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

A SEMI-SUPERVISED MACHINE LEARNING APPROACH FOR ACOUSTIC MONITORING OF ROBOTIC MANUFACTURING FACILITIES Jeffrey Bynum George Mason University, 2019 Thesis Director: Dr. Jill Nelson 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. Chapter 1: Introduction Industrial manufacturing systems must maintain fabrication tolerances as well as meet production capacity requirements [3]. These systems demand persistent monitoring and maintenance due to progressive mechanical wear. Conventional approaches traditionally relied on operator oversight and experience for monitoring system health; however, remote monitoring techniques are increasingly applied. Remote techniques allow flexibility for monitoring inaccessible fabrication environments as well as providing continuous production diagnostics. Moreover, remote monitoring techniques can robustly fuse complex data features for better understanding behind normal and damaged system states. Industrial robots are complex mechanical systems that combine components such as motors, bearings, housings, linear rails, and brakes. These systems typically perform a variety of actuations generalized in 3D Cartesian space. As exampled in robotic manufacturing platforms such as in Figure 1.1, a series of these primary motions describe a process. Actuations for robotic systems can include y-axis (base movement), x-axis (arm extension/retraction), and z-axis (body extension/retraction) motions. Primary motions can be further subdivided into separate, secondary subclasses. Each subclass describes a physically different actuation along the same axis of travel. For example, motion from point A to point B [class label 1(a)] and motion from point B to point A [class label 1(b)] describe two potential subclasses. These subclass actuations are combined sequentially depending on process. These primary motion classes (x, y, z) contain various engaged mechanical hardware over different, discernible sequences. Audible motion characteristics stemming from differences in system dynamics, could possibly be captured through non-invasive, audio recordings. Deviations in system dynamics coupled with corresponding changes in harmonic content