Data-driven Fault Detection and Diagnosis for HVAC water chillers
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- 作者：A. Beghia ; beghi@dei.unipd.it" class="auth_mail" title="E-mail the corresponding author ; R. Brignolia ; r.brignoli@outlook.com" class="auth_mail" title="E-mail the corresponding author ; L. Cecchinatob ; lcecchinato@energyeverest.com" class="auth_mail" title="E-mail the corresponding author ; G. Menegazzoa ; gabriele.menegazzo@yahoo.it" class="auth_mail" title="E-mail the corresponding author ; M. Rampazzoa ; mirco.rampazzo@dei.unipd.it" class="auth_mail" title="E-mail the corresponding author ; F. Simminia ; simminif@dei.unipd.it" class="auth_mail" title="E-mail the corresponding author
- 关键词：Fault Detection and Diagnosis (FDD) ; Heating ; Ventilation and Air Conditioning (HVAC) ; Chiller ; Data-Driven ; Principal Component Analysis (PCA) ; Statistical Process Monitoring (SPM)
- 刊名：Control Engineering Practice
- 出版年：2016
- 出版时间：August 2016
- 年：2016
- 卷：53
- 期：Complete
- 页码：79-91
- 全文大小：1643 K
文摘
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The paper presents a semi-supervised data-driven approach for FDD in HVAC water chillers.
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The work is motivated by filling the gap between FDD theory and practice.
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A recently obtained experimental dataset concerning water chiller systems is used.
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Typical faults in water chiller units are considered.