Journal article
A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers
HVAC&R research, v 20(7), pp 798-809
03 Oct 2014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A new chiller fault detection and diagnosis (FDD) method is proposed in this article. Different from conventional chiller FDD methods, this article considers the FDD problem as a typical one-class classification problem. The fault-free data are classified as the fault-free class. Data of a fault type are regarded as a fault class. The task of fault detection is to detect whether the process data are outliers of the fault-free class. The task of fault diagnosis is to find to which fault class does the process data belong. In this study, support vector data description (SVDD) algorithm is introduced for the one-class classification. The basic idea of the SVDD-based method is to find a minimum-volume hypersphere in a high dimensional feature space to enclose most of the data of an individual class. The proposed method is validated using the ASHRAE RP-1043 (Comstock and Braun 1999) experimental data. It shows more powerful FDD capacity than multi-class SVM-based FDD methods and PCA-based fault detection methods. Four potential applications of the proposed method are also discussed.
Metrics
Details
- Title
- A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers
- Creators
- Yang Zhao - Hong Kong Polytechnic UniversityFu Xiao - Hong Kong Polytechnic UniversityJin Wen - Drexel UniversityYuehong Lu - Hong Kong Polytechnic UniversityShengwei Wang - Hong Kong Polytechnic University
- Publication Details
- HVAC&R research, v 20(7), pp 798-809
- Publisher
- Taylor & Francis
- Number of pages
- 12
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000342845600010
- Scopus ID
- 2-s2.0-84907816618
- Other Identifier
- 991019168613304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Construction & Building Technology
- Engineering, Mechanical
- Thermodynamics