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dc.creatorPrabhu, Rahul Srinivasen_US
dc.date.accessioned2012-06-07T23:17:34Z
dc.date.available2012-06-07T23:17:34Z
dc.date.created2002en_US
dc.date.issued2002
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2002-THESIS-P72en_US
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.en_US
dc.descriptionIncludes bibliographical references (leaves 95-98).en_US
dc.descriptionIssued also on microfiche from Lange Micrographics.en_US
dc.description.abstractThis thesis develops a steady state model of a reciprocating, single-stage, vapor compression chiller, based on thermodynamic principles and the ASHRAE HVAC-1 primary toolkit model, to predict and diagnose the behavior of the chiller. The characteristics of the chiller are identified by measuring certain parameters. Once the parameters are identified, the system boundaries are established, with certain parameters tainted by faults being isolated from the input parameters and, in turn, being derived from them using a nonlinear equation solver. A model-based approach is followed for chiller fault detection and diagnosis. The model generates residues by comparing the output of the chiller model with that of the same model albeit with some faults embedded. A nonzero residue indicates the presence of a fault. Various neuro-based classification techniques such as Adaptive Neuro-Fuzzy Inference Systems (grid partition and subtractive clustering) and Artificial Neural Networks (ANN) are evaluated, culminating in fault identification and isolation by an ANN system, batch trained with a fault matrix using the Levenberg-Marquardt learning algorithm, on a feed-forward back propagation network. Finally, additive sensor noise is introduced in select parameters and its effect on the overall accuracy of the model is tabulated.en_US
dc.format.mediumelectronicen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.publisherTexas A&M Universityen_US
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en_US
dc.subjectmechanical engineering.en_US
dc.subjectMajor mechanical engineering.en_US
dc.titleA neuro-computational approach to chiller fault identification and isolationen_US
dc.typeThesisen_US
thesis.degree.disciplinemechanical engineeringen_US
thesis.degree.nameM.S.en_US
thesis.degree.levelMastersen_US
dc.type.genrethesis
dc.type.materialtexten_US
dc.format.digitalOriginreformatted digitalen_US


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