Affiliation of Author(s):University of Connecticut

Journal:IEEE Access

Funded by:National Science Foundation under Grant CCF-1331850

Key Words:Fault diagnosis, HVAC air handling system, online learning algorithm, hidden Markov model, new fault

Abstract:Air handling systems are key sub-systems of Heating Ventilation and Air Conditioning (HVAC) systems.  They condition and deliver air to satisfy human thermal comfort requirements and provide acceptable indoor air quality.  Faults in their components and sensors may lead to high-energy consumption, poor thermal comfort and unacceptable indoor air quality.  Additionally, new types of faults may falsely be identified as known types.  Identifying failure modes and their severities with low false identification rates is thus critical to know what faults occur and how severe they are.  However, this

All the Authors:Peter B. Luh,Krishnna R. Pattipati

First Author:Ying Yan

Indexed by:Journal paper

Discipline:Engineering

First-Level Discipline:Control Science and Engineering

Document Type:J

Volume:6

Page Number:21682–21696

ISSN No.:2169-3536

Translation or Not:no

Date of Publication:2018-05-07

Included Journals:SCI

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