American Journal of Mechanical Engineering
ISSN (Print): 2328-4102 ISSN (Online): 2328-4110 Website: http://www.sciepub.com/journal/ajme Editor-in-chief: Kambiz Ebrahimi, Dr. SRINIVASA VENKATESHAPPA CHIKKOL
Open Access
Journal Browser
Go
American Journal of Mechanical Engineering. 2016, 4(1), 21-31
DOI: 10.12691/ajme-4-1-4
Open AccessArticle

Industrial Robot Backlash Fault Diagnosis Based on Discrete Wavelet Transform and Artificial Neural Network

Alaa Abdulhady Jaber1, 2, and Robert Bicker1

1Mechanical and Systems Engineering, Newcastle University, Newcastle upon Tyne, UK

2Mechanical Engineering Department, University of Technology, Baghdad, Iraq

Pub. Date: January 15, 2016

Cite this paper:
Alaa Abdulhady Jaber and Robert Bicker. Industrial Robot Backlash Fault Diagnosis Based on Discrete Wavelet Transform and Artificial Neural Network. American Journal of Mechanical Engineering. 2016; 4(1):21-31. doi: 10.12691/ajme-4-1-4

Abstract

Industrial robots are commonplace in production systems and have long been used in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The ability to continuously monitor the status and condition of robots has become a research issue in recent years and is now receiving considerable attention. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults (backlash) that could be progressed in the gearbox of industrial robot joints. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded backlash levels in the robot.

Keywords:
condition monitoring fault detection and diagnosis discrete wavelet transform artificial neural network industrial robot LabVIEW Backlash

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

Figure of 13

References:

[1]  M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control: Wiley, 2005.
 
[2]  R. S. Beebe, Predictive Maintenance of Pumps Using Condition Monitoring. New York: Elsevier Advanced Technology, 2004.
 
[3]  V. F. Filaretov, M. K. Vukobratovic, and A. N. Zhirabok, “Observer-based fault diagnosis in manipulation robots,” Mechatronics, vol. 9, pp. 929-939, 1999.
 
[4]  D. Brambilla, L. M. Capisani, A. Ferrara, and P. Pisu, “Actuators and sensors fault detection for robot manipulators via second order sliding mode observers,” 2008, pp. 61-66.
 
[5]  H. Liu, T. Wei, and X. Wang, “Signal decomposition and fault diagnosis of a scara robot based only on tip acceleration measurement,” 2009, pp. 4811-4816.
 
[6]  I. Trendafilova and H. Van Brussel, “Condition monitoring of robot joints using statistical and nonlinear dynamics tools,” Meccanica, vol. 38, pp. 283-295, 2003.
 
[7]  A. Jaber and R. Bicker, “Industrial Robot Fault Detection Based on Wavelet Transform and LabVIEW,” in IEEE First International Conference on Systems Informatics, Modelling and Simulation, Sheffield, United Kingdom, 2014.
 
[8]  A. A. Jaber and R. Bicker, “The Optimum Selection of Wavelet Transform Parameters for the Purpose of Fault Detection in an Industrial Robot,” in 2014 IEEE - 4th International Conference on Control System, Computing and Engineering, Malaysia 2014.
 
[9]  A. A. Jaber and R. Bicker, “Real-Time Wavelet Analysis of a Vibration Signal Based on Arduino-UNO and LabVIEW,” International Journal of Materials Science and Engineering, vol. 3, pp. 66-70, 2015.
 
[10]  R. Bicker, A. Daadbin, and J. Rosinski, “The monitoring of vibration in industrial robots,” in ASME 12th Biennial Conference on Mechanical Vibration and Noise, 1989.
 
[11]  M. C. Pan, H. Van Brussel, and P. Sas, “Intelligent joint fault diagnosis of industrial robots,” Mechanical Systems and Signal Processing, vol. 12, pp. 571-588, 1998.
 
[12]  J. T. Sawicki, A. K. Sen, and G. Litak, “Multiresolution wavelet analysis of the dynamics of a cracked rotor,” International Journal of Rotating Machinery, vol. 2009, 2009.
 
