Data mining and analysis of lift maintenance records

Today, with the rapid development of science and technology, the lift, as an important means of transportation in high-rise buildings, plays a vital role in people’s daily life. The resulting safety issues are also getting more and more attention. In this project, the lift failure data provided by t...

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Main Author: Ying, Baohui
Other Authors: Ling Keck Voon
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/164167
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author Ying, Baohui
author2 Ling Keck Voon
author_facet Ling Keck Voon
Ying, Baohui
author_sort Ying, Baohui
collection NTU
description Today, with the rapid development of science and technology, the lift, as an important means of transportation in high-rise buildings, plays a vital role in people’s daily life. The resulting safety issues are also getting more and more attention. In this project, the lift failure data provided by the lift company was analyzed and found that the failure frequency of the lift is linked to the fre quency of people’s daily activities, especially on weekdays and rush hours, and will be accompanied by a certain periodicity. Therefore, to predict the specific location and fault category of the lift failure on a certain day, this dissertation uses the Autoregressive Integrated Moving Average Model(AMIRA) method to predict the lift failure rate after the seasonal adjustment of the fault data.Combined with the chi-squared automatic interaction detector (CHAID) decision tree algorithm, the lift fault position and the lift fault type are successfully predicted. Finally, the fuzzy comprehensive evaluation method is used to assess the risk of a single unit of the lift under test, to realize the monitoring of the whole lift system.
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spelling ntu-10356/1641672023-01-09T00:48:45Z Data mining and analysis of lift maintenance records Ying, Baohui Ling Keck Voon School of Electrical and Electronic Engineering EKVLING@ntu.edu.sg Engineering::Electrical and electronic engineering Today, with the rapid development of science and technology, the lift, as an important means of transportation in high-rise buildings, plays a vital role in people’s daily life. The resulting safety issues are also getting more and more attention. In this project, the lift failure data provided by the lift company was analyzed and found that the failure frequency of the lift is linked to the fre quency of people’s daily activities, especially on weekdays and rush hours, and will be accompanied by a certain periodicity. Therefore, to predict the specific location and fault category of the lift failure on a certain day, this dissertation uses the Autoregressive Integrated Moving Average Model(AMIRA) method to predict the lift failure rate after the seasonal adjustment of the fault data.Combined with the chi-squared automatic interaction detector (CHAID) decision tree algorithm, the lift fault position and the lift fault type are successfully predicted. Finally, the fuzzy comprehensive evaluation method is used to assess the risk of a single unit of the lift under test, to realize the monitoring of the whole lift system. Master of Science (Computer Control and Automation) 2023-01-09T00:48:45Z 2023-01-09T00:48:45Z 2022 Thesis-Master by Coursework Ying, B. (2022). Data mining and analysis of lift maintenance records. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/164167 https://hdl.handle.net/10356/164167 en application/pdf Nanyang Technological University
spellingShingle Engineering::Electrical and electronic engineering
Ying, Baohui
Data mining and analysis of lift maintenance records
title Data mining and analysis of lift maintenance records
title_full Data mining and analysis of lift maintenance records
title_fullStr Data mining and analysis of lift maintenance records
title_full_unstemmed Data mining and analysis of lift maintenance records
title_short Data mining and analysis of lift maintenance records
title_sort data mining and analysis of lift maintenance records
topic Engineering::Electrical and electronic engineering
url https://hdl.handle.net/10356/164167
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