Utilizing Non-Contact GMR Sensors for Real-Time State Estimation of Aging Bulk Electric System Assets: A Strategy for Mitigating Failure Risks in Deteriorating Infrastructure

Abstract

This study investigates the persistent problem of limited real-time visibility into the condition of aging bulk electric system assets, where conventional inspection-based monitoring often fails to detect deterioration early enough to prevent costly outages and reliability losses. The purpose of the research was to examine whether utilizing non-contact giant magnetoresistance (GMR) sensors can improve real-time state estimation and mitigate failure risks in deteriorating infrastructure through a quantitative, cross-sectional, case-based design. Data were collected from 214 technically qualified respondents representing clouded enterprise-style utility and infrastructure management cases, including transmission utilities, substation operations units, asset management departments, maintenance contractors, and reliability/planning divisions. The study assessed key variables comprising non-contact GMR sensor utilization, real-time state estimation capability, failure signal detectability, failure-risk mitigation, predictive maintenance support, asset reliability and operational continuity, and infrastructure risk prioritization, using a 5-point Likert instrument with high overall reliability (Cronbach’s alpha = 0.87). The analysis plan combined descriptive statistics, reliability testing, Pearson correlation, simple regression, multiple regression, and ANOVA. Findings showed strong agreement across the major constructs, with mean scores of 4.18 for GMR sensor utilization, 4.24 for state estimation capability, 4.20 for failure-risk mitigation, and 4.22 for asset reliability and operational continuity. GMR sensor utilization had a strong positive relationship with real-time state estimation (r = 0.740, p < .001) and significantly predicted it (β = 0.63, t = 11.42, R² = 0.548, p < .001). Real-time state estimation significantly predicted failure-risk mitigation (β = 0.58, t = 9.87, R² = 0.472, p < .001), while the extended regression model explained 61.4% of the variance in failure-risk mitigation (Adjusted R² = 0.614, F = 68.31, p < .001). Critically deteriorating assets recorded the highest overall mean benefit (4.39), confirming that sensor-enabled monitoring becomes more valuable as asset aging severity increases. The study implies that non-contact GMR sensing can serve as a practical reliability-centered maintenance tool for prioritizing monitoring resources, strengthening predictive maintenance, and improving resilience in aging power infrastructure.