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Incipient fault diagnosis for robot manipulators based on evolution of friction characteristics in transmission components

Published online by Cambridge University Press:  16 September 2024

Xing Zhou ,
Shifeng Huang ,
Yaoqi Xian Open the ORCID record for Yaoqi Xian [Opens in a new window] ,
Huicheng Zhou ,
Wenbin Deng Open the ORCID record for Wenbin Deng [Opens in a new window]  and
Jian Zhou
Xing Zhou
Affiliation:
National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology, Wuhan, China Foshan Institute of Intelligent Equipment Technology, Foshan, China
Shifeng Huang
Affiliation:
National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology, Wuhan, China Foshan Institute of Intelligent Equipment Technology, Foshan, China
Yaoqi Xian*
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan, China
Huicheng Zhou
Affiliation:
National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology, Wuhan, China
Wenbin Deng
Affiliation:
Huashu Robot Co., Ltd., Foshan, China
Jian Zhou
Affiliation:
China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, China
*
Corresponding author: Yaoqi Xian; Email: [email protected]
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Abstract

The tribological behavior can be informative about the incipient faults of robot manipulators. This study explores the evolution of friction characteristics from cold start to thermal equilibrium through a series of steady-state friction experiments. Based on these experimental observations, a friction-based fault diagnosis framework is proposed. The fault diagnosis process primarily involves defining the healthy state, decomposing friction curves and their features, and anomaly detection. Given the dependence of friction characteristics on different sources of faults, the parameters of steady-state experimental friction model are divided into two categories: one associated with contact interactions and the other related to non-contact regimes. Subsequently, confidence regions corresponding to distinguishable friction characteristics are independently constructed. These regions encapsulate the statistical description of the healthy state, characterized by mean values and the covariance of the friction characteristic parameter vectors during the unloaded state. In addition, we conduct experiments that consider the influence of applied loads on friction behavior. These experiments serve as a test set for comparison against nominal statistics. Leveraging the similarity between the effects of wear and load on friction, we introduce equivalent load thresholds to assess the severity of joint degradation. The results demonstrate the feasibility of employing confidence region views based on friction characteristic classification for fault detection and isolation.

