \\frac{t}{\\eta} \\right)^{\\beta-1} \\exp(\\theta X)$. Inference was based on robust standard errors to account for clustered sampling.", "findings": "Systems in the intervention group demonstrated a 34% lower hazard rate of critical failure compared to the control group. The shape parameter $\\beta$ was estimated at 1.85 (95% CI: 1.62, 2.11), indicating a wear-out failure characteristic. Electrical subsystem faults were the predominant failure mode, accounting for over 60% of all downtime incidents.", "conclusion": "The study confirms that targeted, data-informed maintenance protocols can significantly enhance the long-term reliability of process-control systems in demanding operational environments. The quasi-experimental design proved effective for isolating the impact of specific interventions from confounding operational factors.", "recommendations": "Implement predictive maintenance schedules focused on electrical components. Adopt the demonstrated quasi-experimental framework for ongoing system performance monitoring. Integrate environmental covariate data directly into reliability models for asset management.", "key words": "process control, reliability engineering, quasi-experimental design, maintenance optimisation, industrial systems", "cont