Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations – Summary of an Interactive Workshop Highlighting PHM Trends
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Published
Mar 22, 2021
Brian Weiss
Michael Brundage
Abstract
Personnel from the National Institute of Standards and Technology (NIST) organized and led a Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) workshop at the 2019 Annual Conference of the Prognostics and Health Management Society held on September 23rd, 2019 in Scottsdale, Arizona. This event featured panel presentations and discussions from industry, government, and academic participants who are focused in advancing monitoring, diagnostic, and prognostic (collectively known as prognostic and health management (PHM)) capabilities within manufacturing operations. The participants represented a diverse cross-section of technology developers, integrators, end-users/manufacturers (from small to large), and researchers. These contributors discussed 1) what works well, 2) common challenges that need to be addressed, 3) where the community’s priorities should be focused, and 4) how PHM technological adoption can be sped in a cost-effective manner. This report summarizes the workshop and offers lessons learned regarding the current state of PHM. Based upon the discussions, recommended next steps to advance this technological domain are also presented.
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Keywords
Diagnostics, Maintenance, Manufacturing, Measurement, Monitoring, Prognostics, Standards
References
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Barajas, L. G., & Srinivasa, N. (2008). Real-time diagnostics, prognostics health management for large-scale manufacturing maintenance systems. Paper presented at the ASME International Manufacturing Science and Engineering Conference, MSEC2008, Evanston, IL, United States.
Bi, Z. M., Lang, S. Y. T., Shen, W., & Wang, L. (2008). Reconfigurable manufacturing systems: the state of the art. International Journal of Production Research, 46(4), 967-992. doi:10.1080/00207540600905646
Brundage, M. P., Sexton, T., Hodkiewicz, M., Morris, K. C., Arinez, J., Ameri, F., . . . Xiao, G. (2019). Where do we start? Guidance for technology implementation in maintenance management for manufacturing. Journal of Manufacturing Science and Engineering, 141(9).
Coleman, C., Damodaran, S., Chandramouli, M., & Deuel, E. (2017). Making maintenance smarter: Predictive maintenance and the digital supply network. Retrieved from https://www2.deloitte.com/content/dam/insights/us/articles/3828_Making-maintenance-smarter/DUP_Making-maintenance-smarter.pdf
Helu, M., & Hedberg, T. (2015). Enabling Smart Manufacturing Research and Development using a Product Lifecycle Test Bed. 43rd North American Manufacturing Research Conference, NAMRC 43, 1, 86-97. doi:10.1016/j.promfg.2015.09.066
Helu, M., & Weiss, B. A. (2016). The current state of sensing, health management, and control for small-to-medium-sized manufacturers. Paper presented at the ASME 2016 Manufacturing Science and Engineering Conference, MSEC2016.
Holland, S. (2020). Unsettled Technology Opportunities for Vehicle Health Management and the Role for Health-Ready Components.
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.
International Organization for Standardization. (2009). ISO 18435-1:2009 - Industrial automation systems and integration − Diagnostics, capability assessment and maintenance applications integration − Part 1: Overview and general requirements.
International Organization for Standardization. (2012). ISO 13379-1:2012 - Condition monitoring and diagnostics of machines − Data interpretation and diagnostics techniques − Part 1: General guidelines.
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. Manuf Rev (Les Ulis), 3, 10. doi:10.1051/mfreview/2016005
Jin, X., Weiss, B. A., Siegel, D., & Lee, J. (2016). Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing. Int J Progn Health Manag, 7(Spec Iss on Smart Manufacturing PHM), 012. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/28058173
Jovane, F., Koren, Y., & Boer, C. R. (2003). Present and future of flexible automation: Towards new paradigms. CIRP Annals-Manufacturing Technology, 52(2), 543-560. doi:Doi 10.1016/S0007-8506(07)60203-0
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group: Forschungsunion.
Kalgren, P. W., Byington, C. S., Roemer, M. J., & Watson, M. J. (2007). Defining PHM, a lexical evolution of maintenance and logistics. Paper presented at the 2006 IEEE AUTOTESTCON - IEEE Systems Readiness Technology Conference, Anaheim, CA, United states.
Klinger, A., & Weiss, B. A. (2018). Examining Workcell Kinematic Chains to Identify Sources of Positioning Degradation. Paper presented at the Annual Conference of the PHM Society, Philadelphia, Pennsylvania.
