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Observations on Developing Anomaly Detection Programs with Case Study: Robotic Arm Manipulators

Published

Author(s)

Michael E. Sharp

Abstract

Manufacturing environments face many unique challenges in regards to balancing high standards of both product quality and production efficiency. Proper diagnostic health assessment is essential for maximizing uptime and maintaining product and process quality. This is particularly true considering the increasing use of industrial robotics and automated manipulators. Although several monitoring methods and technologies have been previously proposed, practical concerns regarding data limitations, variability of setup, and scarcity of ground truth points of validation from active industrial sites have limited their usefulness and adoption. This paper seeks to provide an overview of barriers and offer a feasible action plan for developing a practical robot health monitoring program, matching available capabilities and assets to maximize knowledge gain. Data observations were made on industrial six Degree of Freedom (DOF) robots actively deployed in a manufacturing facility with a variety of operational tasks.
Citation
International Journal of Advanced Manufacturing Technology

Keywords

Diagnostics, Machine Learning, Maintenance, Manufacturing, Monitoring, Operations Management, Robotics
Created February 13, 2019