Grant: Engage Grants for universities

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