Paper Conference

Proceedings of SimBuild Conference 2012: 5th conference of IBPSA USA


Fault Diagnosis in HVAC Systems Based on the Heat Flow Model

Alexander Schiendorfer, Gerhard Zimmermann, Yan Lu, George Lo
Siemens Corporation, Corporate Research and Technology, Princeton, NJ
Informatik, University of Kaiserslautern, Germany

Abstract: Fault Detection and Diagnosis based on the Heat Flow Model (HFM) provides a generic and extensible framework for monitoring HVAC systems. It supports the finding and fixing of faulty components. During the fault detection phase, measured sensor and control values are used to perform estimations based on the physical properties of the system. Discrepancies of estimated and measured values are collected as a detection failure vector. Diagnosis seeks to find the most probable cause for the observed failures. In HVAC systems, the failures and faults form an m-n relation. Our proposed diagnosis is performed with an associative network to map the relations among failures and faults using the inherent fault simulation capabilities of the HFM nodes at runtime. The similarity of the detection failure vector to the simulated failure vector indicates the probability of the corresponding fault. To find the best method of fault diagnosis, this paper examines different similarity metrics for HFM based FDD, including Euclidean distances, Manhattan distance, root of sum of products, Jaccard index, and a table based metric. The effectiveness of the proposed diagnosis approaches is presented with a case study based on a reference implementation using Simulink and Java.
Pages: 440 - 447