Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection

Safa  Ozdemir; Osamah N. Neamah; Raif Bayir

doi:10.55092/aias20250001

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Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection

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Safa Ozdemir¹, Osamah N. Neamah¹, Raif Bayir²^,∗

¹ Department of Mechatronics Engineering, Graduate Institute of Karabuk University, Karabuk, Turkey

² Department of Mechatronics Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey

* rbayir@karabuk.edu.tr

Volume
Volume 2 Issue 1, 2025
Citation
Ozdemir S, N. Neamah O, Bayir R. Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection. Artif. Intell. Auton. Syst. 2025(1):0001, https://doi.org/10.55092/aias20250001.
DOI
10.55092/aias20250001
Copyright
Copyright2025 by the authors. Published by ELSP.

Abstract

This study presents the design and implementation of a real-time monitoring system for traction elevators, leveraging piezoelectric sensors for vibration measurement and speed sensors for velocity data acquisition. The system is powered by a LattePanda dashboard equipped with an integrated Real-Time Clock (RTC), ensuring precise data collection and timestamping. Vibration data is captured through piezoelectric sensors, while velocity data from speed sensors is used to calculate acceleration. The collected data is stored locally and can also be transmitted remotely. Aimed at improving elevator safety and efficiency, the system detects potential issues such as misalignments and mechanical wear. Given the increasing number of elevator accidents, this study focuses on enhancing monitoring capabilities using advanced technologies. Data from an electric elevator was analyzed with three anomaly detection algorithms: Isolation Forest, Support Vector Machine (SVM), and Z-score. The results revealed that Isolation Forest identified 15 anomalies (1.06% of the data), SVM detected 25 anomalies (1.77% of the data), and Z-score identified 86 anomalies (6.08% of the data). This research not only enhances elevator condition monitoring but also lays the groundwork for future digital twin systems in passenger elevator applications.

Keywords

condition monitoring; passenger elevator; anomaly detection; predictive maintenance

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