Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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.