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Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

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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

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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.