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



Published Nov 13, 2020
Guixiu Qiao Brian A. Weiss


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.

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prognostics and health management, robotics, Maintenance Strategy, Preventive and predictive maintenance

Abdi, H., Nahavandi, S., Frayman, Y., & Maciejewski, A. A. (2012). Optimal mapping of joint faults into healthy joint velocity space for fault-tolerant redundant manipulators. Robotica, 30, 635-648. doi:10.1017/s0263574711000671
Agheli, M., Qu, L., & Nestinger, S. S. (2014). SHeRo: Scalable hexapod robot for maintenance, repair, and operations. Robotics and Computer-Integrated Manufacturing, 30(5), 478-488. doi:10.1016/j.rcim.2014.03.008
Arikan, M. A. S., & Balkan, T. (2000). Process modeling, simulation, and paint thickness measurement for robotic spray painting. Journal of Robotic Systems, 17(9), 479-494. Retrieved from ://WOS:000088830200003
Batzel, T. D., & Swanson, D. C. (2009). Prognostic health management of aircraft power generators. IEEE Transactions on Aerospace and Electronic Systems, 45(2), 473-483. doi:10.1109/TAES.2009.5089535
Bi, Z. M., & Lang, S. Y. T. (2007). Automated robotic programming for products with changes. International Journal of Production Research, 45(9), 2105-2118. doi:10.1080/00207540600733634
Bittencourt, A. C. (2012). On Modeling and Diagnosis of Friction and Wear in Industrial Robots. Thesis. Retrieved from
Buschhaus, A., Blank, A., Ziegler, C., & Franke, J. (2014). Highly Efficient Control System Enabling Robot Accuracy Improvement. Procedia CIRP, 23, 200-205. doi:
Caccavale, F., Marino, A., Pierri, F., & Ieee. (2010). Sensor Fault Diagnosis for Manipulators Performing Interaction Tasks. Ieee International Symposium on Industrial Electronics (Isie 2010), 2121-2126. Retrieved from ://WOS:000295007802102
Chen, H., Fuhlbrigge, T., & Li, X. (2008). Automated industrial robot path planning for spray painting process: a review. Paper presented at the Automation Science and Engineering, 2008. CASE 2008. IEEE International Conference on.
Chen, S. B., & Lv, N. (2014). Research evolution on intelligentized technologies for arc welding process. Journal of Manufacturing Processes, 16(1), 109-122. doi:10.1016/j.jmapro.2013.07.002
Denkena, B., Litwinski, K. M., Brouwer, D., & Boujnah, H. (2013). Design and analysis of a prototypical sensory Z-slide for machine tools. Production Engineering, 7(1), 9-14. doi:10.1007/s11740-012-0419-1
DeVlieg, R. (2010). Expanding the use of robotics in airframe assembly via accurate robot technology. SAE Int. J. Aerospace, 3(1), 198-203.
Edinbarough, I., Balderas, R., & Bose, S. (2005). A vision and robot based on-line inspection monitoring system for electronic manufacturing. Computers in Industry, 56(8–9), 986-996. doi:
ElMaraghy, H. A. (2005). Flexible and reconfigurable manufacturing systems paradigms. International Journal of Flexible Manufacturing Systems, 17(4), 261-276. doi:10.1007/s10696-006-9028-7
Greenway, B. (2000). Robot accuracy. Industrial Robot, 27(4), 257-265. doi:10.1108/01439910010372136
Holland, S. W., Barajas, L. G., Salman, M., & Zhang, Y. (2010). PHM for Automotive Manufacturing & Vehicle Applications. Paper presented at the Prognostics & Health Management Conference, Portland, Oregon.
Hu, S. J., & Koren, Y. (1997). Stream-of-variation theory for automotive body assembly. CIRP Annals-Manufacturing Technology, 46(1), 1-6.
IFR, I. F. o. R. (2015). Industrial Robot Statistics. Retrieved from
Jeffries, K. A. (2013). Enhanced Robotic Automated Fiber Placement with Accurate Robot Technology and Modular Fiber Placement Head. SAE International Journal of Aerospace, 6(2), 774-779. doi:10.4271/2013-01-2290
Jin, X., Siegel, D., Weiss, B. A., Gamel, E., Wang, W., Lee, J., & Ni, J. (2016). The Present Status and Future Growth of Maintenance in US Manufacturing: Results from a Pilot Survey. Manufacturing Review.
Kahan, T., Bukchin, Y., Menassa, R., & Ben-Gal, I. (2009). Backup strategy for robots’ failures in an automotive assembly system. International Journal of Production Economics, 120(2), 315-326. doi:
Kusuda, Y. (1999). Robotization in the Japanese automotive industry. Industrial Robot, 26(5), 358-360. doi:10.1108/01439919910283786
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1), 314-334.
Liu, G. (2001). Control of robot manipulators with consideration of actuator performance degradation and failures. Paper presented at the Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on.
Malinowski, M., Beling, P., Haimes, Y., LaViers, A., Marvel, J., & Weiss, B. (2015). System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing. Paper presented at the Annual Conference of the Prognostics and Health Management Society 2015, Coronado, CA.