Klinger, A. S., & Weiss, B. A. (2018). Robotic Work Cell Test Bed to Support Measurement Science for PHM. Paper presented at the 2018 ASME Manufacturing Science and Engineering Conference (MSEC), College Station, Texas.
Kolberg, D., & Zuhlke, D. (2015). Lean Automation enabled by Industry 4.0 Technologies. Ifac Papersonline, 48(3), 1870-1875. doi:10.1016/j.ifacol.2015.06.359
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-a. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41.
Li, Y., Zhao, W. H., Lan, S. H., Ni, J., Wu, W. W., & Lu, B. H. (2015). A review on spindle thermal error compensation in machine tools. International Journal of Machine Tools & Manufacture, 95, 20-38. doi:10.1016/j.ijmachtools.2015.04.008
Luxhoj, J. T., Riis, J. O., & Thorsteinsson, U. (1997). Trends and perspectives in industrial maintenance management. Journal of Manufacturing Systems, 16(6), 437-453. doi:Doi 10.1016/S0278-6125(97)81701-3
Ly, C., Tom, K., Byington, C. S., Patrick, R., & Vachtsevanos, G. J. (2009). Fault diagnosis and failure prognosis for engineering systems: A global perspective. Paper presented at the 2009 IEEE International Conference on Automation Science and Engineering, CASE 2009, Bangalore, India.
Mori, M., Fujishima, M., Komatsu, M., Zhao, B., & Liu, Y. (2008). Development of remote monitoring and maintenance system for machine tools. CIRP Annals-Manufacturing Technology, 57(1), 433-436. doi:10.1016/j.cirp.2008.03.108
Pellegrino, J., Justiniano, M., Raghunathan, A., & Weiss, B. A. (2016). Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems. NIST Advanced Manufacturing Seriess (AMS). Retrieved from https://dx.doi.org/10.6028/NIST.AMS.100-2
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. International Journal of Advanced Manufacturing Technology, 50(1-4), 297-313. doi:10.1007/s00170-009-2482-0
Qiao, G. (2019). Advanced Sensor and Target Development to Support Robot Accuracy Degradation Assessment. Paper presented at the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).
Qiao, G., & Weiss, B. A. (2017). Accuracy Degradation Analysis for Industrial Robot Systems. Paper presented at the ASME International Manufacturing Science and Engineering Conference, Los Angeles, California.
Qiao, G., & Weiss, B. A. (2018). Monitoring, diagnostics, and prognostics for robot tool center accuracy degradation. Paper presented at the ASME 2018 13th International Manufacturing Science and Engineering Conference.
Roemer, M. J., Nwadiogbu, E., & Bloor, G. (2001). Development of diagnostic and prognostic technologies for aerospace health management applications. Paper presented at the 2001 IEEE Aerospace Conference.
Sexton, T., Brundage, M. P., Hoffman, M., & Morris, K. C. (2017). Hybrid datafication of maintenance logs from ai-assisted human tags. Paper presented at the 2017 ieee international conference on big data (big data).
Sexton, T., Hodkiewicz, M., Brundage, M. P., & Smoker, T. (2018). Benchmarking for keyword extraction methodologies in maintenance work orders. Paper presented at the Proceedings of the Annual Conference of the PHM Society.
Sexton, T. B., & Brundage, M. P. (2019). Nestor: A Tool for Natural Language Annotation of Short Texts. Retrieved from
Sharp, M., Brundage, M. P., Sprock, T., & Weiss, B. A. (2019). Selecting Optimal Data for Creating Informed Maintenance Decisions in a Manufacturing Environment. Paper presented at the Model-Based Enterprise Summit 2019.
Sharp, M. E., Sexton, T. B., & Brundage, M. P. (2016). Semi-Autonomous Labeling of Unstructured Maintenance Log Data for Diagnostic Root Cause Analysis.
Szipka, K., Archenti, A., Vogl, G. W., & Donmez, M. A. (2019). Identification of machine tool squareness errors via inertial measurements. CIRP Annals, 68(1), 547-550. doi:https://doi.org/10.1016/j.cirp.2019.04.070
Thomas, D. S. (2018). The costs and benefits of advanced maintenance in manufacturing: US Department of Commerce, National Institute of Standards and Technology.
Thomas, D. S., & Weiss, B. A. (2020). Economics of Manufacturing Machinery Maintenance: A Survey and Analysis of US Costs and Benefits.
