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

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dc.contributor.advisor Nelson, Jill
dc.contributor.author Bynum, Jeffrey
dc.creator Bynum, Jeffrey
dc.date 2019-08-02
dc.date.accessioned 2020-02-12T21:16:45Z
dc.date.available 2020-02-12T21:16:45Z
dc.identifier.uri http://hdl.handle.net/1920/11666
dc.description.abstract Diagnosing 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. en_US
dc.language.iso en en_US
dc.subject health monitoring en_US
dc.subject SCARA en_US
dc.subject machine learning en_US
dc.subject robotic manufacturing en_US
dc.subject acoustic monitoring en_US
dc.title A Semi-supervised Machine Learning Approach for Acoustic Monitoring of Robotic Manufacturing Facilities en_US
dc.type Thesis en_US
thesis.degree.name Master of Science in Electrical Engineering en_US
thesis.degree.level Master's en_US
thesis.degree.discipline Electrical Engineering en_US
thesis.degree.grantor George Mason University en_US


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