[13]  F. Al-Badour, M. Sunar, and L. Cheded, “Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques,” Mechanical Systems and Signal Processing, vol. 25, pp. 2083-2101, 2011.
 
[14]  S. Debdas, M.F.Quereshi, A.Reddy, D.Chandrakar, and D.Pansari, “A Wavelet based multiresolution analysis for real time condition monitoring of AC machine using vibration analysis,” International Journal of Scientific and Engineering Research, vol. 2, 2011.
 
[15]  E. L. A. Vivas, A. Garcia-Gonzalez, I. Figueroa, and R. Q. Fuentes, “Discrete Wavelet transform and ANFIS classifier for Brain-Machine Interface based on EEG,” in 2013 6th International Conference on Human System Interactions, HSI 2013, 2013, pp. 137-144.
 
[16]  A. Ghods and H. H. Lee, “A frequency-based approach to detect bearing faults in induction motors using discrete wavelet transform,” in Proceedings of the IEEE International Conference on Industrial Technology, 2014, pp. 121-125.
 
[17]  M. Misiti, Y. Misiti, G. Oppenheim, and J.-M. Poggi, Wavelet Toolbox For Use with MATLAB: MathWorks, 1997.
 
[18]  W. L. Lim., “The application of artificial neural networks for sensor validation in diesel engine condition monitoring and fault diagnosis,” M. Phil., School of Civil Engineering and Geosciences, University of Newcastle upon Tyne, UK, 2009.
 
[19]  M. Misiti, Y. Misiti, G. Oppenheim, and J.-M. Poggi, Wavelet Toolbox for Use With Matlab: MathWorks, 2001.
 
[20]  A. Datta, C. Mavroidis, J. Krishnasamy, and M. Hosek, “Neural netowrk based fault diagnostics of industrial robots using wavelt multi-resolution analysis,” in American Control Conference, USA, 2007, pp. 1858-1863.
 
[21]  A. R. Mohanty, MACHINERYCONDITION MONITORING: PRINCIPLES AND PRACTICES: Taylor & Francis Group, 2015.
 
[22]  J. Halme, “Condition monitoring of a material handling industrial robot,” in 19th Internation Congress, Lulea,Sweden, 2006.
 
[23]  British-Standard, “Gears — Cylindrical involute gears and gear pairs — Concepts and geometry,” ed, 2007.
 
[24]  C. Rodriguez-Donate, L. Morales-Velazquez, R. A. Osornio-Rios, G. Herrera-Ruiz, and R. J. Romero-Troncoso, “FPGA-based fused smart sensor for dynamic and vibration parameter extraction in industrial robot links,” Sensors, vol. 10, pp. 4114-4129, 2010.
 
[25]  M. Negnevitsky, Artificial intelligence: a guide to intelligent systems, Second ed.: ADDISON WESLEY, 2005.
 
[26]  J. Mazumdar, “SYSTEM AND METHOD FOR DETERMINING HARMONIC CONTRIBUTIONS FROM NONLINEAR LOADS IN POWER SYSTEMS,” Ph.D Thesis, Electrical and Computer Engineering, Georgia Institute of Technology, USA, 2006.
 
[27]  D. Pandya, S. UPADHYAY, and S. Harsha, “Ann based fault diagnosis of rolling element bearing using time-frequency domain feature,” International Journal of Engineering Science and Technology, vol. 4, pp. 2878-2886, 2012.
 
[28]  J. Heaton. (2008). The Number of Hidden Layers.
 
[29]  S.N.Sivanandam., S.Sumathi., and S.N.Deepa., Introduction to Neural Networks Using MATLAB 6.0: McGraw Hill, 2006.
 
[30]  P. Subbaraj and B. Kannapiran, “Fault detection and diagnosis of pneumatic valve using Adaptive Neuro-Fuzzy Inference System approach,” Applied Soft Computing Journal, vol. 19, pp. 362-371, 2014.