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008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438018600
035 ▼a (MiAaPQ)AAI10808634
035 ▼a (MiAaPQ)purdue:22712
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 621
1001 ▼a Hjortland, Andrew L.
24510 ▼a Automated Fault Detection, Diagnostics, Impact Evaluation, and Service Decision-Making for Direct Expansion Air Conditioners.
260 ▼a [S.l.] : ▼b Purdue University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 298 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: James E. Braun.
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a This work describes approaches for automatically detecting, diagnosis, and evaluating the impacts of common faults in unitary rooftop air conditioning equipment. A semi-empirical component-based modeling approach using virtual sensors has been i
520 ▼a Much of this work has been devoted to estimating the performance impacts of faults that grow over time, like heat exchanger fouling or refrigerant charge leakage. To estimate these impacts, semi-empirical models for predicting the normal perform
520 ▼a In addition, different service and maintenance strategies are compared in this work using a simulation environment that was developed. A data-driven artificial neural network model of a rooftop unit with faults has been derived for this purpose
590 ▼a School code: 0183.
650 4 ▼a Mechanical engineering.
690 ▼a 0548
71020 ▼a Purdue University. ▼b Mechanical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997827 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자