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Fault Diagnostics of Industrial Robots Using Support Vector Machines and Discrete Wavelet Transforms

[+] Author Affiliations
A. Datta, S. Patel, C. Mavroidis

Northeastern University

I. Antoniadis

National Technical University of Athens

J. Krishnasamy, M. Hosek

Brooks Automation, Inc.

Paper No. IMECE2006-15138, pp. 245-251; 7 pages
doi:10.1115/IMECE2006-15138
From:
  • ASME 2006 International Mechanical Engineering Congress and Exposition
  • Manufacturing Engineering and Textile Engineering
  • Chicago, Illinois, USA, November 5 – 10, 2006
  • Conference Sponsors: Manufacturing Engineering Division and Textile Engineering Division
  • ISBN: 0-7918-4774-8 | eISBN: 0-7918-3790-4
  • Copyright © 2006 by ASME

abstract

In this paper we address the problem of fault diagnostics in industrial robots. The goal was to develop a method that automatically, accurately and in a generic way could identify and classify faults once they occur for any type of industrial robot used. Although a large number of diagnosis methods and relevant applications for industrial equipment already exist, the current research in the area of fault diagnosis of industrial robotic manipulators is rather poor, due to the large variability of faults, the unsteady and non-uniform operating conditions, the small amount of sensors used in industrial manipulators and the rather limited time records of the equipment. These restrictions present key challenges of the current research to be undertaken. In this paper we present a novel approach to perform fault diagnostics of industrial robotic systems using Support Vector Machines (SVM) and Discrete Wavelet Transform based feature extraction. Experimental results are obtained from an industrial manipulator used in the semi-conductor industry.

Copyright © 2006 by ASME

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