Conference article

Fault Detection and Diagnosis with Modelica Language using Deep Belief Network

Dongkyu Lee
Green City R&D Team, R&D Division, Hyundai Engineering and Construction Company, South Korea

Byoungdoo Lee
Green City R&D Team, R&D Division, Hyundai Engineering and Construction Company, South Korea

Jin Woo Shin
Department of Electrical Engineering, KAIST, South Korea

Download articlehttp://dx.doi.org/10.3384/ecp15118615

Published in: Proceedings of the 11th International Modelica Conference, Versailles, France, September 21-23, 2015

Linköping Electronic Conference Proceedings 118:66, s. 615-623

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Published: 2015-09-18

ISBN: 978-91-7685-955-1

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

The air handling unit (AHU) is the main component of heating, ventilation and air-conditioning (HVAC) systems, and irregular faults in AHUs are major sources of energy consumption. For energy efficient operation of HVAC, this paper aims to detect and diagnose three abnormal states in the AHU with the popular deep learning model, called Deep Belief Network (DBN), where we train it using various data generated by Modelica.

Keywords

Fault detectin and diagnosis; Air-handling unit; Deep Belief Network

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