Vogl, G. W., Calamari, M., Ye, S., & Donmez, M. A. (2016). A Sensor-Based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes. Paper presented at the 44th North American Manufacturing Research Conference (NAMRC), Blacksburg, VA.
Vogl, G. W., Galfond, B. C., & Jameson, N. J. (2019). Bearing Metrics for Health Monitoring of Machine Tool Linear Axes. Paper presented at the 2019 Manufacturing Science and Engineering Conference (MSEC 2019), Erie, PA.
Vogl, G. W., Jameson, N. J., Archenti, A., Szipka, K., & Donmez, M. A. (2019). Root‐cause analysis of wear‐induced error motion changes of machine tool linear axes. International Journal of Machine Tools and Manufacture, 143, 38-48. doi:https://doi.org/10.1016/j.ijmachtools.2019.05.004
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards Related to Prognostics and Health Management (PHM) for Manufacturing (NISTIR 8012). Retrieved from Gaithersburg, Maryland, USA: http://dx.doi.org/10.6028/NIST.IR.8012
Vogl, G. W., Weiss, B. A., & Helu, M. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30(1), 79-95. doi:10.1007/s10845-016-1228-8
Weiss, B. A. (2019). Developing Measurement Science to Verify and Validate the Identification of Robot Workcell Degradation. Paper presented at the ASME 2019 14th International Manufacturing Science and Engineering Conference (MSEC2019), Erie, Pennsylvania.
Weiss, B. A., Alonzo, D., & Weinman, S. D. (2017). Summary Report on a Workshop on Advanced Monitoring, Diagnostics, and Prognostics for Manufacturing Operations. Retrieved from
Weiss, B. A., Brundage, M. P., & Pellegrino, J. (2020). Summary Report: Meeting of the ASME Standards Subcommittee on Advanced Monitoring, Diagnostics, and Prognostics for Manufacturing Operations Hosted at NIST.
Weiss, B. A., Brundage, M. P., Tamm, Y., Makila, T., & Pellegrino, J. (2019). Summary Report on the Industry Forum for Monitoring, Diagnostics, and Prognostics for Manufacturing Operations. Retrieved from
Weiss, B. A., & Klinger, A. S. (2017). Identification of Industrial Robot Arm Work Cell Use Cases and a Test Bed to Promote Monitoring, Diagnostic, and Prognostic Technologies. Paper presented at the 2017 Annual Conference of the Prognostics and Health Management (PHM) Society, St. Petersburg, FL.
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.
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:Doi 10.1016/S0007-8506(07)61526-1
Albertelli, P., Goletti, M., & Monno, M. (2013). A new receptance coupling substructure analysis methodology to improve chatter free cutting conditions prediction. International Journal of Machine Tools and Manufacture, 72, 16-24. doi:https://doi.org/10.1016/j.ijmachtools.2013.05.003
Barajas, L. G., & Srinivasa, N. (2008). Real-time diagnostics, prognostics health management for large-scale manufacturing maintenance systems. Paper presented at the ASME International Manufacturing Science and Engineering Conference, MSEC2008, Evanston, IL, United States.
Bi, Z. M., Lang, S. Y. T., Shen, W., & Wang, L. (2008). Reconfigurable manufacturing systems: the state of the art. International Journal of Production Research, 46(4), 967-992. doi:10.1080/00207540600905646
Brundage, M. P., Sexton, T., Hodkiewicz, M., Morris, K. C., Arinez, J., Ameri, F., . . . Xiao, G. (2019). Where do we start? Guidance for technology implementation in maintenance management for manufacturing. Journal of Manufacturing Science and Engineering, 141(9).
Coleman, C., Damodaran, S., Chandramouli, M., & Deuel, E. (2017). Making maintenance smarter: Predictive maintenance and the digital supply network. Retrieved from https://www2.deloitte.com/content/dam/insights/us/articles/3828_Making-maintenance-smarter/DUP_Making-maintenance-smarter.pdf
Helu, M., & Hedberg, T. (2015). Enabling Smart Manufacturing Research and Development using a Product Lifecycle Test Bed. 43rd North American Manufacturing Research Conference, NAMRC 43, 1, 86-97. doi:10.1016/j.promfg.2015.09.066
Helu, M., & Weiss, B. A. (2016). The current state of sensing, health management, and control for small-to-medium-sized manufacturers. Paper presented at the ASME 2016 Manufacturing Science and Engineering Conference, MSEC2016.
