Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

Guixiu Qiao and Brian A. Weiss
Publication Target: 
IJPHM
Publication Issue: 
Special Issue on Smart Manufacturing PHM
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_16_013.pdf683.55 KBSeptember 29, 2016 - 8:43am

Unexpected equipment downtime is ‘pain point’ for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
013
Page Count: 
13
Submission Keywords: 
Maintenance Strategy
prognostics and health management
robotics
Preventive and predictive maintenance
Submission Topic Areas: 
Health management system design and engineering
Industrial applications
Sensors
Standards and methodologies
Systems and platform applications
Verification and validation
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