Industrial Robot Fault Detection Based on Statistical Control Chart
Journal Title: American Journal of Engineering and Applied Sciences - Year 2016, Vol 9, Issue 2
Abstract
Industrial Robot Fault Detection Based on Statistical Control Chart Alaa Abdulhady Jaber and Robert Bicker DOI : 10.3844/ajeassp.2016.251.263 American Journal of Engineering and Applied Sciences Volume 9, Issue 2 Pages 251-263 Abstract Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure and 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. In this research a fault detection system based on statistical control chart has been designed. An experimental investigation was accomplished using the PUMA 560 robot. Vibration signals are captured from the robot when it executes a repetitive task and then some statistical features are extracted from the signals, by utilising a developed data acquisition system based on National instruments hardware and software. The extracted vibration features, which are related to the robot healthy and faulty states, have subsequently been used for building and testing a statistical control chart. The chart has been validated using part of the measured data set, not used within the design stage, which represents the robot operating conditions. Validation results indicate the successful detection of faults at the early stages using the key extracted parameters. Copyright © 2016 Alaa Abdulhady Jaber and Robert Bicker. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://thescipub.com/abstract/10.3844/ajeassp.2016.251.263
Authors and Affiliations
Alaa Abdulhady Jaber, Robert Bicker
Keywords
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- EP ID EP203597
- DOI 10.3844/ajeassp.2016.251.263
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