Machine Learning-Driven Predictive Maintenance Data
收藏IEEE2026-04-17 收录
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https://ieee-dataport.org/documents/machine-learning-driven-predictive-maintenance-data
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资源简介：
This paper presents a comprehensive analysis of machine learning applications in predictive maintenance for IoT-enabled smart buildings. Machine learning algorithms enable real-time fault detection and predictive analytics in building operations. IoT sensor networks provide continuous monitoring of HVAC, lighting, and mechanical systems. The integration achieves 8-19% energy reduction and 93-98% fault detection accuracy [1]. Digital twin technologies enhance system modelling and performance optimisation. The study analyses 34 peer-reviewed publications from 2019 to 2025. Implementation challenges include data quality, system integration, and computational requirements. Future research directions focus on explainable AI, federated learning, and edge computing applications.
提供机构：
Harikrishna Elaprolu
