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Sensitivity analysis of factors affecting motion reliability of manipulator and fault diagnosis based on kernel principal component analysis

Published online by Cambridge University Press:  10 December 2021

Jing Yang Open the ORCID record for Jing Yang [Opens in a new window] ,
Lingyan Jin ,
Zejie Han ,
Deming Zhao  and
Ming Hu
Jing Yang
Affiliation:
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China
Lingyan Jin
Affiliation:
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China
Zejie Han
Affiliation:
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China
Deming Zhao
Affiliation:
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China
Ming Hu*
Affiliation:
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China
*
*Corresponding author. E-mail: huming@zstu.edu.cn
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Abstract

As an important index to quantitatively measure the motion performance of a manipulator, motion reliability is affected by many factors, such as joint clearance. The present research utilized a UR10 manipulator as the research object. A factor mapping model for influencing the motion reliability was established. The link flexibility factor, joint flexibility factor, joint clearance factor, and Denavit–Hartenberg (DH) parameters were comprehensively considered in this model. The coupling relationship among the various factors was concisely expressed. Subsequently, the nonlinear response surface method was used to calculate the reliability and sensitivity of the manipulator, which provided an applicable reference for its trajectory planning and motion control. In addition, a data-driven fault diagnosis method based on the kernel principal component analysis (KPCA) was used to verify the motion accuracy and sensitivity of the manipulator, and joint rotation failure was considered as an example to verify the accuracy of the KPCA method. This study on the motion reliability of the manipulator is of great significance for the current motion performance, adjusting the control strategy and optimizing the completion effect of the motion task of a manipulator.

