Design of Escalator Fault Prediction and Intelligent Maintenance System Based on Machine Learning Article Swipe
Xiaoqing Wang
,
Wanli Yu
,
Liqi Lin
,
Pengcheng Liu
,
Dongdong Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/atde250892
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/atde250892
As urban transit systems become increasingly dependent on vertical transportation infrastructure, ensuring the reliability and safety of escalators is critical for operational efficiency and public safety. Traditional escalator maintenance strategies, primarily based on scheduled inspections or reactive repairs, often fail to detect early-stage faults, resulting in unexpected failures, increased downtime, and elevated costs. In response to these challenges, this study proposes an intelligent escalator fault prediction and maintenance system powered by machine learning algorithms. The system integrates real-time sensor data, fault history, and operational parameters to construct predictive models using decision trees, support vector machines, and deep learning techniques. Experimental results on real-world maintenance datasets demonstrate that the proposed approach significantly improves fault detection accuracy, enables early warnings, and reduces maintenance response time. This work presents a scalable and adaptive framework that enhances equipment availability and extends escalator service life through intelligent, data-driven diagnostics and maintenance planning.
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All OpenAlex metadata
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https://doi.org/10.3233/atde250892Digital Object Identifier
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Design of Escalator Fault Prediction and Intelligent Maintenance System Based on Machine LearningWork title
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enPrimary language
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2025Year of publication
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2025-10-01Full publication date if available
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Xiaoqing Wang, Wanli Yu, Liqi Lin, Pengcheng Liu, Dongdong ChenList of authors in order
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https://doi.org/10.3233/atde250892Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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