%0 Conference Paper
%A Yimin Chen
%A Eliot Crowe
%A Guanjing Lin
%A Jessica Granderson
%B The 9th ACM International Conference on Systems for Energy-Efficient  Built Environments (BuildSys 2022)
%D 2022
%G eng
%R https://doi.org/10.1145/3563357.3567405
%T Integration of FDD Data to Aid HVAC System Maintenance
%8 11/11/2022
%X <p>Maintenance of heating, ventilation and air conditioning (HVAC) systems in building portfolios becomes increasingly<br />challenging as systems become more complex, and as the number of systems increases across a managed portfolio. Data-driven maintenance approaches employ multiple data sources to analyze the system’s operation and maintenance<br />(O&amp;M) status, and hence can effectively support decision making for complex systems’ maintenance. Automated fault<br />detection and diagnostics (FDD) tools are used to identify abnormal operations and resolve the types and locations of<br />problems in HVAC systems. Data generated by FDD tools contain essential information in terms of the system's<br />abnormal operation such as fault causes, fault location, fault occurrence, and duration. Therefore, the integration of FDD<br />tools’ output data into data-driven maintenance tools can significantly support the maintenance decision-making<br />procedure, and streamline HVAC system’s O&amp;M processes. However, the semantic heterogeneity and the structural<br />heterogeneity in FDD data lower data interpretability and interoperability, and hence hinder the integration of the data<br />by other maintenance tools. In this paper, we propose a framework to organize and integrate FDD data, so that the<br />data can be efficiently queried by or integrated into other maintenance tools. The framework includes the FDD data<br />model, the fault taxonomy library, and organized FDD data structure. The case study demonstrates that the FDD data<br />reorganized under the framework can be efficiently analyzed to assist HVAC system maintenance.</p>