Deep Learning-Based Predictive Maintenance Framework for Industrial Equipment Health Monitoring
Keywords:
Predictive Maintenance, Deep Learning, Industrial Internet of Things, System Architecture, Data Governance, Robustness, Fairness, Sustainability, Socio‑Technical SystemsAbstract
The increasing complexity and inter‑connectivity of industrial equipment have elevated predictive maintenance from a cost‑saving tactic to a strategic imperative for operational continuity, safety, and sustainability. This paper presents a comprehensive deep learning‑based predictive maintenance framework designed for industrial equipment health monitoring. The framework integrates data acquisition architectures, deep learning model selection, deployment infrastructure, and governance mechanisms within a unified system‑level perspective. Emphasis is placed on structural trade‑offs between model accuracy and computational efficiency, the governance of heterogeneous sensor data streams, and the policy implications of algorithmically driven maintenance scheduling. The discussion extends to robustness against data drift and adversarial perturbations, fairness in maintenance resource allocation across different asset classes, and sustainability concerns regarding energy consumption of large‑scale neural networks. A cross‑domain comparison illustrates how the framework can be adapted from rotating machinery to static infrastructure, revealing domain‑specific constraints that shape architectural decisions. The paper argues that the effective deployment of deep learning for predictive maintenance requires not only technical innovation but also careful consideration of socio‑technical factors such as organizational readiness, regulatory compliance, and lifecycle management of learned models. By framing predictive maintenance as a socio‑technical system, this work provides researchers and practitioners with a holistic reference for designing, evaluating, and governing deep learning‑based health monitoring solutions in industrial settings.
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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.
