Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network
Industrial robots have long been used in production systems 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 majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. 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 faults in the robot.
condition monitoring, fault diagnosis, Discrete wavelet transform, Artificial neural network, Industrial robot, LabVIEW
ABDULHADY JABER, A. & BICKER, R. 2016. Industrial Robot Backlash Fault Diagnosis Based on Discrete Wavelet Transform and Artificial Neural Network. American Journal of Mechanical Engineering, 4, 21-31.
AL-BADOUR, F., SUNAR, M. & CHEDED, L. 2011. Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques. Mechanical Systems and Signal Processing, 25, 2083-2101.
ANIL KUMAR, T. C., SINGH, G. & NAIKAN, V. N. A. 2015. Effectiveness of vibration monitoring in the health assessment of induction motor. International Journal of Prognostics and Health Management, 6.
BOUKABACHE, H., ESCRIBA, C., ZEDEK, S. & FOURNIOLS, J. Y. 2013. Wavlet decomposition based diagnostic for structural health monitoring on metallic aircrafts: Case of crack triangulation and corrosion detection. International Journal of Prognostics and Health Management, 4.
BRITISH-STANDARD 2007. Gears — Cylindrical involute gears and gear pairs — Concepts and geometry.
DATTA, A., MAVROIDIS, C., KRISHNASAMY, J. & HOSEK, M. Neural netowrk based fault diagnostics of industrial robots using wavelt multi-resolution analysis. American Control Conference, 2007 USA. 1858-1863.
DEBDAS, S., M.F.QUERESHI, A.REDDY, D.CHANDRAKAR & D.PANSARI 2011. A Wavelet based multiresolution analysis for real time condition monitoring of AC machine using vibration analysis. International Journal of Scientific and Engineering Research, 2.
FILARETOV, V. F., VUKOBRATOVIC, M. K. & ZHIRABOK, A. N. 1999. Observer-based fault diagnosis in manipulation robots. Mechatronics, 9, 929-939.
GHODS, A. & LEE, H. H. A frequency-based approach to detect bearing faults in induction motors using discrete wavelet transform. Proceedings of the IEEE International Conference on Industrial Technology, 2014. 121-125.
HALME, J. Condition monitoring of a material handling industrial robot. 19th Internation Congress, 2006 Lulea,Sweden.
HEATON, J. 2008. The Number of Hidden Layers [Online]. http://www.heatonresearch.com/node/707.
JABER, A. & BICKER, R. Industrial Robot Fault Detection Based on Wavelet Transform and LabVIEW. IEEE First International Conference on Systems Informatics, Modelling and Simulation, 2014 Sheffield, United Kingdom. IEEE Computer society.
JABER, A. A. & BICKER, R. 2015. Real-Time Wavelet Analysis of a Vibration Signal Based on Arduino-UNO and LabVIEW. International Journal of Materials Science and Engineering, 3, 66-70.
JABER, A. A. & BICKER, R. 2016. Fault diagnosis of industrial robot gears based on discrete wavelet transform and artificial neural network. Insight: Non-Destructive Testing and Condition Monitoring, 58, 179-186.
LIANG, X., ZUO, M. J. & HOSEINI, M. R. 2015. Vibration signal modeling of a planetary gear set for tooth crack detection. Engineering Failure Analysis, 48, 185-200.
LIM., W. L. 2009. The application of artificial neural networks for sensor validation in diesel engine condition monitoring and fault diagnosis. M. Phil., University of Newcastle upon Tyne.
MAZUMDAR, J. 2006. SYSTEM AND METHOD FOR DETERMINING HARMONIC CONTRIBUTIONS FROM NONLINEAR LOADS IN POWER SYSTEMS. Ph.D Thesis, Georgia Institute of Technology.
MISITI, M., MISITI, Y., OPPENHEIM, G. & POGGI, J.-M. 1997. Wavelet Toolbox For Use with MATLAB, MathWorks.
MISITI, M., MISITI, Y., OPPENHEIM, G. & POGGI, J.-M. 2001. Wavelet Toolbox for Use With Matlab, MathWorks.
MOHANTY, A. R. 2015. MACHINERYCONDITION MONITORING: PRINCIPLES AND PRACTICES, Taylor & Francis Group.
NEGNEVITSKY, M. 2005. Artificial intelligence: a guide to intelligent systems, ADDISON WESLEY.
OLSSON, E., FUNK, P. & XIONG, N. 2004. Fault diagnosis in industry using sensor readings and case-based reasoning. Journal of Intelligent and Fuzzy Systems, 15, 41-46.
PAN, M. C., VAN BRUSSEL, H. & SAS, P. 1998. Intelligent joint fault diagnosis of industrial robots. Mechanical Systems and Signal Processing, 12, 571-588.
PANDYA, D., UPADHYAY, S. & HARSHA, S. 2012. Ann based fault diagnosis of rolling element bearing using time-frequency domain feature. International Journal of Engineering Science and Technology, 4, 2878-2886.
RODRIGUEZ-DONATE, C., MORALES-VELAZQUEZ, L., OSORNIO-RIOS, R. A., HERRERA-RUIZ, G. & ROMERO-TRONCOSO, R. J. 2010. FPGA-based fused smart sensor for dynamic and vibration parameter extraction in industrial robot links. Sensors, 10, 4114-4129.
S.N.SIVANANDAM., S.SUMATHI. & S.N.DEEPA. 2006. Introduction to Neural Networks Using MATLAB 6.0, McGraw Hill.
SAWICKI, J. T., SEN, A. K. & LITAK, G. 2009. Multiresolution wavelet analysis of the dynamics of a cracked rotor. International Journal of Rotating Machinery, 2009.
SPONG, M. W., HUTCHINSON, S. & VIDYASAGAR, M. 2005. Robot Modeling and Control, Wiley.
SUBBARAJ, P. & KANNAPIRAN, B. 2014. Fault detection and diagnosis of pneumatic valve using Adaptive Neuro-Fuzzy Inference System approach. Applied Soft Computing Journal, 19, 362-371.
VAN, M., KANG, H. J. & RO, Y. S. 2011. A robust fault detection and isolation scheme for robot manipulators based on neural networks.
VIVAS, E. L. A., GARCIA-GONZALEZ, A., FIGUEROA, I. & FUENTES, R. Q. Discrete Wavelet transform and ANFIS classifier for Brain-Machine Interface based on EEG. 2013 6th International Conference on Human System Interactions, HSI 2013, 2013. 137-144.
YILDIRIM, A. & ESKI, I. 2010. Noise analysis of robot manipulator using neural networks. Robotics and Computer-Integrated Manufacturing, 26, 282-290.
YUAN, J., LIU, G. & WU, B. 2011. Power efficiency estimation-based health monitoring and fault detection of modular and reconfigurable robot. IEEE Transactions on Industrial Electronics, 58, 4880-4887.