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dc.contributor.authorTretyakov, Dmitry
dc.date.accessioned2016-11-14T13:51:43Z
dc.date.available2016-11-14T13:51:43Z
dc.date.issued2016-08
dc.identifier.citationIntelligent Control and Automation , 2016, 7, 84- 92en_US
dc.identifier.urihttp://dx.doi.org/10.4236/ica.2016.73009
dc.identifier.urihttp://hdl.handle.net/123456789/1240
dc.description.abstractThe article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.en_US
dc.language.isoenen_US
dc.publisherScientific Research Publishingen_US
dc.subjectSelf-Learningen_US
dc.subjectDiagnosticsen_US
dc.subjectFault Detectionen_US
dc.subjectClustersen_US
dc.subjectK-Meansen_US
dc.subjectTurbomachineen_US
dc.subjectGas Turbineen_US
dc.subjectCentrifugal Superchargeren_US
dc.subjectGas Compressor Uniten_US
dc.titleA Self-Learning Diagnosis Algorithm Based on Data Clusteringen_US
dc.typeArticleen_US


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