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Observations on developing reliability information utilization in a manufacturing environment with case study: robotic arm manipulators

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Abstract

Manufacturing environments face many unique challenges with regard to balancing high standards of both product quality and production efficiency. Proper diagnostic health assessment is essential for maximizing uptime and maintaining product and process quality. Information for diagnostic assessments, and reliability information in general, can come from a myriad of sources that can be processed and managed through numerous algorithms that range from simplistic to hypercomplex. One area that typifies the assortment of information sources in a modern manufacturing setting is found with the use of industrial robotics and automated manipulators. Although several monitoring methods and technologies have been previously proposed for this and other assets, adoption has been sporadic with returns on investment not always meeting expectations. Practical concerns regarding data limitations, variability of setup, and scarcity of ground truth points of validation from active industrial sites have contributed to this. This paper seeks to provide an overview of barriers and offer a feasible action plan for developing a practical condition monitoring information utilization program, matching available capabilities and assets to maximize knowledge gain. Observations are made on a real-world case study involving industrial 6 degrees of freedom (DOF) robots actively deployed in a manufacturing facility with a variety of operational tasks.

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References

  1. Li R, Verhagen W, Curran R (2018) A functional architecture of prognostics and health management using a systems engineering approach. In: European conference of the prognostics and health management society

  2. Saxena A, Roychoudhury I, Celaya J, Goebel K (2012) Requirements flowdown for prognostics and health management. In: AIAA Infotech at aerospace conference and exhibit 2012, p 13. https://doi.org/10.2514/6.2012-2554

  3. Vogl GW, Weiss BA, Moneer Helu O (2016) A review of diagnostic and prognostic capabilities and best practices for manufacturing. J Intell Manuf 0:1–17

    Google Scholar 

  4. Lee G-Y, Kim M, Quan Y-J, Kim M-S, Kim TJY, Yoon H-S, Min S, Kim D-H, Mun J-W, Oh JW, ang Chung-Soo Kim IGC, Chu W-S, Yang J, Bhandari B, Lee C-M, IHn J-B, Ahn S-H (2018) Machine health management in smart factory: a review. J Mech Sci Technol 32(3):987–1009

    Article  Google Scholar 

  5. Moa K, Chen Z, Tao X, Xue Q, Wu H, Hou J (2017) A visual model based evaluation framework of cloud-based prognostics and health management. In: IEEE international conference. Smart Cloud, pp 33–40

  6. Wu D, Jennings C, Terpenny J, Kumara S, Gao RX (2018) Cloud-based parallel machine learning for tool wear prediction. Journal of Manufacturing Science and Engineering.

  7. Tountopoulos V, Kavakli E, Sakellariou R (2018) Towards a cloud-based controller for data-driven service orchestration in smart manufacturing. In: Sixth international conference on enterprise systems

  8. Specht R, Isermann R (1988) On-line identification of inertia, friction and gravitational forces applied to an industrial robot. IFAC Proceedings Volumes 21(16):219–224

    Article  Google Scholar 

  9. Mitsuishi M (1989) Diagnostic system for robot using a force-torque sensor. Robotersysteme 5:40–46

    Google Scholar 

  10. Isermann R (1990) Estimation of physical parameters for dynamic processes with application to an industrial robot. 1990 American control conference

  11. Wünnenberg J, Frank P (1990) Dynamic model based incipient fault detection concept for robots. IFAC Proceedings Volumes 23(8):61–66

    Article  Google Scholar 

  12. Freyermuth B (1991) An approach to model based fault diagnosis of industrial robots. Proceedings. 1991 IEEE International Conference on Robotics and Automation 2:1350–1356

    Article  Google Scholar 

  13. Zhou C, Chinnam RB, Korostelev A (2012) Hazard rate models for early detection of reliability problems using information from warranty databases and upstream supply chain. Int J Prod Econ 139(1):180–195. Supply Chain Risk Management. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S092552731200165X

    Article  Google Scholar 

  14. Vemuri A, Polycarpou M, Diakourtis S (1998) Neural network based fault detection in robotic manipulators. IEEE Trans Robot Autom 14(2):342–348

    Article  Google Scholar 

  15. Datta A, Mavroidis C, Krishnasamy J, Hosek M (2007) Neural netowrk based fault diagnostics of industrial robots using wavelt multi-resolution analysis. 2007 American control conference

  16. Bittencourt AC, Saarinen K, Sander-Tavallaey S, Gunnarsson S, Norrlöf M (2014) A data-driven approach to diagnostics of repetitive processes in the distribution domain – applications to gearbox diagnostics in industrial robots and rotating machines. Mechatronics 24(8):1032–1041

    Article  Google Scholar 

  17. Jaber AA, Bicker R (2018) Development of a condition monitoring algorithm for industrial robots based on artificial intelligence and signal processing techniques. International Journal of Electrical and Computer Engineering (IJECE) 8(2):996

    Article  Google Scholar 

  18. Kothamasu R, Huang S, VerDuin W (2006) System health monitoring and prognostics — a review of current paradigms and practices. Int J Adv Manuf Technol 24(28):1012–1024

    Article  Google Scholar 

  19. Huang N, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995

    Article  MathSciNet  MATH  Google Scholar 

  20. Prohorov Y, Rozanov Y (1969) Probability theory, basic concepts. Limit theorems, random processes. Springer, Berlin

    MATH  Google Scholar 

  21. Xia M, Li T, Liu L, Xu L, Silva CWD (2017) Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder. IET Sci Meas Technol 11(6):687–695

    Article  Google Scholar 

  22. Nadaraya EA (1964) On estimating regression. Theory of Probability and its Applications 9(1):141–142. [Online]. Available: https://epubs.siam.org/doi/abs/10.1137/1109020

    Article  MATH  Google Scholar 

  23. Sculley D (2010) Web-scale k-means clustering. In: Proceedings of the 19th international conference on world wide web - WWW, p 10

  24. Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–441 and 498–520

    Article  MATH  Google Scholar 

  25. Golub GH, Reinsch C (1970) Singular value decomposition and least squares solutions. Numer Math 14(5):403–420

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Michael Sharp.

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Sharp, M. Observations on developing reliability information utilization in a manufacturing environment with case study: robotic arm manipulators. Int J Adv Manuf Technol 102, 3243–3264 (2019). https://doi.org/10.1007/s00170-018-03263-z

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  • DOI: https://doi.org/10.1007/s00170-018-03263-z

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