fault detection; Robot manipulator; Gaussian Process Regression; Support Vector Machine; Ensemble Random Forest; Narrow Neural Network
Résumé :
[en] In the domain of robotics and automation, the accurate supervision of key parameters is essential to ensure the safe and efficient operation of various systems. This paper presents a comparative study for fault detection based on datadriven approaches to estimate actuator torques subjected to different kind of intermittent faults in a two-degree-offreedom SCARA robot. Different supervised machine learning techniques are used: Gaussian Process Regression with a Matérn 5/2 kernel, Ensemble Random Forest Regression, Support Vector Machine with a medium Gaussian kernel, and a
Narrow Neural Network. Two types of fault scenarios are defined including different evaluation criteria such computation
time, R-squared, Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. The process of model selection involves not only these criteria but also factors like efficiency in both healthy and faulty scenarios, as well as the significant impact of a fault that occurred in one torque on the second torque. Furthermore, our selection is also based on the adequacy between the optimal computation time and model complexity. We conclude that, GPR with a Matérn 5/2 kernel function is the most appropriate one for the estimation compared with others for this type of application.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
ALDRINI, Joma ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Anil Kumar, A.
CHIHI, Ines ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Sidhom, L.
✱ Ces auteurs ont contribué de façon équivalente à la publication.
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Intermittent fault detection for MIMO systems: a case study on SCARA robot
Date de publication/diffusion :
08 décembre 2023
Nom de la manifestation :
ternational Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23)
Lieu de la manifestation :
Inde
Date de la manifestation :
7–8 December 2023
Sur invitation :
Oui
Manifestation à portée :
International
Titre du périodique :
IET Computer Vision
ISSN :
1751-9632
eISSN :
1751-9640
Maison d'édition :
Institution of Engineering and Technology, Royaume-Uni
Y. Zhong, W. Zhang, Y. Zhang, J. Zuo, and H. Zhan, “Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter,” J. Intell. Robot. Syst., vol. 96, no. 3-4, pp. 555-572, Dec. 2019, doi: 10.1007/s10846-019-01002-4.
B. Abci, M. El Badaoui El Najjar, V. Cocquempot, and G. Dherbomez, “An informational approach for sensor and actuator fault diagnosis for autonomous mobile robots,” J. Intell. Robot. Syst., vol. 99, no. 2, pp. 387-406, Aug. 2020, doi: 10.1007/s10846-019-01099-7.
O. Sergiyenko and A. Zhirabok, “Fault Identification in Mobile Robot Groups Using Sliding Mode Observers,” Program. Comput. Softw., vol. 46, no. 8, pp. 679-688, Dec. 2020, doi: 10.1134/S0361768820080216.
M. A.Alobaidy, Dr. J. Abdul-Jabbar, and S. Alkhayyt, “Faults Diagnosis in Robot Systems: A Review,” Al-Rafidain Eng. J. AREJ, vol. 25, no. 2, pp. 166-177, Dec. 2020, doi: 10.33899/rengj.2020.127782.1051.
J. Aldrini, I. Chihi, and L. Sidhom, “Fault diagnosis and self-healing for smart manufacturing: a review,” J. Intell. Manuf., Jul. 2023, doi: 10.1007/s10845-023-02165-6.
G. K. Fourlas, S. Karkanis, G. C. Karras, and K. J. Kyriakopoulos, “Model Ǻased Actuator Fault Diagnosis for a Mobile Robot”.
H.-J. Ma and G.-H. Yang, “Simultaneous fault diagnosis for robot manipulators with actuator and sensor faults,” Inf. Sci., vol. 366, pp. 12-30, Oct. 2016, doi: 10.1016/j.ins.2016.05.016.
K. O. Omali, M. N. Kabbaj, and M. Benbrahim, “Sensor fault detection and isolation for robot manipulator,” presented at the 2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, ICAM'2018, Fez, Morocco, 2019, p. 020023. doi: 10.1063/1.5090640.
