Liang, TaiwangChen, ChongWang, TaoZhang, AoQin, Jian2022-11-032022-11-032022-10-28Liang T, Chen C, Wang T, et al., (2022) A machine learning-based approach for elevator door system fault diagnosis. In: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 20-24 August 2022, Mexico City, pp. 28-33978-1-6654-9043-62161-8070https://doi.org/10.1109/CASE49997.2022.9926596https://dspace.lib.cranfield.ac.uk/handle/1826/18650The door system is the core part of the elevator. An accurate diagnosis of the door system can aid engineers in troubleshooting and reduce maintenance costs. However, the research of fault diagnosis based on elevator operation and maintenance data is still in its infancy. With the development of the industrial Internet-of-things, real-time monitoring data of elevator can be collected and used for fault diagnosis modeling. This paper investigates a machine learning-based approach to achieve accurate elevator door fault diagnosis. An experimental study was conducted based on the monitoring data collected from the real-world elevator door system. The experimental results revealed that XGBoost algorithm can accurately identify the fault type of the elevator door.enAttribution-NonCommercial 4.0 InternationalFault diagnosisSupport vector machinesRadio frequencyCostsMaintenance engineeringFeature extractionElevatorsConference paper978-1-6654-9042-92161-8089