Strathprints logo
Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Condition monitoring of robot joints using statistical and nonlinear dynamics tools

Trendafilova, I. and Van Brussel, H.H. (2003) Condition monitoring of robot joints using statistical and nonlinear dynamics tools. Meccanica, 38 (2). pp. 283-295. ISSN 0025-6455

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

This paper considers the problem for condition monitoring of robot joints employing measured acceleration signals. The study aims at (1) Determining features, to be extracted directly from the measured acceleration signals, to detect defects in robot joints and at (2) Finding features dependent on the size of the fault in order to quantify the present defects. The signals coming from intact robot joints and from joints containing backlash or clearance are analyzed using nonlinear dynamics as well as statistical tools. A method for defect detection that employs nonlinear autoregressive (AR) modeling of the acceleration signals is successfully applied to detect backlash and clearance in robot joints. Two procedures for defect quantification are considered - one of them based on the AR modeling and the other employing nonlinear dynamics and statistical features. The problems are considered in the context of a pattern recognition paradigm.

Item type: Article
ID code: 5017
Keywords: condition monitoring, nonlinear dynamics, pattern recognition, signal processing, mechanical engineering, Mechanical engineering and machinery, Mechanics of Materials, Mechanical Engineering, Condensed Matter Physics
Subjects: Technology > Mechanical engineering and machinery
Department: Faculty of Engineering > Mechanical and Aerospace Engineering
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 20 Dec 2007
    Last modified: 04 Sep 2014 15:34
    URI: http://strathprints.strath.ac.uk/id/eprint/5017

    Actions (login required)

    View Item