Fault detection and diagnostics of an HVAC sub-system using adaptive resonance theory neural networks

The commercial building sector consumed about 20% of the total primary energy in the U.S. in 2008. A significant yet avoidable portion of the energy consumption is due to inefficient system operations. The inefficiencies can be attributed to degrading HVAC sub-systems, and undetected abnormal conditions. Recognition and remediation of these conditions through advanced data analytics can reduce energy consumption by 5% to 20%. This could save about $9 billion in utility costs in the U.S. alone. Modern buildings are constantly sending messages in the form of sensor data. However, this data is only as good as the system that collects it. Therefore, the present work explores fault detection and diagnostics (FDD) of an HVAC sub-system, in particular an air handling unit (AHU), through the evaluation of various methods. The detection methods include a controls alarm threshold, rule-based expressions, regression, one-class support vector machine (SVM), back-propagation, adaptive resonance theory (ART), and lateral priming adaptive resonance theory (LAPART). The diagnosis of AHU faults were performed using a multi-class SVM and LAPART algorithms. The results from the fault detection experiments were reviewed based on the two-class classification where the number of false positives and false negatives where compared. The diagnostic results were evaluated based on the comparison of precision and probability of detection values.

Name thesis:degree_name

Mechanical Engineering

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Year

2015

Author dc:creator

• Jones, Christian Birk

Contributors dc:contributor

• Mammoli, Andrea
• Caudell, Thomas
• Tapia, Lydia
• Sorrentino, Francesco

• air handling unit
• artificial neural networks
• adaptive resonance theory
• laterally primed adaptive resonance theory
• support vector machines
• fault detection
• fault diagnostics
• HVAC
• machine learning

Language dc:language

English

Identifier

https://digitalrepository.unm.edu/me_etds/28

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:me_etds-1027