PREDICTIVE MAINTENANCE AND RELIABILITY-CENTRED ASSET MANAGEMENT FOR RESILIENT INDUSTRIAL INFRASTRUCTURE IN SAUDI ARABIA: A FRAMEWORK FOR TRANSFORMERS, SWITCHGEAR AND HIGH-VOLTAGE ELECTRICAL SYSTEMS

Industrial electricity networks depend on transformers, switchgear and associated high-voltage apparatus whose failures can propagate from individual defects to prolonged production interruptions. This critical review evaluates how predictive diagnostics can be coupled with reliability-centred asset management to strengthen infrastructure resilience in Saudi Arabia. Thirty DOI-traceable studies published during 2020–2025 were selected through a structured, purposive examination of transformer condition assessment, partial-discharge sensing, circuit-breaker diagnostics, maintenance optimisation and grid transition.Evidence was synthesised by failure mechanism, measurement maturity, decision relevance and transferability to industrial substations. Transformer literature offers comparatively mature dissolved-gas, thermal and health-index approaches, although diagnostic ambiguity and poorly calibrated remaining-life estimates persist. Switchgear research demonstrates promising acoustic, electromagnetic and data-driven detection but often relies on laboratory defects or sparsely labelled field observations. Across asset classes, prognostic accuracy alone does not establish maintenance value: network topology, safety consequences, outage windows, spares and recovery capability determine which intervention is justified. An integrated, human-governed framework is proposed linking condition evidence, uncertainty-aware failure-mode assessment, consequence modelling, constrained work planning and post-intervention learning.