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

Unexpected equipment downtime is a '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.

[1]  Anthony A. Maciejewski,et al.  Optimal mapping of joint faults into healthy joint velocity space for fault-tolerant redundant manipulators , 2011, Robotica.

[2]  Berend Denkena,et al.  Design and analysis of a prototypical sensory Z-slide for machine tools , 2013, Prod. Eng..

[3]  Yoram Koren,et al.  Stream-of-Variation Theory for Automotive Body Assembly , 1997 .

[4]  Atsushi Yamada,et al.  Reliability improvement of industrial robots by optimizing operation plans based on deterioration evaluation , 2002 .

[5]  Mike Wilson Vision systems in the automotive industry , 1999 .

[6]  Xu Fang Research on prognostics technology of robot system , 2008 .

[7]  André Carvalho Bittencourt,et al.  On Modeling and Diagnosis of Friction and Wear in Industrial Robots , 2014 .

[8]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[9]  Bifeng Song,et al.  Application of Prognostic and Health Management technology on aircraft fuel system , 2010, 2010 Prognostics and System Health Management Conference.

[10]  T.D. Batzel,et al.  Prognostic Health Management of Aircraft Power Generators , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Khumbulani Mpofu,et al.  Towards achieving a fully intelligent robotic arc welding: a review , 2015, Ind. Robot.

[12]  Immanuel Edinbarough,et al.  A vision and robot based on-line inspection monitoring system for electronic manufacturing , 2005, Comput. Ind..

[13]  Chanhun Park,et al.  Design and kinematics analysis of dual arm robot manipulator for precision assembly , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[14]  John Norrish,et al.  Recent Progress on Programming Methods for Industrial Robots , 2010, ISR/ROBOTIK.

[15]  S. Niku Introduction to Robotics: Analysis, Systems, Applications , 2001 .

[16]  Kyle A. Jeffries,et al.  Enhanced Robotic Automated Fiber Placement with Accurate Robot Technology and Modular Fiber Placement Head , 2013 .

[17]  Irad Ben-Gal,et al.  Backup strategy for robots’ failures in an automotive assembly system , 2009 .

[18]  Wei Zhu,et al.  A Design of End Effector for Measuring Robot Orientation Accuracy and Repeatability , 2011 .

[19]  M. A. Sahir Arikan,et al.  Process modeling, simulation, and paint thickness measurement for robotic spray painting , 2000, J. Field Robotics.

[20]  Rainer Müller,et al.  Reconfigurable handling systems as an enabler for large components in mass customized production , 2013, J. Intell. Manuf..

[21]  Nan Chen,et al.  Prognostics and Health Management: A Review on Data Driven Approaches , 2015 .

[22]  Mahdi Agheli,et al.  SHeRo: Scalable hexapod robot for maintenance, repair, and operations , 2014 .

[23]  Mohamed AbuAli,et al.  A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains , 2014 .

[24]  Hon-Yuen Tam,et al.  A non-contact technique for the on-site inspection of molds and dies polishing , 2004 .

[25]  Guangjun Liu Control of robot manipulators with consideration of actuator performance degradation and failures , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[26]  Armando Fox,et al.  Improving Machine Tool Interoperability Using Standardized Interface Protocols: MT Connect , 2008 .

[27]  Hoda A. ElMaraghy,et al.  Flexible and reconfigurable manufacturing systems paradigms , 2005 .

[28]  Mark Summers Robot Capability Test and Development of Industrial Robot Positioning System for the Aerospace Industry , 2005 .

[29]  Jörg Franke,et al.  Highly Efficient Control System Enabling Robot Accuracy Improvement , 2014 .

[30]  Brian A. Weiss,et al.  The present status and future growth of maintenance in US manufacturing: results from a pilot survey , 2016, Manufacturing review.

[31]  Ken Young,et al.  Accuracy assessment of the modern industrial robot , 2000 .

[32]  Thomas A. Fuhlbrigge,et al.  Automated industrial robot path planning for spray painting process: A review , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[33]  Sherman Y. T. Lang,et al.  Automated robotic programming for products with changes , 2007 .

[34]  M.L. Malinowski,et al.  System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing , 2015, Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference.

[35]  Bojan Jerbić,et al.  Calibration of an Industrial Robot Using a Stereo Vision System , 2014 .

[36]  Behrooz Parhami,et al.  Defect, Fault, Error,..., or Failure? , 1997 .

[37]  Francesco Pierri,et al.  Sensor fault diagnosis for manipulators performing interaction tasks , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[38]  Russell Devlieg,et al.  Expanding the Use of Robotics in Airframe Assembly Via Accurate Robot Technology , 2010 .

[39]  Yves Berthier,et al.  Degradation of high loaded oscillating bearings: Numerical analysis and comparison with experimental observations , 2014 .

[40]  Joan Pellegrino,et al.  Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems , 2016 .

[41]  N. Lv,et al.  Research evolution on intelligentized technologies for arc welding process.pdf , 2018 .

[42]  Joan Pellegrino,et al.  Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop , 2015, Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference.

[43]  Y. F. Yong,et al.  Off‐Line Programming , 2007 .

[44]  Yoshihiro Kusuda Robotization in the Japanese automotive industry , 1999 .