Intelligent Condition Monitoring for Mission-Critical Electrical Infrastructure: Integrating Sensors, Data Analytics, and Predictive Diagnostics

Abstract

Mission-critical electrical infrastructure increasingly requires maintenance strategies capable of identifying degradation before it progresses into disruptive or catastrophic failure. This review examines the role of intelligent condition monitoring in strengthening asset reliability through the coordinated application of sensor technologies, data analytics, and predictive diagnostic methods. A structured narrative review approach was adopted to synthesise relevant engineering literature on electrical asset failure mechanisms, sensing modalities, condition assessment, prognostics, digital monitoring architectures, and implementation challenges. The review considered transformers, substations, rotating machinery, renewable-energy systems, protection infrastructure, and associated cyber-physical environments.

The findings show that effective condition monitoring depends on integrating complementary electrical, thermal, vibration, acoustic, optical, and environmental measurements with analytical methods capable of extracting meaningful health indicators. Statistical analysis, anomaly detection, health-index modelling, machine learning, digital twins, and remaining useful life estimation substantially improve the ability to distinguish transient operating variations from progressive equipment deterioration. Integrated SCADA, Industrial Internet of Things, edge-computing, and secure communication architectures further enhance real-time visibility and support maintenance decisions across distributed assets. However, implementation remains constrained by data quality, interoperability limitations, legacy-system compatibility, cybersecurity exposure, model explainability, workforce capability, and insufficient field validation.

The review concludes that intelligent monitoring is most effective when sensing, analytics, diagnostics, cybersecurity, and maintenance governance operate as an integrated decision-support ecosystem rather than as isolated technologies. It recommends greater emphasis on interoperable architectures, uncertainty-aware prognostics, explainable artificial intelligence, secure edge analytics, standardised condition data, and multi-site validation under diverse operating environments. These priorities can support earlier intervention, reduce unplanned outages, improve asset utilisation, and strengthen the resilience and lifecycle performance of critical electrical systems.