Keywords

robot manipulatorfrictionfault diagnosis
Type
Research Article
Information
Robotica , First View , pp. 1 - 19
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Aivaliotis, P., Arkouli, Z., Georgoulias, K. and Makris, S., “Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots,” Robot Com Int Manuf 71, 102177 (2021).Google Scholar
Abid, A., Khan, M. T. and Iqbal, J., “A review on fault detection and diagnosis techniques: Basics and beyond,” Artif Intell Rev 54(5), 3639–3664 (2021).Google Scholar
Isermann, R.. Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems (Springer Science & Business Media, Berlin, Heidelberg, 2011).Google Scholar
Khalastchi, E. and Kalech, M., “On fault detection and diagnosis in robotic systems,” ACM Comput Surv 51, 1–24 (2018).Google Scholar
Yuan, Y., Ma, G., Cheng, C., Zhou, B., Zhao, H., Zhang, H.-T. and Ding, H., “A general end-to-end diagnosis framework for manufacturing systems,” Natl Sci Rev 7(2), 418–429 (2020).Google ScholarPubMed
Yin, S., Ding, S. X., Xie, X. and Luo, H., “A review on basic data-driven approaches for industrial process monitoring,” IEEE Trans Ind Electron 61(11), 6418–6428 (2014).Google Scholar
Chen, Z., Mauricio, A., Li, W. and Gryllias, K., “A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks,” Mech Syst Signal Process 140, 106683 (2020).Google Scholar
Jia, F., Lei, Y., Lu, N. and Xing, S., “Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization,” Mech Syst Signal Process 110, 349–367 (2018).Google Scholar
Ince, T., Kiranyaz, S., Eren, L., Askar, M. and Gabbouj, M., “Real-time motor fault detection by 1-D convolutional neural networks,” IEEE Trans Ind Electron 63(11), 7067–7075 (2016).Google Scholar
Liu, R., Yang, B., Zio, E. and Chen, X., “Artificial intelligence for fault diagnosis of rotating machinery: A review,” Mech Syst Signal Process 108, 33–47 (2018).Google Scholar
Vemuri, A. T. and Polycarpou, M. M., “A methodology for fault diagnosis in robotic systems using neural networks,” Robotica 22(4), 419–438 (2004).Google Scholar
Hu, M., Wu, J., Yang, J., Zhang, L. and Yang, F., “Fault diagnosis of robot joint based on BP neural network,” Robotica 40(12), 4388–4404 (2022).Google Scholar
Zhou, X., Zhou, H., He, Y., Huang, S., Zhu, Z. and Chen, J., “Harmonic reducer in-situ fault diagnosis for industrial robots based on deep learning,” Science China Technological Sciences 65(9), 2116–2126 (2022).Google Scholar
He, Y., Chen, J., Zhou, X. and Huang, S., “In-situ fault diagnosis for the harmonic reducer of industrial robots via multi-scale mixed convolutional neural networks,” J Manuf Syst 66, 233–247 (2023).Google Scholar
Zhou, X., Zeng, H., Chen, C., Xiao, H. and Xiang, Z., “An attention-enhanced multi-modal deep learning algorithm for robotic compound fault diagnosis,” Measure Sci Technol 34(1), 014007 (2022).Google Scholar
McIntyre, M. L., Dixon, W. E., Dawson, D. M. and Walker, I. D., “Fault identification for robot manipulators,” IEEE Trans Robot 21(5), 1028–1034 (2005).Google Scholar
Muradore, R. and Fiorini, P., “A PLS-based statistical approach for fault detection and isolation of robotic manipulators,” IEEE Trans Ind Electron 59(8), 3167–3175 (2011).Google Scholar
Dixon, W. E., Walker, I. D., Dawson, D. M. and Hartranft, J. P., “Fault detection for robot manipulators with parametric uncertainty: A prediction-error-based approach,” IEEE Trans Robot Autom 16(6), 689–699 (2000).Google Scholar
Namvar, M. and Aghili, F., “Failure detection and isolation in robotic manipulators using joint torque sensors,” Robotica 28(4), 549–561 (2010).Google Scholar
Freyermuth, B.. “An Approach to Model Based Fault Diagnosis of Industrial Robots,” In: Proceedings. 1991 IEEE International Conference on Robotics and Automation, (IEEE Computer Society, 1991) pp. 1350–1356.Google Scholar
Bittencourt, A. C., Axelsson, P., Jung, Y. and Brogårdh, T., “Modeling and identification of wear in a robot joint under temperature uncertainties,” IFAC Proc Vol 44, 10293–10299 (2011).Google Scholar
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P. and Sun, Y., “A Review on Degradation Models in Reliability Analysis, Engineering Asset Lifecycle Management,” In: Proceedings of the 4th World Congress on Engineering Asset Management (WCEAM 2009), (Springer, 2010) pp. 369–384.Google Scholar
Siciliano, B., Khatib, O. and Kröger, T.. Springer Handbook of Robotics (Springer, Cham, Switzerland, 2008).Google Scholar
Huang, S., Gao, M., Liu, L., Chen, J. and Zhang, J., “Collision detection for cobots: A back-input compensation approach,” IEEE/ASME Trans Mechatron 27(6), 4951–4962 (2022).Google Scholar
Bittencourt, A. C., Wernholt, E., Sander-Tavallaey, S. and Brogårdh, T., “An Extended Friction Model to Capture Load and Temperature Effects in Robot Joints,” In: 2010 IEEE/RSJ international conference on intelligent robots and systems, (IEEE, 2010) pp. 6161–6167.Google Scholar
Bittencourt, A. C. and Gunnarsson, S., “Static friction in a robot joint—modeling and identification of load and temperature effects,” J Dyn Syst Meas Control 134(5), 051013 (2012).Google Scholar
Bittencourt, A. C. and Axelsson, P., “Modeling and experiment design for identification of wear in a robot joint under load and temperature uncertainties based on friction data,” IEEE/ASME Trans Mechatron 19, 1694–1706 (2013).Google Scholar
Al-Bender, F. and Swevers, J., “Characterization of friction force dynamics,” IEEE Contr Syst Mag 28(6), 64–81 (2008).Google Scholar
Johanastrom, K. and Canudas-De-Wit, C., “Revisiting the LuGre friction model,” IEEE Contr Syst Mag 28, 101–114 (2008).Google Scholar
Stribeck, R., “Die wesentlichen eigenschaften der gleit-und rollenlager,” Z Ver Dtsch Ing 46, 1341–1348 (1902).1432–1438, 1463–1470.Google Scholar
Marton, L. and Lantos, B., “Friction and Backlash Measurement and Identification Method for Robotic Arms,” In: International Conference on Advanced Robotics, (IEEE, 2009) pp. 1–6.Google Scholar
De Wit, C. C. and Carrillo, J., “A modified EW-RLS algorithm for systems with bounded disturbances,” Automatica 26(3), 599–606 (1990).Google Scholar
Armstrong-Helouvry, B.. Control of Machines with Friction (Springer Science & Business Media, New York, 2012).Google Scholar
Waiboer, R., Aarts, R. and Jonker, B., “Velocity Dependence of Joint Friction in Robotic Manipulators with Gear Transmissions,” In: ECCOMAS Thematic Conference Multibody Dynamics, (2005) pp. 1–19.Google Scholar
Iskandar, M. and Wolf, S., “Dynamic Friction Model with Thermal and Load Dependency: Modeling, Compensation, and External Force estimation,” In: International Conference on Robotics and Automation (ICRA), (IEEE, 2019) pp. 7367–7373.Google Scholar
Pagani, R., Legnani, G., Incerti, G., Beschi, M. and Tiboni, M., “The Influence of Heat Exchanges on Friction in Robotic Joints: Theoretical Modelling, Identification and Experiments,” In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, (American Society of Mechanical Engineers, 2020) pp. V010T010A053.Google Scholar
Golub, G. H. and Pereyra, V., “The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate,” SIAM J Numer Anal 10(2), 413–432 (1973).Google Scholar
De Maesschalck, R., Jouan-Rimbaud, D. and Massart, D. L., “The mahalanobis distance,” Chemometr Intell Lab Syst 50(1), 1–18 (2000).Google Scholar
Bittencourt, A. C.. Modeling and Diagnosis of Friction and Wear in Industrial Robots (Linköping University Electronic Press, Sweden, 2014).Google Scholar