Fault diagnosis in HVAC chillers

Author

Choi, Kihoon ; Namburu, Setu M. ; Azam, Mohammad S. ; Luo, Jianhui ; Pattipati, Krishna R. ; Patterson-Hine, Ann

Volume

8

Issue

3

fYear

2005

Firstpage

24

Lastpage

32

Abstract

In this article, we consider a data-driven approach for fault detection and isolation (FDI) of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.

Keywords

HVAC; digital simulation; fault diagnosis; least squares approximations; principal component analysis; support vector machines; FDI; HVAC chillers; MPCA; MPLS; SVM; data-driven fault detection; data-driven fault isolation; digital simulation; fault diagnosis; least squares approximations; multiway dynamic principal component analysis; multiway partial least squares; support vector machines; Capacitance; Control systems; Fault detection; Fault diagnosis; Instruments; Refrigerants; Temperature control; Valves; Water heating; Water resources;

fLanguage

English

Journal_Title

Instrumentation & Measurement Magazine, IEEE

Publisher

ieee

ISSN

1094-6969

Type

jour

DOI

10.1109/MIM.2005.1502443

Filename

1502443