Nelson, JillBynum, Jeffrey2020-02-122020-02-12https://hdl.handle.net/1920/11666Diagnosing 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.enHealth monitoringSCARAMachine learningRobotic manufacturingAcoustic monitoringThesis