Holland, S. (2020). Unsettled Technology Opportunities for Vehicle Health Management and the Role for Health-Ready Components.
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.
International Organization for Standardization. (2009). ISO 18435-1:2009 - Industrial automation systems and integration − Diagnostics, capability assessment and maintenance applications integration − Part 1: Overview and general requirements.
International Organization for Standardization. (2012). ISO 13379-1:2012 - Condition monitoring and diagnostics of machines − Data interpretation and diagnostics techniques − Part 1: General guidelines.
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. Manuf Rev (Les Ulis), 3, 10. doi:10.1051/mfreview/2016005
Jin, X., Weiss, B. A., Siegel, D., & Lee, J. (2016). Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing. Int J Progn Health Manag, 7(Spec Iss on Smart Manufacturing PHM), 012. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/28058173
Jovane, F., Koren, Y., & Boer, C. R. (2003). Present and future of flexible automation: Towards new paradigms. CIRP Annals-Manufacturing Technology, 52(2), 543-560. doi:Doi 10.1016/S0007-8506(07)60203-0
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group: Forschungsunion.
Kalgren, P. W., Byington, C. S., Roemer, M. J., & Watson, M. J. (2007). Defining PHM, a lexical evolution of maintenance and logistics. Paper presented at the 2006 IEEE AUTOTESTCON - IEEE Systems Readiness Technology Conference, Anaheim, CA, United states.
Klinger, A., & Weiss, B. A. (2018). Examining Workcell Kinematic Chains to Identify Sources of Positioning Degradation. Paper presented at the Annual Conference of the PHM Society, Philadelphia, Pennsylvania.
Klinger, A. S., & Weiss, B. A. (2018). Robotic Work Cell Test Bed to Support Measurement Science for PHM. Paper presented at the 2018 ASME Manufacturing Science and Engineering Conference (MSEC), College Station, Texas.
Kolberg, D., & Zuhlke, D. (2015). Lean Automation enabled by Industry 4.0 Technologies. Ifac Papersonline, 48(3), 1870-1875. doi:10.1016/j.ifacol.2015.06.359
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-a. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41.
Li, Y., Zhao, W. H., Lan, S. H., Ni, J., Wu, W. W., & Lu, B. H. (2015). A review on spindle thermal error compensation in machine tools. International Journal of Machine Tools & Manufacture, 95, 20-38. doi:10.1016/j.ijmachtools.2015.04.008
Luxhoj, J. T., Riis, J. O., & Thorsteinsson, U. (1997). Trends and perspectives in industrial maintenance management. Journal of Manufacturing Systems, 16(6), 437-453. doi:Doi 10.1016/S0278-6125(97)81701-3
Ly, C., Tom, K., Byington, C. S., Patrick, R., & Vachtsevanos, G. J. (2009). Fault diagnosis and failure prognosis for engineering systems: A global perspective. Paper presented at the 2009 IEEE International Conference on Automation Science and Engineering, CASE 2009, Bangalore, India.
Mori, M., Fujishima, M., Komatsu, M., Zhao, B., & Liu, Y. (2008). Development of remote monitoring and maintenance system for machine tools. CIRP Annals-Manufacturing Technology, 57(1), 433-436. doi:10.1016/j.cirp.2008.03.108
Pellegrino, J., Justiniano, M., Raghunathan, A., & Weiss, B. A. (2016). Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems. NIST Advanced Manufacturing Seriess (AMS). Retrieved from https://dx.doi.org/10.6028/NIST.AMS.100-2
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. International Journal of Advanced Manufacturing Technology, 50(1-4), 297-313. doi:10.1007/s00170-009-2482-0
Qiao, G. (2019). Advanced Sensor and Target Development to Support Robot Accuracy Degradation Assessment. Paper presented at the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).
Qiao, G., & Weiss, B. A. (2017). Accuracy Degradation Analysis for Industrial Robot Systems. Paper presented at the ASME International Manufacturing Science and Engineering Conference, Los Angeles, California.
Qiao, G., & Weiss, B. A. (2018). Monitoring, diagnostics, and prognostics for robot tool center accuracy degradation. Paper presented at the ASME 2018 13th International Manufacturing Science and Engineering Conference.
