Recent Advances in Industrial Sensor Reliability Modeling: A Systematic Literature Review for Risk-Aware Decision-Making Under Global Supply Chain Disruptions

Abstract

Industrial sensors support process monitoring, safety assurance, and maintenance decision-making, yet sensor degradation remains insufficiently integrated with uncertainty, supply-chain constraints, and operational resilience. This systematic literature review synthesizes recent advances in physics-based, data-driven, and hybrid reliability modeling of industrial sensors for risk-aware decision-making amid global supply chain disruptions. Following PRISMA 2020, searches of Scopus and IEEE Xplore, complemented by supplementary searches, yielded 87 studies published during 2021–2026. To distinguish sensor reliability from the reliability of assets monitored using sensor data, the corpus was post-inclusion stratified into direct sensor-degradation and sensor-reliability evidence; hybrid sensor–system studies; sensor-monitored asset prognostics; and general prognostics or decision-context evidence. The findings show increasing methodological convergence, but links among sensor degradation, uncertainty quantification, maintenance decisions, spare-part logistics, and resilience outcomes remain fragmented. Accordingly, this review proposes an integrated reliability–resilience framework connecting degradation mechanisms, probabilistic sensor and asset remaining useful life, risk-aware maintenance, spare-part availability, logistics lead time, maintenance delay, downtime-cost exposure, and resilience retention. A Piping and Instrumentation Diagram (P&ID)-based case involving the Xylene Recovery Column, along with an illustrative SIF-1 analysis, demonstrates the framework’s operational implications. Under the stated assumptions, replacement unavailability increases from 0.11% under normal conditions to 28.96% and 92.88% under moderate and severe disruptions, respectively. These results are sensitivity-analysis outputs rather than plant-calibrated predictions. Overall, the review positions sensor degradation as a metrological source of uncertainty whose operational consequences may be amplified by supply-chain constraints.