Massi, F., Bouscharain, N., Milana, S., Le Jeune, G., Maheo, Y., & Berthier, Y. (2014). Degradation of high loaded oscillating bearings: Numerical analysis and comparison with experimental observations. Wear, 317(1-2), 141-152. doi:10.1016/j.wear.2014.06.004
Mitsi, S., Bouzakis, K. D., Mansour, G., Sagris, D., & Maliaris, G. (2004). Off-line programming of an industrial robot for manufacturing. The International Journal of Advanced Manufacturing Technology, 26(3), 262-267. doi:10.1007/s00170-003-1728-5
Muller, R., Esser, M., & Vette, M. (2013). Reconfigurable handling systems as an enabler for large components in mass customized production. Journal of Intelligent Manufacturing, 24(5), 977-990. doi:10.1007/s10845-012-0624-y
National Institute of Standards and Technology. (2015, June 2015). Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems. Retrieved from
Ngan, C.-C., & Tam, H.-Y. (2004). A non-contact technique for the on-site inspection of molds and dies polishing. Journal of Materials Processing Technology, 155–156, 1184-1188. doi:
Niku, S. (2011). Introduction to robotics: analysis, systems, applications (2nd ed.): Hoboken, NJ : Wiley, c2011.
Ogbemhe, J., & Mpofu, K. (2015). Towards achieving a fully intelligent robotic arc welding: a review. Industrial Robot-an International Journal, 42(5), 475-484. doi:10.1108/ir-03-2015-0053
Pan, Z., Polden, J., Larkin, N., Van Duin, S., & Norrish, J. (2012). Recent progress on programming methods for industrial robots. Robotics and Computer-Integrated Manufacturing, 28(2), 87-94. doi:
Parhami, B. (1997). Defect, fault, error, ..., or failure? Ieee Transactions on Reliability, 46(4), 450-451. doi:10.1109/tr.1997.693776
Park, C., & Park, K. (2008). Design and kinematics analysis of dual arm robot manipulator for precision assembly. Paper presented at the Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on.
Shen, T., Wan, F., Cui, W., & Song, B. (2010). Application of prognostic and health management technology on aircraft fuel system. Paper presented at the 2010 Prognostics and System Health Management Conference, PHM '10, Macau, China.
Shirinzadeh, B. (2000). Repeatability and accuracy - who cares and why? Industrial Robot, 27(4), 250-251. Retrieved from ://WOS:000088571800002
Siegel, D., Lee, J., & Dempsey, P. (2014). Investigation and Evaluation of Condition Indicators, Variable Selection, and Health Indication Methods and Algorithms For Rotorcraft Gear Components. Paper presented at the MFPT 2014 Conference, Virginia Beach, VA.
Siegel, D., Zhao, W., Lapira, E., AbuAli, M., & Lee, J. (2014). A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains. Wind Energy, 17(5), 695-714.
Summers, M. (2005). Robot capability test and development of industrial robot positioning system for the aerospace industry. SAE transactions, 114(1), 1108-1118.
Švaco, M., Šekoranja, B., Šuligoj, F., & Jerbić, B. (2014). Calibration of an Industrial Robot Using a Stereo Vision System. Procedia Engineering, 69, 459-463. doi:
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and Health Management: A Review on Data Driven Approaches. Mathematical Problems in Engineering, 2015, 1-17. doi:10.1155/2015/793161
Vijayaraghavan, A., Sobel, W., Fox, A., Dornfeld, D., UC Berkeley, & Warndorf, P. (2008). Improving machine tool interoperability using standardized interface protocols: MT connect. Proceedings of 2008 International Symposium on Flexible Automation, June.
Wang, R., Liu, L., & Xu, F. (2008). Research on prognostics technology of robot system. Machine Tool and Hydraulics, 30(11), 15-19. Retrieved from
Weiss, B. A., Vogl, G. W., Helu, M., Qiao, G., Pellegrino, J., Justiniano, M., & Raghunathan, A. (2015). Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop. Paper presented at the Annual Conference of the Prognostics and Health Management Society 2015, Coronado, CA.
Wilson, M. (1999). Vision systems in the automotive industry. Industrial Robot, 26(5), 354-357. doi:10.1108/01439919910283768
Yamada, A., & Takata, S. (2002). Reliability Improvement of Industrial Robots by Optimizing Operation Plans Based on Deterioration Evaluation. CIRP Annals - Manufacturing Technology, 51(1), 319-322. doi:10.1016/s0007-8506(07)61526-1
Young, K., & Pickin, C. G. (2000). Accuracy assessment of the modern industrial robot. Industrial Robot-an International Journal, 27(6), 427-436. doi:10.1108/01439910010378851
Zhang, X. P., Yan, W. C., Zhu, W., & Wen, T. (2012). A Design of End Effector for Measuring Robot Orientation Accuracy and Repeatability. In R. Zhu (Ed.), Applied Mechanics and Civil Engineering (Vol. 137, pp. 382-386).
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