Fault detection in robots based on discrete wavelet transformation and eigenvalue of energy
 
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1
Department of Electrical Engineering, University of Biskra,Biskra, Algeria
 
2
College of Engineering, Royal University for Women,Bahrain
 
3
Department of Electrical Engineering, University of Mohamed Boudiaf, M’Sila, Algeria
 
 
Submission date: 2023-06-07
 
 
Final revision date: 2023-09-18
 
 
Acceptance date: 2023-10-13
 
 
Online publication date: 2023-10-23
 
 
Publication date: 2023-10-23
 
 
Corresponding author
Saloua Ouarhlent   

Department of Electrical Engineering, University of Biskra,Biskra, Algeria
 
 
Diagnostyka 2023;24(4):2023407
 
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ABSTRACT
This article addresses the problem of fault detection in robot manipulator systems. In the production field, online detection and prevention of unexpected robot stops avoids disruption to the entire manufacturing line. A number of researchers have proposed fault diagnosis architectures for electrical systems such as induction motor, DC motor, etc..., utilizing the technique of discrete wavelet transform (DWT). The results obtained from the use of DWT coefficient analysis in the field of diagnosis are very encouraging. Inspired by previous work, The objective of this paper is to present a methodology that enables accurate fault detection in the actuator of a 2 DOF robot arm to avoid system performance degradation; a partial reduction in joint torque constitutes the actuator fault, resulting in a deviation from the desired end-effector motion. The actuator fault detection is carried out by analysing the torques signals using DWT. The stored energy at each level of the DWT contains information which can be used as a fault indicator. The Matlab/Simulink simulation on the manipulator robot demonstrates the effectiveness of the proposed technique.
 
REFERENCES (31)
1.
Patton R., Uppal F, Lopez-Toribio C. Soft computing approaches to fault diagnosis for dynamic systems: a survey. IFAC Proceedings Volumes 2000; 33(11): 303-315. https://doi.org/10.1016/S1474-....
 
2.
Alobaidy MAA, Abdul-Jabbar JM, Al-khayyt SZ. Faults diagnosis in robot systems: a review. Al-Rafidain Engineering Journal (AREJ) 2020; 25(2): 164-175. https://doi.org/10.33899/rengj....
 
3.
Jaber AA, Bicker R. Industrial robot backlash fault diagnosis based on discrete wavelet transform and artificial neural network. American Journal of Mechanical Engineering 2016; 4(1): 21-31. http://dx.doi.org/10.12691/ajm....
 
4.
Ameid T, Menacer A, Talhaoui H, Azzoug Y. Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive. ISA transactions 2018; 79: 217-231. https://doi.org/10.1016/j.isat....
 
5.
Yang WY. Signals and Systems with MATLAB: Springer Science & Business Media 2009.
 
6.
Mallat S. A wavelet tour of signal processing. 1999: Elsevier.
 
7.
Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence 1989; 11(7): 674-693. https://doi.org/10.1109/34.192....
 
8.
Jaber A, Bicker R. Industrial robot fault detection based on wavelet transform and LabVIEW. First International Conference on Systems Informatics, Modelling and Simulations. School of Mechanical and System Engineering, Newcastle University, UK 2014. http://dx.doi.org/10.1109/SIMS....
 
9.
Bae H, Kim YT, Kim S, Lee S, Wang BH. Fault detection of induction motors using fourier and wavelet analysis. Journal of Advanced Computational Intelligence and Intelligent Informatics 2004; 8(4): 431-436.
 
10.
Baig S, Farrukh F, Mughal M. Discrete wavelet transforms-algorithms and applications. Intech 2011.
 
11.
Freddi A Longhi S, Monteriu A, Ortenzi D, Proietti Pagnotta D. Fault tolerant control scheme for robotic manipulators affected by torque faults. IFAC-PapersOnLine 2018; 51(24): 886-893.
 
12.
Kmelnitsky VM. Automated On-line Diagnosis and Control Configuration in Robotic Systems Using Model Based Analytical Redundancy. Worcester Polytechnic Institute 2002.
 
