American Journal of Mechanical Engineering
ISSN (Print): 2328-4102 ISSN (Online): 2328-4110 Website: Editor-in-chief: Kambiz Ebrahimi, Dr. SRINIVASA VENKATESHAPPA CHIKKOL
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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


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.

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

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