Abstract
{ "background": "Process-control systems are critical for industrial and infrastructure projects, yet their long-term reliability in challenging operational environments is under-researched. Previous studies in the region have often relied on cross-sectional data, limiting the analysis of performance degradation over time.", "purpose and objectives": "This study aims to replicate and extend a foundational reliability assessment by applying a panel-data econometric framework. The objective is to quantify the temporal dynamics of system failure rates and identify key engineering and operational determinants of reliability.", "methodology": "We utilise a uniquely compiled longitudinal dataset of system performance metrics from multiple industrial sites. Reliability is modelled using a fixed-effects panel regression: $\\lambda{it} = \\alphai + \\beta X{it} + \\epsilon{it}$, where $\\lambda{it}$ is the failure rate for system $i$ at time $t$, $\\alphai$ captures unobserved system heterogeneity, and $X_{it}$ is a vector of time-varying covariates. Inference is based on cluster-robust standard errors.", "findings": "The analysis reveals a significant positive association between ambient particulate concentration and control-system fault incidence, with a one standard deviation increase correlating with an estimated 18% rise in the monthly failure rate (95% CI: 12% to 24%). Redundancy configurations showed non-linear protective effects.", "conclusion": "The panel-data approach confirms and refines earlier findings, demonstrating that environmental stressors are a more persistent driver of reliability loss than previously quantified. Unobserved site-specific factors account for substantial variation.", "recommendations": "Future system design for similar contexts must prioritise enhanced environmental hardening. Reliability assessments should adopt longitudinal data strategies to better inform maintenance scheduling and lifecycle costing.", "key words": "system reliability, panel data, process control, fixed-effects model, industrial engineering", "contribution statement": "This study provides the first longitudinal, multi-site empirical model for control-system reliability in its context, generating a novel dataset and demonstrating the value of