A machine learning-based approach for elevator door system fault diagnosis

Simple item page
dc.contributor.authorLiang, Taiwang
dc.contributor.authorChen, Chong
dc.contributor.authorWang, Tao
dc.contributor.authorZhang, Ao
dc.contributor.authorQin, Jian
dc.date.accessioned2022-11-03T12:58:47Z
dc.date.available2022-11-03T12:58:47Z
dc.date.issued2022-10-28
dc.description.abstractThe 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.en_UK
dc.identifier.citationLiang 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. 28-33en_UK
dc.identifier.eisbn978-1-6654-9042-9
dc.identifier.eissn2161-8089
dc.identifier.isbn978-1-6654-9043-6
dc.identifier.issn2161-8070
dc.identifier.urihttps://doi.org/10.1109/CASE49997.2022.9926596
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18650
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFault diagnosisen_UK
dc.subjectSupport vector machinesen_UK
dc.subjectRadio frequencyen_UK
dc.subjectCostsen_UK
dc.subjectMaintenance engineeringen_UK
dc.subjectFeature extractionen_UK
dc.subjectElevatorsen_UK
dc.titleA machine learning-based approach for elevator door system fault diagnosisen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Elevator_door_system_fault_diagnosis-2022.pdf
Size:
2.85 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)