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

Alaa Abdulhady Jaber and ROBERT BICKER
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_16_017.pdf1.2 MBAugust 15, 2016 - 9:24am

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 utilizing 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.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
017
Page Count: 
13
Submission Keywords: 
condition monitoring
fault diagnosis
Discrete wavelet transform
Artificial neural network
Industrial robot
LabVIEW
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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