Roemer, M. J., Nwadiogbu, E., & Bloor, G. (2001). Development of diagnostic and prognostic technologies for aerospace health management applications. Paper presented at the 2001 IEEE Aerospace Conference.
Sexton, T., Brundage, M. P., Hoffman, M., & Morris, K. C. (2017). Hybrid datafication of maintenance logs from ai-assisted human tags. Paper presented at the 2017 ieee international conference on big data (big data).
Sexton, T., Hodkiewicz, M., Brundage, M. P., & Smoker, T. (2018). Benchmarking for keyword extraction methodologies in maintenance work orders. Paper presented at the Proceedings of the Annual Conference of the PHM Society.
Sexton, T. B., & Brundage, M. P. (2019). Nestor: A Tool for Natural Language Annotation of Short Texts. Retrieved from
Sharp, M., Brundage, M. P., Sprock, T., & Weiss, B. A. (2019). Selecting Optimal Data for Creating Informed Maintenance Decisions in a Manufacturing Environment. Paper presented at the Model-Based Enterprise Summit 2019.
Sharp, M. E., Sexton, T. B., & Brundage, M. P. (2016). Semi-Autonomous Labeling of Unstructured Maintenance Log Data for Diagnostic Root Cause Analysis.
Szipka, K., Archenti, A., Vogl, G. W., & Donmez, M. A. (2019). Identification of machine tool squareness errors via inertial measurements. CIRP Annals, 68(1), 547-550. doi:https://doi.org/10.1016/j.cirp.2019.04.070
Thomas, D. S. (2018). The costs and benefits of advanced maintenance in manufacturing: US Department of Commerce, National Institute of Standards and Technology.
Thomas, D. S., & Weiss, B. A. (2020). Economics of Manufacturing Machinery Maintenance: A Survey and Analysis of US Costs and Benefits.
Vogl, G. W., Calamari, M., Ye, S., & Donmez, M. A. (2016). A Sensor-Based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes. Paper presented at the 44th North American Manufacturing Research Conference (NAMRC), Blacksburg, VA.
Vogl, G. W., Galfond, B. C., & Jameson, N. J. (2019). Bearing Metrics for Health Monitoring of Machine Tool Linear Axes. Paper presented at the 2019 Manufacturing Science and Engineering Conference (MSEC 2019), Erie, PA.
Vogl, G. W., Jameson, N. J., Archenti, A., Szipka, K., & Donmez, M. A. (2019). Root‐cause analysis of wear‐induced error motion changes of machine tool linear axes. International Journal of Machine Tools and Manufacture, 143, 38-48. doi:https://doi.org/10.1016/j.ijmachtools.2019.05.004
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards Related to Prognostics and Health Management (PHM) for Manufacturing (NISTIR 8012). Retrieved from Gaithersburg, Maryland, USA: http://dx.doi.org/10.6028/NIST.IR.8012
Vogl, G. W., Weiss, B. A., & Helu, M. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30(1), 79-95. doi:10.1007/s10845-016-1228-8
Weiss, B. A. (2019). Developing Measurement Science to Verify and Validate the Identification of Robot Workcell Degradation. Paper presented at the ASME 2019 14th International Manufacturing Science and Engineering Conference (MSEC2019), Erie, Pennsylvania.
Weiss, B. A., Alonzo, D., & Weinman, S. D. (2017). Summary Report on a Workshop on Advanced Monitoring, Diagnostics, and Prognostics for Manufacturing Operations. Retrieved from
Weiss, B. A., Brundage, M. P., & Pellegrino, J. (2020). Summary Report: Meeting of the ASME Standards Subcommittee on Advanced Monitoring, Diagnostics, and Prognostics for Manufacturing Operations Hosted at NIST.
Weiss, B. A., Brundage, M. P., Tamm, Y., Makila, T., & Pellegrino, J. (2019). Summary Report on the Industry Forum for Monitoring, Diagnostics, and Prognostics for Manufacturing Operations. Retrieved from
Weiss, B. A., & Klinger, A. S. (2017). Identification of Industrial Robot Arm Work Cell Use Cases and a Test Bed to Promote Monitoring, Diagnostic, and Prognostic Technologies. Paper presented at the 2017 Annual Conference of the Prognostics and Health Management (PHM) Society, St. Petersburg, FL.
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.
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:Doi 10.1016/S0007-8506(07)61526-1
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Technical Papers