Computational fieldwork support for efficient operation and maintenance of mechanical, electrical and plumbing systems

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.