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dc.contributor.authorvon Hahn, Timothyen
dc.date.accessioned2020-09-25T21:21:34Z
dc.date.available2020-09-25T21:21:34Z
dc.identifier.urihttp://hdl.handle.net/1974/28150
dc.description.abstractMachinery health monitoring is an important tool for improving the productivity of today's manufacturers. Two data-driven techniques are prominent in machinery health monitoring: feature engineering and end-to-end deep learning. The research presented here explores these two data-driven techniques in the context of tool wear monitoring during metal machining. Feature engineering was used as a practical approach to predict when CNC machine tools were worn. A robust data-pipeline was developed, and multiple features and models were tested. The best model achieved a precision-recall area-under-curve (PR-AUC) of 0.349, an acceptable result for the imbalanced data set. For the first known time in tool wear monitoring, and as a demonstration of end-to-end deep learning, self-supervised learning with a disentangled-variational-autoencoder was used. An anomaly detection technique was employed to make predictions in both the input and latent spaces. The method achieved a top PR-AUC score of 0.45 on a common milling data set. However, the approach generalized poorly across the combination of parts on the industrial CNC data set, but a top score of 0.41 was achieved for certain parts. Further data collection will enhance the results for all approaches. Overall, the work highlights the abundance of opportunities at the intersection between machine learning and machinery health monitoring.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/us/*
dc.subjectmachine learningen
dc.subjectdeep learningen
dc.subjectmachinery health monitoringen
dc.subjectvariational autoencoderen
dc.subjectanomaly detectionen
dc.subjecttool wearen
dc.subjecttool condition monitoringen
dc.subjectself-supervised learningen
dc.subjectmanufacturingen
dc.subjectfeature engineeringen
dc.titleFeature Engineering and End-to-End Deep Learning in Tool Wear Monitoringen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorMechefske, Chris
dc.contributor.departmentMechanical and Materials Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Except where otherwise noted, this item's license is described as Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada