Adaptive Multi-scale PHM for Robotic Assembly Processes

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Published Oct 18, 2015
Benjamin Y. Choo Peter A. Beling Amy E. LaViers Jeremy A. Marvel Brian A. Weiss

Abstract

Adaptive multiscale prognostics and health management (AM-PHM) is a methodology designed to support PHM in smart manufacturing systems. As a rule, PHM information is not used in high-level decision-making in manufacturing systems. AM-PHM leverages and integrates component- level PHM information with hierarchical relationships across the component, machine, work cell, and production line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. A description of the AM-PHM methodology with a simulated canonical robotic assembly process is presented.

How to Cite

Y. Choo, B. ., A. Beling, P. ., E. LaViers, A. ., A. Marvel, J. ., & A. Weiss, B. . (2015). Adaptive Multi-scale PHM for Robotic Assembly Processes. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2681
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Keywords

remaining useful life (RUL), Fault Tree, robotics, smart manufacturing

References
Ahmad, R., Kamaruddin, S. (2012). An Overview of Time- based and Condition-based Maintenance in Industrial Application. Computers & Industrial Engineering 63, 135-149. doi:10.1016/j.cie.2012.02.002

Al-Habaibeh, A., & Gindy, N. (2000). New Approach for Systematic Design of Condition Monitoring Systems for Milling Processes. Journal of Materials Processing Technology 107 (1-3), 243-251. doi:10.1016/S0924- 0136(00)00718-4

Altintas, Y., Verl, A., Brecher, C., Uriarte, L., & Pritschow, G. (2011). Machine Tool Feed Drives. CIRP Annals – Manufacturing Technology 60 (2), 779-796. doi:10.1016/j.cirp.2011.05.010

Barajas, L. G., & Srinivasa, N. (2008). Real-time Diagnostics, Prognostics Health Management for Large-scale Manufacturing Maintenance Systems. ASME International Manufacturing Science and Engineering Conference (85-94), Evanston, IL.

Batzel, T. D., & Swanson, D. C. (2009). Prognostic Health Management of Aircraft Power Generators. IEEE Transactions on Aerospace and Electronic Systems 45, 473-483.

Biehl, S., Staufenbiel, S., Recknagel, S., Denkena, B., & Bertram, O. (2012). Thin Film Sensors for Condition Monitoring in Ball Screw Drives. 1st Joint International Symposium on
System-Integrated Intelligence 2012: New Challenges for Product and Production Engineering.

Biswas, G., & Mahadevan, S. (2007). A Hierarchical Model-based approach to Systems Health Management. IEEE Aerospace Conference, March, 3-10. doi: 10.1109/AERO.2007.352943

Borisov, O., Fletcher, S., Longstaff, A., & Myers, A. (2013). New Low Cost Sensing Head and Taut Wire Method for Automated Straightness Measurement of Machine Tool Axes.
Optics and Lasers in Engineering 51, 978-985.

Celik, Lee, Vasudevan, and Son (2010) DDDAS-Based Multi-Fidelity Simulation Framework for Supply Chain Systems. IIE Transactions 42, 325-341.

Datta, K., Jize, N., Maclise, D., & Goggin, D. (2004). An IVHM Systems Analysis & Optimization Process. IEEE Aerospace Conference. IEEE (3706-3716).

Feldman, A., de Castro, H. V., and van Gemund A. (2013) Model-Based Diagnostic Decision-Support System for Satellites. IEEE Aerospace Conference. March 2-9, Big Sky, MT.

Ferri, F. A. S., Rodrigues, L. R., Gomes, J. P. P., de Medeiros, I. P., Galvo, R. K. H., & Nascimento, C. L. (2013). Combining PHM Information and System Architecture to Support Aircraft
Maintenance Planning. IEEE Systems Conference (60-65), April 15-18, Orlando, FL. doi:10.1109/SysCon.2013.6549859

Furness, R. J., Wu, C. L., & Ulsoy, A. G. (1996). Statistical Analysis of Feed, Speed, and Wear on Hole Quality in Drilling. Journal of Manufacturing Science and Engineering 118, 367-375.

Hofmeister, J. P., Wagoner, R. S., & Goodman, D. L. (2013). Prognostic Health Management (PHM) of Electrical Systems using Conditioned-based Data for Anomaly and Prognostic Reasoning. Chemical Engineering Transactions 33, 991-996.

Holdren, J. P. (2011). Report to the President on Ensuring American Leadership in Advanced Manufacturing. President's Council of Advisors on Science and Technology. https://www.whitehouse.gov/sites/default/files/microsit es/ostp/pcast-advanced-manufacturing-june2011.pdf

Holland, S. W., Barajas, L. G., Salman, M., & Zhang, Y. (2010). PHM for Automotive Manufacturing & Vehicle Applications. Prognostics & Health Management Conference, Portland, OR.

Hopp, W., & Spearman, M., (3rd). (2008). Factory Physics. Long Grove, IL: Waveland Press, Inc.

Hu, S. J., & Koren, Y. (1997). Stream-of-variation Theory for Automotive Body Assembly. CIRP Annals- Manufacturing Technology 46, 1-6.

International Organization for Standardization, 2002. ISO 13373-1:2002 - Condition Monitoring and Diagnostics of Machines −Vibration Condition Monitoring − Part 1: General
Procedures. Genève, Switzerland: International Organization for Standardization.
International Organization for Standardization, 2003. ISO 13374-1:2003 - Condition Monitoring and Diagnostics of Machines − Data Processing, Communication and Presentation −
Part 1: General Guidelines. Genève, Switzerland: International Organization for Standardization.

International Organization for Standardization, 2004. ISO 13381-1:2004 - Condition Monitoring and Diagnostics of Machines − Prognostics − Part 1: General Guidelines. Genève, Switzerland: International Organization for Standardization.

International Organization for Standardization, 2012. ISO 13372:2012 - Condition Monitoring and Diagnostics of Machines − Vocabulary. Genève, Switzerland: International Organization for Standardization.

Jalali, S. A., & Kolarik, W. J. (1991). Tool Life and Machinability Models for Drilling Steels. International Journal of Machine Tools and Manufacture 31, 273- 282.

Kadirgama, K., Abou-El-Houssein, K. A., Noor, M. M., Sharma, K. V., & Mohammad, B. (2011). Tool life and wear mechanism when machining Hastelloy C-22HS. Wear 270, 258-268.

Liao, L., Minhas, R., Rangarajan, A., Kurtoglu, T., & de Kleer, J. (2014). A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data. Annual Conference of the Prognostics and Health Management Society, September 29-October 2, Fort Worth, TX.

Marvel, J. A. (2014). Collaborative Robotics: A Gateway into Factory Automation. ThomasNet News. http://news.thomasnet.com/IMT/2014/09/03/collaborati ve-robots-a-gateway-into-factory-automation/

Marvel, J. A., Eastman, R., Cheok, G., Saidi, K., Hong, T., & Messina, E. (2012). Technology Readiness Levels for Randomized Bin Picking. Proceedings of the Workshop on Performance Metrics for Intelligent Systems (109-113), March 20-22, College Park, MD. doi:10.1145/2393091.2393114

Marvel, J. A., & Falco, J. A. (2012). Best Practices and Performance Metrics Using Force Control for Robotic Assembly. Gaithersburg, MD: National Institute of Standards and Technology. doi:http://dx.doi.org/10.6028/NIST.IR.7901

Mhenni, F., Nguyen, N., & Choley, J. -Y. (2014). Automatic Fault Tree Generation from SysML System Models. Advanced Intelligent Mechatronics, IEEE/ASME International Conference On (715-720), July 8-11, Besancon, France. doi:10.1109/AIM.2014.6878163

Narasimhan,S., & Brownston, L. (2007). HyDE – A General Framework for Stochastic and Hybrid Model-based Diagnosis. International Workshop on Principles of Diagnosis. May 29-31, Nashville, TN.

National Institute of Standards and Technology. 2015. Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems. National Institute of Standards and Technology.

Orcutt, M. (2014). Manufacturers Adding Robots to the Factory Floor in Record Numbers. MIT Technology Review. http://www.technologyreview.com/graphiti/529971/rob ots-rising/

Peng, Y., Dong, M., & Zuo, M.J. (2010). Current Status of Machine Prognostics in Condition-based Maintenance: a Review. The International Journal of Advanced Manufacturing Technology 50, 297-313.

Philippot, A., Marang, P., Gellot, F., Ptin, J. F., & Riera, B. (2014). Fault Tolerant Control for Manufacturing Discrete Systems by Filter and Diagnoser Interactions. Annual Conference of the Prognostics and Health Management Society, September 29-October 2, Fort Worth, TX.

Sandvik Coromant (2005). Metalcutting Technical Guide: Turning, Milling, Drilling, Boring, Toolholding; Handbook from Sandvik Coromant. Sandvik Coromant.

Shen, T., Wan, F., Cui, W., & Song, B. (2010). Application of Prognostic and Health Management Technology on Aircraft Fuel System. Prognostics and System Health Management Conference, IEEE Computer Society. Macau, China.

Snyder, W. E. (1985). Industrial Robots: Computer Interfacing and Control. Englewood Cliffs, New Jersey: Prentice-Hall.

SAE International (2009). Potential Failure Mode and Effects Analysis in Design (Design FMEA), Potential Failure Mode and Effects Analysis in Manufacturing and Assembly Processes (Process FMEA). J1739_200901, Warrendale, PA: Society of Automotive Engineers International.

Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for Prognostics and Health Management (PHM) Techniques within Manufacturing Operations. Annual Conference of the Prognostics and Health Management Society, September 29-October 2, Fort Worth, TX.

Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards Related to Prognostics and Health Management (PHM) for Manufacturing (Tech. Rep.). Gaithersburg, MD: National Institute of Standards and Technology.

Wünsch, D., Lüder, A., & Heinze, M. (2010). Flexibility and Re-configurability in Manufacturing by Means of Distributed Automation Systems – an Overview. In Kühnle, H., Distributed Manufacturing (51-70). London: Springer. doi:10.1007/978-1-84882-707-3

Yoon, J., He, D., & Van Hecke, B. (2014). A PHM Approach to Additive Manufacturing Equipment Health Monitoring, Fault Diagnosis, and Quality Control. Annual Conference of the Prognostics and Health Management Society, September 29-October 2, Fort Worth, TX.
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Technical Research Papers