HVAC System Fault Diagnosis via Feature Selection and Classification

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2023 13th International Conference on Information Science and Technology (ICIST)
PublisherIEEE
Pages432-440
Number of pages9
ISBN (electronic)979-8-3503-1392-5
ISBN (print)979-8-3503-1393-2
Publication statusPublished - 2023

Publication series

Name
ISSN (Print)2164-4357
ISSN (electronic)2573-3311

Conference

Title13th International Conference on Information Science and Technology (ICIST 2023)
LocationTexas A&M University & Renaissance Cairo Mirage City Hotel
PlaceEgypt
CityDoha & Cairo
Period8 - 14 December 2023

Abstract

Fault detection and diagnosis play a crucial role in energy savings in heating, ventilation, and air conditioning systems. It enables timely and appropriate repairs to prevent system malfunctioning and reduce energy waste. In this paper, a proposed feature selection algorithm or a neurodynamic optimization algorithm based on information gain is used for selecting a certain number of significant features, and then a stacking classifier is utilized to classify data into several different fault types by using the selected features. Experimental results are elaborated to demonstrate the superior performance of the proposed method against baselines in terms of accuracy on most of the datasets. © 2023 IEEE

Research Area(s)

  • Fault detection and diagnosis, heating ventilation and air conditioning (HVAC) systems, feature selection, classification

Citation Format(s)

HVAC System Fault Diagnosis via Feature Selection and Classification. / Ye, Xuntan; Li, Hongzong; Wang, Jun.
2023 13th International Conference on Information Science and Technology (ICIST). IEEE, 2023. p. 432-440.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review