13.
Leuschen ML, Walker ID, Cavallaro JR. Investigation of reliability of hydraulic-robots for hazardous environments using analytic redundancy. Annual Reliability and Maintainability. Symposium 1999 Proceedings (Cat. No. 99CH36283) 1999. https://doi.org/10.1109/RAMS.1....
 
14.
Spong MW, Hutchinson S, Vidyasagar M. Robot modeling and control. John Wiley & Sons 2020.
 
15.
Jaber AA, Design of an intelligent embedded system for condition monitoring of an industrial robot. Springer 2016.
 
16.
Ngui WK, Salman Leong M, Hee LM, Abdelrhman AM. Wavelet analysis: mother wavelet selection methods. Applied mechanics and materials 2013; 393: 953-958. http://dx.doi.org/10.4028/www.....
 
17.
Walker JS. A primer on wavelets and their scientific applications. CRC press 2008.
 
18.
Hariharan G, Kannan K. Review of wavelet methods for the solution of reaction–diffusion problems in science and engineering. Applied Mathematical Modelling 2014; 38(3): 799-813. https://doi.org/10.1016/j.apm.....
 
19.
Lee B. Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. The International Journal of Advanced Manufacturing Technology 1999; 15(4): 238-243. https://doi.org/10.1007/s00170....
 
20.
Guechi E-H, et al., MPC control and LQ optimal control of a two-link robot arm: A comparative study. Machines 2018; 6(3): 37. https://doi.org/10.3390/machin....
 
21.
Patel VVJR. Ziegler-Nichols Tuning Method: Understanding the PID Controller. Resonance 2020; 25(10): 1385-1397. http://dx.doi.org/10.1007/s120....
 
22.
Utami AR, Yuniar RJ, Giyantara A, Saputra AD. Cohen-Coon PID tuning method for self-balancing robot. 2022 International Symposium on Electronics and Smart Devices (ISESD) 2022. https://doi.org/10.1109/ISESD5....
 
23.
Sahrir NH, Mohd Basri MA. Modelling and manual tuning PID control of quadcopter. Control, instrumentation and mechatronics: theory and practice 2022; 921: 346-357. https://doi.org/10.1007/978-98....
 
24.
Zárate-Ramos J, Rodríguez-Hernández J, Cruz-Domínguez J, Nieto-Gutiérrez N, Sánchez-López C. Arbitrary order PID controller design for an inverted pendulum system. 2023 International Conference on Fractional Differentiation and Its Applications (ICFDA) 2023. https://doi.org/10.1109/ICFDA5....
 
25.
Bakošová M, Oravec J, Čirka Ľ. Software for PID controller tuning. V Proceedings of the 17th Interantional Conference on Porcess Control’09, FCFT SUT in Bratislava 2009.
 
26.
Bucz S, Kozakova A. Advanced methods of PID controller tuning for specified performance. PID Control for Industrial Processes: Books 2018.
 
27.
Sathish Kumar A, Naveen S, Vijayakumar R, Suresh V, Asary AR, Madhu S, Palani K. An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications. Scientific Reports 2023; 13(1): 8253. https://doi.org/10.1038/s41598....
 
28.
Dachang Z. Baolin D, Puchen Z, Shouyan C. Constant force PID control for robotic manipulator based on fuzzy neural network algorithm. Complexity 2020; 2020: 1-11. https://doi.org/10.1155/2020/3....
 
29.
Autsou S, Rassolkin A, Vaimann T, Kudelina K. Analysis of possible faults and diagnostic methods of the Cartesian industrial robot. Proceedings od the Estonian Academy of Sciences 2022; 71(3): 227-240. https://doi.org/10.3176/proc.2....
 
30.
Ouamara D, Boukhnifer M, Chaibet A, Maidi A. Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis. Diagnostyka 2023; 24(1): 2023113. https://doi.org/10.29354/diag/....
 
31.
Halder S, Bhat S, Zychma D, Sowa P. Broken rotor bar fault diagnosis techniques based on motor current signature analysis for induction motor—A review. Energies 2022; 15(22): 8569. https://doi.org/10.3390/en1522....
 
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