FAULT DIAGNOSIS IN HVAC SYSTEMS BASED ON THE HEAT FLOW MODEL
Alexander Schiendorfer, Gerhard Zimmermann, Yan Lu, George Lo
Fault Detection and Diagnosis based on the Heat Flow Model (HFM) provides a generic and extensible frame- work for monitoring HVAC systems. It supports the find- ing and fixing of faulty components. During the fault detection phase, measured sensor and control values are used to perform estimations based on the physical prop- erties of the system. Discrepancies of estimated and mea- sured values are collected as a detection failure vector. Di- agnosis seeks to find the most probable cause for the ob- served failures. In HVAC systems, the failures and faults form an m-n relation. Our proposed diagnosis is per- formed with an associative network to map the relations among failures and faults using the inherent fault simula- tion capabilities of the HFM nodes at runtime. The simi- larity of the detection failure vector to the simulated fail- ure vector indicates the probability of the corresponding fault. To find the best method of fault diagnosis, this pa- per 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 ap- proaches is presented with a case study based on a refer- ence implementation using Simulink and Java.
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