Manufacturing systems are becoming increasingly complex as more advanced and emerging technologies are integrated into the factory floor to yield new processes or increase the efficiency of existing processes. As greater complexity is formed across the factory, new relationships are often generated that can lead to advanced capabilities, yet produce unforeseen faults and failures. Industrial robot arm work cells within the manufacturing environment present increasing complexity, emergent technologies, new relationships, and unpredicted faults/failures. To maintain required levels of productivity, process quality, and asset availability, manufacturers must reconcile this complexity to understand how the health degradation of constituent physical elements and functional tasks impact one another through the monitoring of critical informative measures and metrics. This article presents the initial efforts in developing a novel hierarchical decomposition methodology. The innovation in this method is that it provides the manufacturer with sufficient discretion to physically deconstruct their system and functionally decompose their process to user-defined levels based upon desired monitoring, maintenance, and control levels. This enables the manufacturer to specify relationships within and across the physical, functional, and information domains to identify impactful health degradations without having to know all possible failure modes. The hierarchical decomposition methodology will advance the state of the art in terms of improving machine health by highlighting how health degradations propagate through the relationship network prior to a piece of equipment compromising the productivity or quality of a process. The first two steps of the methodology, physical decomposition and functional decomposition, are defined in detail and applied to a multi-robot work cell use case.
Industrial robotics users, integrators, and manufacturers are implementing advanced monitoring, diagnostics, and prognostics (collectively known as Prognostics and Health Management (PHM)) techniques and technologies. PHM can take many different forms when implemented, and measures of effectiveness are highly dependent on the techniques implemented. A test bed has been built, and a use case designed, to represent common manufacturing tasks performed in robot work cells where PHM can provide greater equipment and process health intelligence. The physical and functional relationships within the work cell are mapped using a hierarchical deconstruction method to gain a better understanding of the propagation of effects of both equipment and process degradation. The test bed has been built so PHM techniques and technologies can be integrated and tested in a realistic scenario. Data is recorded for post processing and analysis for the verification and validation (V&V) of the implemented PHM techniques. The test bed will serve as a platform to develop, test, verify, and validate PHM techniques at the National Institute of Standards and Technology (NIST), and provide industry participants a standard platform for testing their PHM technologies. Having a common testing platform will provide industry a foundation for sets of tests to evaluate PHM. This paper presents the test bed and use case, the relationships therein, and the data management and collection approaches used to enable future research.
Automated industrial workcells are becoming increasingly complex and varied due to greater accessibility of advanced robotic and sensing technologies. Degradation monitoring and diagnostics must advance to reduce the impact of increased system complexity on troubleshooting faults and failures and to optimize system operations. A new methodology is being developed for the design and implementation of monitoring kinematic chains commonly found in robot workcells. This method will enable the identification of degraded components which contribute to relative positioning accuracy error between moving objects, tools, devices, and other components. The proposed methodology is being developed and tested on a six degree of freedom industrial robot arm workcell use case developed at the National Institute of Standards and Technology (NIST). Industrial robot users and integrators can use this method to examine the kinematic chains within their workcells and design a key position monitoring implementation. With the added key position monitoring, degradations can be identified at a designed resolution allowing for enhanced maintenance planning and production control. The methodology will be extended to other manufacturing workcells in the future.
The National Institute of Standards and Technology (NIST) is performing research to advance the state of the art in monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) to enhance decision-making at the factory floor to promote smarter maintenance and control strategies. One specific thrust in this hierarchical research is focused at the work cell level. A robot system is the focus of this research level where the manufacturing community would benefit from measurement science (e.g., performance metrics, test methods, reference datasets, software tools) to design, deploy, verify, and validate PHMC technologies aimed at a robot system work cell. NIST’s identification of representative manufacturing robot work cell use cases will provide the foundation for which it will construct its own physical test bed. The test bed is designed to emulate the chosen robot system use case and afford sufficient flexibility to add, subtract, or upgrade components and capabilities to be commensurate with common industrial practices. This paper presents various use case options that NIST has considered and highlights the one that will be the foundation of the physical test bed. Additionally, the initial test bed design is introduced.
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