Keywords

manipulatormotion reliabilitysensitivity analysisfault diagnosiskernel principal component analysis
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2547 - 2566
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Sun, D. and Chen, G., “Kinematic accuracy analysis of planar mechanisms with clearance involving random and epistemic uncertainty,” Eur. J. Mech. A/Solids 58, 256–261 (2016).CrossRefGoogle Scholar
Nazari, V. and Notash, L., “Motion analysis of manipulators with uncertainty in kinematic parameters,” J. Mech. Robot. 8(2), 021014 (2016).CrossRefGoogle Scholar
Sun, Y., Liu, Y. and Liu, H., “Temperature compensation for a Six-Axis Force/Torque Sensor based on the particle Swarm optimization least square support vector machine for space manipulator,” IEEE Sens. J. 16(3), 798–805 (2016).CrossRefGoogle Scholar
Wu, J., Zhang, D., Liu, J. and Han, X., “A computational framework of kinematic accuracy reliability analysis for industrial robots,” Appl. Math. Modell. 82, 189–216 (2020).CrossRefGoogle Scholar
Jawale, H. P. and Thorat, H. T., “Positional error estimation in serial link manipulator under joint clearances and backlash,” J. Mech. Robot 5(2), 2–8 (2013).CrossRefGoogle Scholar
Jia, Q., Li, T., Chen, G., Sun, H. and Zhang, J., “Sensitivity analysis on factors influencing motion reliability of space manipulator based on the multilayer mapping model,” J. Mech. Eng. 53(11), 10–19 (2017).CrossRefGoogle Scholar
Kim, J., Song, W. J. and Kang, B. S., “Stochastic approach to kinematic reliability of open-loop mechanism with dimensional tolerance,” Appl. Math. Model 34(5), 1225–1237 (2010).CrossRefGoogle Scholar
Pandey, M. D. and Zhang, X., “System reliability analysis of the robotic manipulator with random joint clearances,” Mech. Mach. Theory 58, 137–152 (2012).CrossRefGoogle Scholar
Du, X., “Time-dependent mechanism reliability analysis with envelope functions and first-order approximation,” J. Mech. Des. Trans. ASME, 136(8), 1–7 (2014).CrossRefGoogle Scholar
Zhang, J. and Du, X., “Time-dependent reliability analysis for function generation mechanisms with random joint clearances,” Mech. Mach. Theory 92, 184–199 (2015).CrossRefGoogle Scholar
Zhao, J., Yan, S. and Wu, J., “Analysis of parameter sensitivity of space manipulator with harmonic drive based on the revised response surface method,” Acta Astronaut. 98(1), 86–96 (2014).CrossRefGoogle Scholar
Yu, S. and Wang, Z., “A novel time-variant reliability analysis method based on failure processes decomposition for dynamic uncertain structures,” J. Mech. Design, Trans. ASME 140(5), 1–11 (2018).CrossRefGoogle Scholar
Yu, S. and Wang, Z., “A general decoupling approach for time- and space-variant system reliability-based design optimization,” Comput. Methods Appl. Mech. Eng. 357, 112608 (2019).CrossRefGoogle Scholar
Zhang, J., Xiao, M., Gao, L. and Chu, S., “A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities,” Comput. Methods Appl. Mech. Eng. 344, 13–33 (2019).CrossRefGoogle Scholar
Wu, J., Zhang, D., Liu, J. and Han, X., “A moment approach to positioning accuracy reliability analysis for industrial robots,” IEEE Trans. Reliabil. 69(2), 699–714 (2020).CrossRefGoogle Scholar
Van, M., Kang, H. J., Suh, Y. S. and Shin, K. S., “A robust fault diagnosis and accommodation scheme for robot manipulators,” Int. J. Control. Autom. Syst. 11(2), 377–388 (2013).CrossRefGoogle Scholar
Zhang, J., Xiao, M., Gao, L. and Fu, J., “A novel projection outline based active learning method and its combination with Kriging meta model for hybrid reliability analysis with random and interval variables,” Comput. Methods Appl. Mech. Eng. 341, 32–52 (2018).CrossRefGoogle Scholar
Zhang, J., Wang, J. and Du, X., “Time-dependent probabilistic synthesis for function generator mechanisms,” Mech. Mach. Theory 46(9), 1236–1250 (2011).CrossRefGoogle Scholar
Korayem, A. H., Azimirad, V., Binabaji, H. and Korayem, M. H., “Sensitivity analysis of flexible joint nonholonomic wheeled mobile manipulator in singular configuration,” Acta Astronaut. 72, 62–77 (2012).CrossRefGoogle Scholar
Siyu, C., Jinyuan, T., Caiwang, L. and Qibo, W., “Nonlinear dynamic characteristics of geared rotor bearing systems with dynamic backlash and friction,” Mech. Mach. Theory 46(4), 466–478 (2011).CrossRefGoogle Scholar
Erkaya, S., “Effects of joint clearance on the motion accuracy of robotic manipulators,” J. Mech. Eng., 64(2), 82–94 (2018).Google Scholar
Pandey, M. D. and Zhang, X., “System reliability analysis of the robotic manipulator with random joint clearances,” Mech. Mach. Theory 58, 137–152 (2012).CrossRefGoogle Scholar
Ping, M. H., Han, X., Jiang, C. and Xiao, X. Y., “A time-variant extreme-value event evolution method for time-variant reliability analysis,” Mech. Syst. Signal Process. 130, 333–348 (2019).CrossRefGoogle Scholar
Jiang, C., Qiu, H., Gao, L., Wang, D., Yang, Z. and Chen, L., “Real-time estimation error-guided active learning Kriging method for time-dependent reliability analysis,” Appl. Math. Modell. 77, 82–98 (2020).CrossRefGoogle Scholar
Wu, J., Zhang, D., Liu, J. and Han, X., “A moment approach to positioning accuracy reliability analysis for industrial robots,” IEEE Trans. Reliabil. 69(2), 699–714 (2020).CrossRefGoogle Scholar
Bucher, C. G. and Bourgund, U., “A fast and efficient response surface approach for structural reliability problems,” Struct. Saf. 7(1), 57–66 (1990).CrossRefGoogle Scholar
Chen, S., Tang, J., Luo, C. and Wang, Q. B., “Nonlinear dynamic characteristics of geared rotor bearing systems with dynamic backlash and friction,” Mech. Mach. Theory 46(4), 466–478 (2011).Google Scholar
Yue, H. H. and Qin, S. J., “Reconstruction-based fault identification using a combined index,” Indust. Eng. Chem. Res. 40(20), 4403–4414 (2001).CrossRefGoogle Scholar