LDR | | 02046nmm uu200397 4500 |
001 | | 000000332852 |
005 | | 20240805171717 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438018600 |
035 | |
▼a (MiAaPQ)AAI10808634 |
035 | |
▼a (MiAaPQ)purdue:22712 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 621 |
100 | 1 |
▼a Hjortland, Andrew L. |
245 | 10 |
▼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. |
502 | 1 |
▼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 |
710 | 20 |
▼a Purdue University.
▼b Mechanical Engineering. |
773 | 0 |
▼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 |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997827
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |