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MERGING SUBJECT MATTER EXPERTISE AND DEEP CONVOLUTIONAL NEURALNETWORK FOR STATE-BASED ONLINE MACHINE-PART INTERACTIONCLASSIFICATION

dc.contributor.authorHao, Wang
dc.contributor.authorYassine Qamsane
dc.contributor.authorJames, Moyne
dc.contributor.authorKira, Barton
dc.contributor.advisorBarton, Kira
dc.date.accessioned2021-09-21T20:33:27Z
dc.date.available2021-09-21T20:33:27Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/2027.42/169573
dc.description.abstractMachine-part interaction classification is a key capability required by Cyber-Physical Systems (CPS), a pivotal enabler of Smart Manufacturing (SM). While previous relevant studies on the subject have primarily focused on time series classification, change point detection is equally important because it provides temporal information on changes in behavior of the machine. In this work, we address point detection and time series classification for machine-part interactions with a deep Convolutional Neural Network (CNN) based framework. The CNN in this framework utilizes a two-stage encoder-classifier structure for efficient feature representation and convenient deployment customization for CPS. Though data-driven, the design and optimization of the framework are Subject Matter Expertise (SME) guided. An SME defined Finite State Machine (FSM) is incorporated into the framework to prohibit intermittent misclassifications. In the case study, we implement the framework to perform machine-part interaction classification on a milling machine, and the performance is evaluated using a testing dataset and deployment simulations. The implementation achieved an average F1-Score of 0.946 across classes on the testing dataset and an average delay of 0.24 seconds on the deployment simulations.
dc.subjectdeep learning
dc.subjectsmart manufacturing
dc.subjectcyber physical systems
dc.subjectchange point detection
dc.subjecttime series classification
dc.titleMERGING SUBJECT MATTER EXPERTISE AND DEEP CONVOLUTIONAL NEURALNETWORK FOR STATE-BASED ONLINE MACHINE-PART INTERACTIONCLASSIFICATION
dc.typeProject
dc.contributor.affiliationumMechanical Engineering
dc.contributor.affiliationumComputer Science
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/169573/1/honors_capstone_report_hao_wang.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/169573/2/capstone_ppt_hao_wang.pptx
dc.identifier.doihttps://dx.doi.org/10.7302/2618
dc.working.doi10.7302/2618en
dc.owningcollnameHonors Program, The College of Engineering


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