M. Hu, J. Wu, J. Yang, L. Zhang, and F. Yang, “Fault diagnosis of robot joint based on BP neural network,” Robotica, vol. 40, no. 12, pp. 4388-4404, Dec. 2022, doi: 10.1017/S0263574722000984.
T. Wang, L. Zhang, and X. Wang, “Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers,” CES Trans. Electr. Mach. Syst., vol. 7, no. 2, pp. 144-152, Jun. 2023, doi: 10.30941/CESTEMS.2023.00014.
I. Raouf, P. Kumar, H. Lee, and H. S. Kim, “Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System,” Mathematics, vol. 11, no. 4, p. 945, Feb. 2023, doi: 10.3390/math11040945.
L. Sidhom and I. Chihi, “Fault actuators detection based on inputs-outputs data mapping: application to a robot manipulator,” Int. J. Adv. Mechatron. Syst., vol. 7, no. 5, p. 328, 2017, doi: 10.1504/IJAMECHS.2017.095891.
Z. El Mrabet, N. Sugunaraj, P. Ranganathan, and S. Abhyankar, “Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems,” Sensors, vol. 22, no. 2, p. 458, Jan. 2022, doi: 10.3390/s22020458.
R. K. Patel and V. K. Giri, “Feature selection and classification of mechanical fault of an induction motor using random forest classifier,” Perspect. Sci., vol. 8, pp. 334-337, Sep. 2016, doi: 10.1016/j.pisc.2016.04.068.
S. S. Nagam, R. K. Panda, A. Mohapatra, and S. Anand, “Gaussian Process Regression based Fault Location in DC Microgrid,” in 2020 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA: IEEE, Feb. 2020, pp. 1-6. doi: 10.1109/TPEC48276.2020.9042542.
A. Srivastava and S. K. Parida, “Data driven approach for fault detection and Gaussian process regression based location prognosis in smart AC microgrid,” Electr. Power Syst. Res., vol. 208, p. 107889, Jul. 2022, doi: 10.1016/j.epsr.2022.107889.
M. Noori, H. Hassani, A. Javaherian, H. Amindavar, and S. Torabi, “Automatic fault detection in seismic data using Gaussian process regression,” J. Appl. Geophys., vol. 163, pp. 117-131, Apr. 2019, doi: 10.1016/j.jappgeo.2019.02.018.
D. L. De Souza, M. H. Granzotto, G. M. De Almeida, and L. C. Oliveira-Lopes, “Fault Detection and Diagnosis Using Support Vector Machines - A SVC and SVR Comparison,” J. Saf. Eng., vol. 3, no. 1, pp. 18-29, Jun. 2014, doi: 10.5923/j.safety.20140301.03.
P. Venkata, V. Pandya, K. Vala, and A. V. Sant, “Support vector machine for fast fault detection and classification in modern power systems using quarter cycle data,” Energy Rep., vol. 8, pp. 92-98, Dec. 2022, doi: 10.1016/j.egyr.2022.10.279.
D. Maincer, Y. Benmahamed, M. Mansour, M. Alharthi, and S. S. M. Ghonein, “Fault Diagnosis in Robot Manipulators Using SVM and KNN,” Intell. Autom. Soft Comput., vol. 35, no. 2, pp. 1957-1969, 2023, doi: 10.32604/iasc.2023.029210.
A. Bhuyan, B. K. Panigrahi, K. Pal, and S. Pati, “Convolutional Neural Network Based Fault Detection for Transmission Line,” in 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India: IEEE, Jul. 2022, pp. 1-4. doi: 10.1109/ICICCSP53532.2022.9862446.
A. Hichri et al., “Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems,” Sustainability, vol. 14, no. 17, p. 10518, Aug. 2022, doi: 10.3390/su141710518.
C. N. Cho, J. T. Hong, and H. J. Kim, “Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators,” J. Intell. Robot. Syst., vol. 95, no. 1, pp. 137-147, Jul. 2019, doi: 10.1007/s10846-018-0781-0.
