Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection
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...
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| Format: | Article |
| Language: | English |
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ELS Publishing (ELSP)
2025-02-01
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| Series: | Artificial Intelligence and Autonomous Systems |
| Subjects: | |
| Online Access: | https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2025/aias20250001.pdf |
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| _version_ | 1849762700556500992 |
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| author | Safa Ozdemir Osamah N. Neamah Raif Bayir |
| author_facet | Safa Ozdemir Osamah N. Neamah Raif Bayir |
| author_sort | Safa Ozdemir |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-549ba8936475443ea32e92ff605c19b8 |
| institution | DOAJ |
| issn | 2959-0744 2959-0752 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | ELS Publishing (ELSP) |
| record_format | Article |
| series | Artificial Intelligence and Autonomous Systems |
| spelling | doaj-art-549ba8936475443ea32e92ff605c19b82025-08-20T03:05:41ZengELS Publishing (ELSP)Artificial Intelligence and Autonomous Systems2959-07442959-07522025-02-012111510.55092/aias202500011857449028260384768Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detectionSafa Ozdemir0Osamah N. Neamah1Raif Bayir2Department of Mechatronics Engineering, Graduate Institute of Karabuk University, Karabuk, TurkeyDepartment of Mechatronics Engineering, Graduate Institute of Karabuk University, Karabuk, TurkeyDepartment of Mechatronics Engineering, Faculty of Engineering, Karabuk University, Karabuk, TurkeyThis 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.https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2025/aias20250001.pdfcondition monitoringpassenger elevatoranomaly detectionpredictive maintenance |
| spellingShingle | Safa Ozdemir Osamah N. Neamah Raif Bayir Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection Artificial Intelligence and Autonomous Systems condition monitoring passenger elevator anomaly detection predictive maintenance |
| title | Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection |
| title_full | Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection |
| title_fullStr | Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection |
| title_full_unstemmed | Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection |
| title_short | Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection |
| title_sort | enhanced safety and efficiency in traction elevators a real time monitoring system with anomaly detection |
| topic | condition monitoring passenger elevator anomaly detection predictive maintenance |
| url | https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AIAS/2025/aias20250001.pdf |
| work_keys_str_mv | AT safaozdemir enhancedsafetyandefficiencyintractionelevatorsarealtimemonitoringsystemwithanomalydetection AT osamahnneamah enhancedsafetyandefficiencyintractionelevatorsarealtimemonitoringsystemwithanomalydetection AT raifbayir enhancedsafetyandefficiencyintractionelevatorsarealtimemonitoringsystemwithanomalydetection |