Reference number : GC-2017-Q4-00721, amendment number : , agreement type : G, recipient legal name : Gunay, Burak (Carleton University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2_x000D_
emissions in Canada; and, about 30% of the energy used in commercial buildings is wasted due to poorly_x000D_
maintained, degraded, and improperly controlled equipment and components. Therefore, data-driven analytical_x000D_
tools for building operation and maintenance have the potential to reduce our environmental impact and to_x000D_
provide comfortable, healthy, and productive indoor environments._x000D_
The objective of this research project is to develop algorithms that will benchmark the maintenance_x000D_
performance of building systems and components through text-mining within work-order management_x000D_
systems. Work-orders are the traditional form of information keeping in buildings. The work-order_x000D_
management systems contain operators' descriptions of maintenance routines and failure patterns in HVAC_x000D_
systems and their components, albeit as large amorphous documents. Consequently, they are seldom used to_x000D_
extract information about common HVAC faults and their occurrence frequencies. New algorithms will be_x000D_
developed to extract useful information from text-mining work-order management systems. The algorithms_x000D_
will identify top system and component-level failure modes, develop component level failure rate models, and_x000D_
introduce failure modes and effects analysis tools for building systems and their components._x000D_
The proposed research project will make significant intellectual, environmental, economic, and HQP_x000D_
contributions to Canada. New datasets and methods will be created. Adoption of these methods by the industry_x000D_
partner, Canada's largest property manager Bentall Kennedy, will contribute to our knowledge-based economy._x000D_
Wider usage of the algorithms developed in this research project will reduce the environmental and economic_x000D_
impact of commercial buildings. The HQP will work on data from real buildings, learn their systems and_x000D_
components, and their shortcomings; and conduct interdisciplinary research on building performance and_x000D_
data-science, program purpose french : Indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2_x000D_
emissions in Canada; and, about 30% of the energy used in commercial buildings is wasted due to poorly_x000D_
maintained, degraded, and improperly controlled equipment and components. Therefore, data-driven analytical_x000D_
tools for building operation and maintenance have the potential to reduce our environmental impact and to_x000D_
provide comfortable, healthy, and productive indoor environments._x000D_
The objective of this research project is to develop algorithms that will benchmark the maintenance_x000D_
performance of building systems and components through text-mining within work-order management_x000D_
systems. Work-orders are the traditional form of information keeping in buildings. The work-order_x000D_
management systems contain operators' descriptions of maintenance routines and failure patterns in HVAC_x000D_
systems and their components, albeit as large amorphous documents. Consequently, they are seldom used to_x000D_
extract information about common HVAC faults and their occurrence frequencies. New algorithms will be_x000D_
developed to extract useful information from text-mining work-order management systems. The algorithms_x000D_
will identify top system and component-level failure modes, develop component level failure rate models, and_x000D_
introduce failure modes and effects analysis tools for building systems and their components._x000D_
The proposed research project will make significant intellectual, environmental, economic, and HQP_x000D_
contributions to Canada. New datasets and methods will be created. Adoption of these methods by the industry_x000D_
partner, Canada's largest property manager Bentall Kennedy, will contribute to our knowledge-based economy._x000D_
Wider usage of the algorithms developed in this research project will reduce the environmental and economic_x000D_
impact of commercial buildings. The HQP will work on data from real buildings, learn their systems and_x000D_
components, and their shortcomings; and conduct interdisciplinary research on building performance and_x000D_
data-science, agreement title english : Benchmarking operation of commercial buildings through text-mining maintenance work-orders, agreement title french : Benchmarking operation of commercial buildings through text-mining maintenance work-orders, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-01-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada