Observations on developing reliability information utilization in a manufacturing environment with case study: robotic arm manipulators
Manufacturing environments face many unique challenges with regard 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. Information for diagnostic assessments, and reliability information in general, can come from a myriad of sources that can be processed and managed through numerous algorithms that range from simplistic to hypercomplex. One area that typifies the assortment of information sources in a modern manufacturing setting is found with the use of industrial robotics and automated manipulators. Although several monitoring methods and technologies have been previously proposed for this and other assets, adoption has been sporadic with returns on investment not always meeting expectations. Practical concerns regarding data limitations, variability of setup, and scarcity of ground truth points of validation from active industrial sites have contributed to this. This paper seeks to provide an overview of barriers and offer a feasible action plan for developing a practical condition monitoring information utilization program, matching available capabilities and assets to maximize knowledge gain. Observations are made on a real-world case study involving industrial 6 degrees of freedom (DOF) robots actively deployed in a manufacturing facility with a variety of operational tasks.
KeywordsDiagnostics Machine learning Maintenance Manufacturing Monitoring Operations management Robotics
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