Predictive Maintenance in Smart Industrial Environments Using Machine Learning
Keywords:
predictive maintenance, machine learning, smart manufacturing, cyber-physical systems, industrial IoT, system architecture, sustainability, fairness, governance, Industry 4.0Abstract
Predictive maintenance has emerged as a cornerstone of smart industrial environments, leveraging machine learning to anticipate equipment failures, reduce unplanned downtime, and optimise lifecycle costs. This paper presents a systems-level examination of predictive maintenance within the broader context of cyber-physical production systems and Industry 4.0 infrastructures. We critically analyse the architectural choices that underpin data acquisition, feature engineering, and model deployment, highlighting the trade-offs between centralised and edge-based processing paradigms. The discussion extends to the selection and adaptation of machine learning algorithms, ranging from supervised learning for failure classification to deep reinforcement learning for dynamic maintenance scheduling. Operational challenges such as data heterogeneity, label scarcity, concept drift, and real-time latency are examined through the lens of robustness and scalability. The paper also investigates the sustainability implications of predictive maintenance, including energy consumption of computing resources and the environmental footprint of sensor networks. Fairness and equity concerns arise when maintenance decisions disproportionately affect certain production lines or workforce groups; these are assessed alongside governance frameworks that mandate transparency, auditability, and accountability. Policy recommendations are proposed to align predictive maintenance deployments with regulatory standards and ethical guidelines. By integrating technical, organisational, and societal perspectives, this work offers a holistic framework for designing resilient and responsible predictive maintenance systems in smart industrial environments.
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