An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment
Well-preserved buildings can be a great asset to the country, contributing to its economic growth. The lifespan of a building and its assets can be extended through proper maintenance. This will improve the longevity of its function, sustain its performance, and optimize maintenance costs. Predictiv...
Saved in:
Main Authors: | , |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | http://eprints.utm.my/107727/1/FarahIlyana2023_AnoverviewofPredictiveMaintenanceinRelation.pdf http://eprints.utm.my/107727/ http://dx.doi.org/10.1088/1755-1315/1274/1/012003 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Teknologi Malaysia |
Language: | English |
id |
my.utm.107727 |
---|---|
record_format |
eprints |
spelling |
my.utm.1077272024-09-29T06:26:21Z http://eprints.utm.my/107727/ An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment Hairuddin, Farah Ilyana Azri, Suhaibah G70.212-70.215 Geographic information system Well-preserved buildings can be a great asset to the country, contributing to its economic growth. The lifespan of a building and its assets can be extended through proper maintenance. This will improve the longevity of its function, sustain its performance, and optimize maintenance costs. Predictive maintenance is a proactive approach to maintenance, as it is conducted based on the current operational state of equipment rather than average life statistics. It is generally implemented in building maintenance, machinery, and many other industries. While predictive maintenance has grown in application with artificial intelligence, digital twins, and machine learning, its application with geographical information systems (GIS) and 3D GIS have limitedly discussed. Geospatial predictive maintenance can be realized by integrating the asset's location with its maintenance semantic and temporal information. By incorporating geospatial thinking into the predictive framework, it can help optimize decision-making processes such as allocating maintenance costs based on calculating affected areas, visualizing the location of assets, understanding their interactions with meteorological events and virtual situations. Therefore, this paper will discuss a review of predictive maintenance in relation to GIS and 3D GIS. 2023 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/107727/1/FarahIlyana2023_AnoverviewofPredictiveMaintenanceinRelation.pdf Hairuddin, Farah Ilyana and Azri, Suhaibah (2023) An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment. In: International Graduate Conference of Built Environment and Surveying 2023, GBES 2023, 17 September 2023 - 18 September 2023, Hybrid, Johor Bahru, Johor, Malaysia. http://dx.doi.org/10.1088/1755-1315/1274/1/012003 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
G70.212-70.215 Geographic information system |
spellingShingle |
G70.212-70.215 Geographic information system Hairuddin, Farah Ilyana Azri, Suhaibah An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment |
description |
Well-preserved buildings can be a great asset to the country, contributing to its economic growth. The lifespan of a building and its assets can be extended through proper maintenance. This will improve the longevity of its function, sustain its performance, and optimize maintenance costs. Predictive maintenance is a proactive approach to maintenance, as it is conducted based on the current operational state of equipment rather than average life statistics. It is generally implemented in building maintenance, machinery, and many other industries. While predictive maintenance has grown in application with artificial intelligence, digital twins, and machine learning, its application with geographical information systems (GIS) and 3D GIS have limitedly discussed. Geospatial predictive maintenance can be realized by integrating the asset's location with its maintenance semantic and temporal information. By incorporating geospatial thinking into the predictive framework, it can help optimize decision-making processes such as allocating maintenance costs based on calculating affected areas, visualizing the location of assets, understanding their interactions with meteorological events and virtual situations. Therefore, this paper will discuss a review of predictive maintenance in relation to GIS and 3D GIS. |
format |
Conference or Workshop Item |
author |
Hairuddin, Farah Ilyana Azri, Suhaibah |
author_facet |
Hairuddin, Farah Ilyana Azri, Suhaibah |
author_sort |
Hairuddin, Farah Ilyana |
title |
An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment |
title_short |
An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment |
title_full |
An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment |
title_fullStr |
An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment |
title_full_unstemmed |
An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment |
title_sort |
overview of predictive maintenance in relation to 2d and 3d geographical information system (gis) for built environment |
publishDate |
2023 |
url |
http://eprints.utm.my/107727/1/FarahIlyana2023_AnoverviewofPredictiveMaintenanceinRelation.pdf http://eprints.utm.my/107727/ http://dx.doi.org/10.1088/1755-1315/1274/1/012003 |
_version_ |
1811681250833858560 |