Automatic HVAC fault detection and diagnosis system generation based on heat flow models

Abstract

This article introduces a new graph-based modeling methodology called heat flow modeling (HFM) for the purpose of mapping building information model (BIM) of HVAC systems automatically into fault detection and diagnosis (FDD) systems that can be integrated into HVAC control systems. The goal is an efficient and effective support of the maintenance of HVAC systems to detect and locate faults that may reduce energy efficiency, user comfort, or system lifetime. The nodes of the HFM model have a one-to-one relationship with HVAC system components and related building entities. The nodes are connected by arcs that model the flows in the HVAC systems, e.g., air, water, and information flows. The functionality of the nodes includes state variable estimations and failure rule evaluations. The failure rule outputs can be fed to an associative network based diagnosis engine to locate the faults. Since HFM nodes are instances of generic classes derived from small libraries, HVAC FDD systems can be automatically generated. The simulation result has shown the effectiveness of a proposed FDD approach and two software prototypes demonstrating the reduced engineering effort of fault detection for a small bank HVAC system.

Acknowledgments

The authors would like express their gratitude to Dr. Vladimir Bazjanac and Dr. James O’Donnell from Lawrence Berkeley National Laboratory for providing sample IFC files.

Gerhard Zimmermann is Professor. Yan Lu, Member ASHRAE, is Senior Research Scientist. George Lo is Senior Principle Scientist.