AI-Enabled Anomaly Detection and Predictive Maintenance in Industrial Systems
Keywords:
AI-enabled anomaly detection; predictive maintenance; industrial systems; cyber-physical systems; edge computing; governance; sustainability; resilience; fairness; infrastructureAbstract
Industrial systems are increasingly expected to operate continuously, safely, and efficiently while facing volatile demand, aging assets, workforce constraints, and tightening environmental requirements. Artificial intelligence has emerged as a central enabler of anomaly detection and predictive maintenance, promising earlier fault identification, better remaining useful life estimation, and more adaptive maintenance scheduling. Yet the transition from laboratory models to dependable industrial infrastructure is not primarily a matter of algorithmic accuracy. It involves architectural choices across sensing, edge computing, cloud platforms, data governance, human workflows, and regulatory oversight. This paper examines AI-enabled anomaly detection and predictive maintenance from a system-level perspective. It discusses the conceptual foundations of anomaly detection, the architecture of condition-based and predictive maintenance, the trade-offs among model complexity, interpretability, latency, robustness, and cost, and the implications of deployment in edge-cloud environments. It further considers governance, fairness, sustainability, cybersecurity, and policy concerns that arise when maintenance decisions become partially automated. Through cross-domain comparisons in manufacturing, energy, and transportation, the paper argues that successful industrial AI depends on socio-technical integration rather than isolated model performance. The conclusion emphasizes resilient data infrastructures, explainable decision support, lifecycle governance, and sustainable deployment strategies as prerequisites for trustworthy and scalable predictive maintenance.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.