A method for extracting performance metrics using work-order data

Holistic performance metrics are necessary to understand how operational resources are used and to detect anomalous zones, floors, equipment, and work-order categories in large commercial and institutional buildings. Work-order data in computerized maintenance management systems (CMMS) represent an untapped potential to extract such performance metrics. In this paper, a method to conduct text analytics on CMMS data is developed and demonstrated through a case study in which four years’ worth of data from four large commercial buildings are used. Association rule mining technique is employed to identify building, system, and component-level recurring work-order taxonomies and common failure modes. The results highlight the potential of kernel density functions, decision trees, Sankey diagrams, survival curves and stacked line plots to effectively visualize the temporal, spatial, and categorical anomalies in the complaint patterns. It is identified that often only a few floors and complaint types account for most of the complaints in a building. The analysis of operator comments reveal that the most frequent lighting-related complaints are resolved by replacing ballasts and lights, and the thermal and air quality complaints are addressed by adjusting the temperature setpoints, airflow rates, and fan operation schedules.