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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 Bynum, Jeffrey
dc.creator Bynum, Jeffrey 2019-08-02 2020-02-12T21:16:45Z 2020-02-12T21:16:45Z
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 Master of Science in Electrical Engineering en_US Master's en_US Electrical Engineering en_US George Mason University en_US

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