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dc.contributor.advisorNguyen, Van Hop
dc.contributor.authorNgo, Thuy Dung
dc.date.accessioned2024-03-14T07:40:28Z
dc.date.available2024-03-14T07:40:28Z
dc.date.issued2020
dc.identifier.urihttp://keep.hcmiu.edu.vn:8080/handle/123456789/4502
dc.description.abstractBosch is a global company which pervades in over 60 countries around the world. Its operations are divided into four business sectors: Mobility Solutions, Industrial Technology, Consumer Goods and Energy and Building Technology. Bosch Powertrain Solutions plant in Dong Nai manufactures continuously variable transmission pushbelts (CVT pushbelt). The company started the production in 2008 and in its first year of operation, it produced 1.6 million pushbelts. By March 2018, the production of Bosch plant in Dong Nai has been more than 25 million CVT pushbelts. Having manufactured a wide range and a vast number of pushbelts to satisfy customer demand, machinery breakdown is just “common sense”. However, it is considered as one of the most substantial losses of the plant. The purpose of this thesis is to predict equipment breakdowns and failures based on machine learning model stacking which would be a great facilitator of fault detection and further decisionmaking.en_US
dc.language.isoenen_US
dc.subjectMachine learningen_US
dc.titleApplying Stacked Machine Learning Model In Predicting Equipment Failures- A Bosch Company Case Studyen_US
dc.typeThesisen_US


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