Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/339645
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dc.coverage.spatial
dc.date.accessioned2021-09-08T04:18:46Z-
dc.date.available2021-09-08T04:18:46Z-
dc.identifier.urihttp://hdl.handle.net/10603/339645-
dc.description.abstractPredictive analytics has found a predominant place in almost all the fields. It has transformed the perspective of the industrial maintenance activities from preventive to predictive maintenance. Predictive maintenance focus on scheduling the maintenance activity before the onset of failures to reduce the downtime of equipment and to mitigate the effect of failures. This accelerated the deployment of Artificial Intelligence and Machine Learning based prognostic models, to predict the faults in the equipment under study. The existing models are backed by intense feature engineering, which inherently has the threat of overlooking the important features of the data newline
dc.format.extent
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titlePredictive Analytic Framework for Machine State Classification and Remaining Useful Life Prediction Using Machine Learning
dc.title.alternative
dc.creator.researcherSharanya S
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideRevathi Venkataraman
dc.publisher.placeKattankulathur
dc.publisher.universitySRM University
dc.publisher.institutionDepartment of Computer Science Engineering
dc.date.registered
dc.date.completed2021
dc.date.awarded
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science Engineering

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80_recommendation.pdfAttached File283.29 kBAdobe PDFView/Open
certificate page.pdf190.46 kBAdobe PDFView/Open
chapter 1.pdf387.39 kBAdobe PDFView/Open
chapter 2.pdf264.64 kBAdobe PDFView/Open
chapter 3.pdf601.91 kBAdobe PDFView/Open
chapter 4.pdf781.87 kBAdobe PDFView/Open
chapter 5.pdf863.65 kBAdobe PDFView/Open
chapter 6.pdf155.05 kBAdobe PDFView/Open
curriculum vitae.pdf26.68 kBAdobe PDFView/Open
list of publications.pdf153.03 kBAdobe PDFView/Open
preliminary page.pdf359.14 kBAdobe PDFView/Open
references.pdf297.37 kBAdobe PDFView/Open
title page.pdf138.4 kBAdobe PDFView/Open


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