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.

Self-Training of a Fault-Free Model for Residential Air Conditioner Fault Detection and Diagnostics

Published

Author(s)

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

Abstract

This study is meant to be applied to residential, unitary, air-source, single-speed, vapor compression air-conditioning systems with nominal cooling capacities less than 19 kW (65,000 Btu•h-1 or 5.4 tons). In this research, a method for self-training FDD and the new concepts of zone density and operational domain coverage rate are introduced. Various fault-free correlation techniques, such as ANN (artificial neural network), MPR (multivariable polynomial regression), and combined ANN-MPR, are demonstrated and compared for self-training FDD. For the validation of this method, a full FDD algorithm was developed to include eight scenarios (1- fault-free, 2-compressor valve leakage, 3-improper outdoor air flow, 4-improper indoor air flow, 5-liquid-line restriction, 6-refrigerant undercharge, 7-refrigerant overcharge, and 8- presence of non-condensable gases) using data from 72 fault-free and 84 faulty tests.
Citation
Technical Note (NIST TN) - NIST TN 1881
Report Number
NIST TN 1881

Keywords

Fault detection, fault diagnosis, heat pump, self-training

Citation

Payne, W. , Domanski, P. , Heo, J. and Du, Z. (2015), Self-Training of a Fault-Free Model for Residential Air Conditioner Fault Detection and Diagnostics, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.1881 (Accessed April 23, 2024)
Created September 15, 2015, Updated November 10, 2018