Integration of FDD data to aid HVAC system maintenance

Abstract

Maintenance of heating, ventilation and air conditioning (HVAC) systems in building portfolios becomes increasingly 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 (O&M) status, and hence can effectively support decision making for complex systems' maintenance. Automated fault detection and diagnostics (FDD) tools are used to identify abnormal operations and resolve the types and locations of problems in HVAC systems. Data generated by FDD tools contain essential information in terms of the system's abnormal operation such as fault causes, fault location, fault occurrence, and duration. Therefore, the integration of FDD tools' output data into data-driven maintenance tools can significantly support the maintenance decision-making procedure, and streamline HVAC system's O&M processes. However, the semantic heterogeneity and the structural heterogeneity in FDD data lower data interpretability and interoperability, and hence hinder the integration of the data by other maintenance tools. In this paper, we propose a framework to organize and integrate FDD data, so that the data can be efficiently queried by or integrated into other maintenance tools. The framework includes the FDD data model, the fault taxonomy library, and organized FDD data structure. The case study demonstrates that the FDD data reorganized under the framework can be efficiently analyzed to assist HVAC system maintenance.