Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A Data Clustering Technique for Fault Detection and Diagnostics in Field-Assembled Air Conditioners

Published

Author(s)

William V. Payne, Piotr A. Domanski, Jaehyeok Heo

Abstract

Fault Detection and Diagnostics (FDD) can be used to commission and maintain the performance of residential style, split-system, air conditioners and heat pumps. Previous investigations by various researchers have provided techniques for detecting and diagnosing all common faults, but these techniques are ideally applied to systems that have been thoroughly tested in a carefully instrumented laboratory environment. Although such an approach can be applied to factory-assembled systems, installation variations of field-assembled systems require in-situ adaptation of FDD methods. Providing a workable solution to this problem has been the impetus for this work which describes a method for adapting laboratory style FDD techniques to field installed systems by automatically customizing the FDD fault-free performance models for random installation differences.
Citation
International Journal of Air-Conditioning and Refrigeration
Volume
26

Keywords

Air-Conditioner Fault, ANN Model, Fault Detection and Diagnosis, Heat Pump Fault, MPR Model, Split-System Fault

Citation

Payne, W. , Domanski, P. and Heo, J. (2018), A Data Clustering Technique for Fault Detection and Diagnostics in Field-Assembled Air Conditioners, International Journal of Air-Conditioning and Refrigeration, [online], https://doi.org/10.1142/S2010132518500153 (Accessed May 30, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created May 14, 2018, Updated November 10, 2018