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postgraduate thesis (Non-HKU): Computational fieldwork support for efficient operation and maintenance of mechanical, electrical and plumbing systems
Title | Computational fieldwork support for efficient operation and maintenance of mechanical, electrical and plumbing systems |
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Authors | |
Issue Date | 2011 |
Publisher | ProQuest, UMI Dissertation Publishing (for Carnegie Mellon University). |
Abstract | There is significant potential for improvement in the performance of Operation and Maintenance (O&M) fieldwork. O&M occurs throughout the lifecycle of a building; the majority of expenses in a building's lifecycle are incurred during O&M. Many strategies have been developed to enhance the O&M environment. However, it is well-known that the maintenance industry adapts new technologies more slowly than other industries. Although the industry's O&M support systems have been enhanced considerably, its overall style of O&M fieldwork has remained essentially unchanged for decades. Furthermore, tradespeople, whose primary roles are O&M fieldwork, vastly underutilize information in the field due to problems with information accessibility and reliability.
This research investigates current practices from the initial phase of assigning O&M requests through the completion of the requests in order to identify inefficiency in O&M fieldwork and to develop strategies to improve the environment from the perspective of computational support. As the first step, shadowing tradespeople was conducted to better understand current O&M fieldwork and pinpoint bottlenecks in the workflow. Statistical analyses (F-test, Analysis of Variance and R2-Test) were conducted to see the correlation among O&M activities as well as the similarity of the collected data.
An Augmented Reality (AR)-based Operation and Maintenance Fieldwork Facilitator (AROMA-FF) is developed to computationally support O&M fieldwork. An O&M information model is developed by enhancing an existing Building Information Model with the data collected from O&M fieldwork practice. An Augmented Reality-based interface is developed for an intuitive user interface. BACnet protocol is used to get sensor-derived operation data in real time from Building Automation Systems.
A series of experiments was conducted in order to quantitatively measure improvement in O&M efficiency by using a software prototype of the AR-based O&M Fieldwork Facilitator. The key metric was time spent on O&M activities. The most impressive finding from the experiment is that while the subjects were trying to locate the target area, they spent, on average, 49% less time with the prototype than conventional strategies in addition to an 8% decrease in time spent getting operation-related data. These results show that the prototype is capable of improving O&M fieldwork efficiency. |
Degree | Doctor of Philosophy |
Dept/Program | Civil Engineering |
Persistent Identifier | http://hdl.handle.net/10722/200319 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Lee, SH | - |
dc.date.accessioned | 2014-08-07T02:18:06Z | - |
dc.date.available | 2014-08-07T02:18:06Z | - |
dc.date.issued | 2011 | - |
dc.identifier.isbn | 978-1243605061 | - |
dc.identifier.uri | http://hdl.handle.net/10722/200319 | - |
dc.description.abstract | There is significant potential for improvement in the performance of Operation and Maintenance (O&M) fieldwork. O&M occurs throughout the lifecycle of a building; the majority of expenses in a building's lifecycle are incurred during O&M. Many strategies have been developed to enhance the O&M environment. However, it is well-known that the maintenance industry adapts new technologies more slowly than other industries. Although the industry's O&M support systems have been enhanced considerably, its overall style of O&M fieldwork has remained essentially unchanged for decades. Furthermore, tradespeople, whose primary roles are O&M fieldwork, vastly underutilize information in the field due to problems with information accessibility and reliability. This research investigates current practices from the initial phase of assigning O&M requests through the completion of the requests in order to identify inefficiency in O&M fieldwork and to develop strategies to improve the environment from the perspective of computational support. As the first step, shadowing tradespeople was conducted to better understand current O&M fieldwork and pinpoint bottlenecks in the workflow. Statistical analyses (F-test, Analysis of Variance and R2-Test) were conducted to see the correlation among O&M activities as well as the similarity of the collected data. An Augmented Reality (AR)-based Operation and Maintenance Fieldwork Facilitator (AROMA-FF) is developed to computationally support O&M fieldwork. An O&M information model is developed by enhancing an existing Building Information Model with the data collected from O&M fieldwork practice. An Augmented Reality-based interface is developed for an intuitive user interface. BACnet protocol is used to get sensor-derived operation data in real time from Building Automation Systems. A series of experiments was conducted in order to quantitatively measure improvement in O&M efficiency by using a software prototype of the AR-based O&M Fieldwork Facilitator. The key metric was time spent on O&M activities. The most impressive finding from the experiment is that while the subjects were trying to locate the target area, they spent, on average, 49% less time with the prototype than conventional strategies in addition to an 8% decrease in time spent getting operation-related data. These results show that the prototype is capable of improving O&M fieldwork efficiency. | - |
dc.language | eng | - |
dc.publisher | ProQuest, UMI Dissertation Publishing (for Carnegie Mellon University). | - |
dc.title | Computational fieldwork support for efficient operation and maintenance of mechanical, electrical and plumbing systems | en_US |
dc.type | PG_Thesis_External | en_US |
dc.description.thesisname | Doctor of Philosophy | - |
dc.description.thesislevel | Doctoral | - |
dc.description.thesisdiscipline | Civil Engineering | - |
dc.identifier.email | Lee, SH: shlee1@hku.hk | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 370 | - |
dc.publisher.place | US | - |