Analysis of thermal environment and energy performance by biased economizer outdoor air temperature sensor fault

Chul Ho Kim, Sung Chan Lee, Kyung Soon Park, Kwang Ho Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In order to develop a fault detection and diagnosis (FDD) algorithm for air handling unit (AHU) based on machine learning, the economizer outdodor air temperature (OAT) sensor fault was modeled. Through EnergyPlus program, the economizer OAT sensor modeled a fault that measures the OAT that’s higher or lower than the actual temperature. The distribution of abnormal node point air temperature produced by the fault at the control point of the economizer was reviewed. In addition, the relationship between OAT & mixed air temperature (MAT), OAT & outdoor air fraction (OAF), AHU cooling coil energy, and chiller energy were comparatively analyzed for each fault model. In the case of the fault models, although outdoor air suitable for cooling could be utilized during the operation, outdoor air was not introduced for cooling, and in the opposite case, outdoor air was introduced even when OAT was higher than the indoor set-point temperature, reducing the energy saving effect for cooling. In the future, we aim to analyze energy performance and indoor air quality according to the fault in the return air temperature (RAT) sensor and error in the opening position of the damper. In addition, we plan to continue the analysis of fault data for the various elements of an AHU, and develop FDD algorithms using machine learning.

Original languageEnglish
Pages (from-to)2083-2094
Number of pages12
JournalJournal of Mechanical Science and Technology
Issue number4
Publication statusPublished - 2022 Apr

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program-Advanced Technology Center Plus) (20009710, Artificial Intelligence (AI) Based Automation Technology Development of Fire Protection System Design Drawings) funded by the Ministry of trade, Industry & Energy (MOTIE, Korea). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT, MOE) and (No. 2019M3E7A1113095).

Publisher Copyright:
© 2022, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.


  • Economizer
  • EnergyPlus
  • Fault detection diagnosis
  • Machine learning
  • Outdoor air temperature sensor

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering


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