Data-driven Fault Detection and Diagnosis for HVAC water chillers. (August 2016)
- Record Type:
- Journal Article
- Title:
- Data-driven Fault Detection and Diagnosis for HVAC water chillers. (August 2016)
- Main Title:
- Data-driven Fault Detection and Diagnosis for HVAC water chillers
- Authors:
- Beghi, A.
Brignoli, R.
Cecchinato, L.
Menegazzo, G.
Rampazzo, M.
Simmini, F. - Abstract:
- Abstract: Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life. Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses. Since it is not a common practice to collect historical data regarding unforeseen phenomena and abnormal behaviours for HVAC installations, in this paper, a semi-supervised data-driven approach is employed for fault detection and isolation that makes no use of a priori knowledge about abnormal phenomena. The proposed method exploits Principal Component Analysis (PCA) to distinguish anomalies from normal operation variability and a reconstruction-based contribution approach to isolate variables related to faults. The diagnosis task is then tackled by means of a decision table that associates the influence of faults to certain characteristic features. The Fault Detection and Diagnosis (FDD) algorithm performance is assessed by exploiting experimental datasets from two types of water chiller systems. Abstract : Highlights: The paper presents a semi-supervised data-driven approach for FDD in HVAC water chillers. The work is motivated by filling the gap between FDD theory and practice. A recently obtained experimental dataset concerning water chiller systems is used. Typical faults in water chiller units are considered.
- Is Part Of:
- Control engineering practice. Volume 53(2016)
- Journal:
- Control engineering practice
- Issue:
- Volume 53(2016)
- Issue Display:
- Volume 53, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 53
- Issue:
- 2016
- Issue Sort Value:
- 2016-0053-2016-0000
- Page Start:
- 79
- Page End:
- 91
- Publication Date:
- 2016-08
- Subjects:
- Fault Detection and Diagnosis (FDD) -- Heating -- Ventilation and Air Conditioning (HVAC) -- Chiller -- Data-Driven -- Principal Component Analysis (PCA) -- Statistical Process Monitoring (SPM)
Automatic control -- Periodicals
629.89 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09670661 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.conengprac.2016.04.018 ↗
- Languages:
- English
- ISSNs:
- 0967-0661
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3462.020000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 1335.xml