Data-Enabled Fault Detection and Reliability Assessment in Modern Engineering Infrastructure

Authors

  • Keepak Heokhale Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
  • Massimo D. Larsen Department of Computer Science, University of Central Florida, Orlando, FL, USA.
  • Zhengzihan Chen Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
  • Arthur Bearnett Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.

Keywords:

Data-driven fault detection, reliability engineering, infrastructure systems, system resilience, condition monitoring, socio-technical governance

Abstract

The accelerating digitalization of critical engineering infrastructure has created an unprecedented opportunity to embed data-enabled fault detection and reliability assessment into the operational fabric of large-scale systems. Moving beyond traditional periodic inspection and rule-based alarms, modern infrastructure increasingly relies on streaming sensor data, machine learning models, and networked decision-support platforms to anticipate degradation, localize anomalies, and optimize maintenance in near real time. This paper provides a system-level examination of the architectural, governance, and policy dimensions that determine whether such data-driven capabilities can be sustained, scaled, and trusted. We argue that the migration from physics-based models to hybrid and data-intensive paradigms fundamentally reshapes the structural trade-offs between specificity and generalizability, between centralized cloud analytics and edge autonomy, and between model accuracy and explainability. The discussion integrates perspectives from reliability engineering, cyber-physical systems, machine learning lifecycles, and sociotechnical governance to identify the points of friction that emerge when algorithmic fault detection meets regulatory frameworks originally designed for electromechanical assets. Special emphasis is placed on deployment robustness under nonstationary operating conditions, fairness in predictive maintenance decisions that affect multiple user communities, environmental sustainability of pervasive sensing, and the institutional adaptations required for shared data infrastructures. By synthesizing cross-domain case illustrations and prior frameworks, the paper proposes a forward-looking research agenda that treats reliability assessment not as a stand-alone technical computation but as an infrastructure-wide governance challenge requiring new standards, lifecycle accountability mechanisms, and inclusive policy designs. The analysis concludes that sustainable data-enabled infrastructure reliability depends as much on organizational alignment and adaptive regulation as on sensor density or algorithmic sophistication.

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Published

2026-03-15

Issue

Vol. 1 No. 1 (2026): Journal of Engineering Systems and Digital Innovation

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Articles

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