A Semi-supervised Machine Learning Approach for Acoustic Monitoring of Robotic Manufacturing Facilities

dc.contributor.advisorNelson, Jill
dc.contributor.authorBynum, Jeffrey
dc.creatorBynum, Jeffrey
dc.date2019-08-02
dc.date.accessioned2020-02-12T21:16:45Z
dc.date.available2020-02-12T21:16:45Z
dc.description.abstractDiagnosing characteristic industrial equipment characteristic behavior non-invasively and in situ is an emerging field of study. An algorithm was developed to acoustically monitor mechanical systems with minimal data labels. The methodology was evaluated using a semiconductor device manufacturing process, consisting of a Selective Compliance Assembly Robot Arm (SCARA) system, via an embedded microphone array. Combined unsupervised and supervised data analysis techniques to identify critical processes for eventual life-cycle tracking, was demonstrated. A spectrogram-based convolutional neural network performed primary robotic motion segmentation with an average accuracy of 85% using ground-truth validation data. Subsequent unsupervised analysis using similarity metrics as well as k-means clustering on engineered features had mixed success in distinguishing secondary robotic actuations. A semi-supervised technique was viable to differentiate characteristics in robotic motions with limited available labeled data. Data visualizations demonstrated potential limitations in engineered feature separability as well as probable error sources. Further refinement is required for better segmentation accuracy as well as identifying features that represent secondary characteristics in manufacturing systems.
dc.identifier.urihttps://hdl.handle.net/1920/11666
dc.language.isoen
dc.subjectHealth monitoring
dc.subjectSCARA
dc.subjectMachine learning
dc.subjectRobotic manufacturing
dc.subjectAcoustic monitoring
dc.titleA Semi-supervised Machine Learning Approach for Acoustic Monitoring of Robotic Manufacturing Facilities
dc.typeThesis
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorGeorge Mason University
thesis.degree.levelMaster's
thesis.degree.nameMaster of Science in Electrical Engineering

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