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A K-nearest clamping force classifier for bolt tightening of wind turbine hubs

Secco, EL; Deters, C; Wurdemann, HA; Lam, HK; Seneviratne, L; Althoefer, K; (2016) A K-nearest clamping force classifier for bolt tightening of wind turbine hubs. Journal of Intelligent Computing , 7 (1) pp. 18-30. Green open access

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Abstract

A fuzzy-logic controller supporting the manufacturing of wind turbines and the bolt tightening of their hubs has been designed. The controller embeds assembly error recognition capability and detects tightening faults like misalignment, different threads, cross threads and wrong or small nuts. According to this capability, K-nearest classifiers have been implemented to cluster the output controllers into the diverse fault scenarios. Classifiers make use of the time of execution of the tightening process, the final angular position of and applied torque of the tightening tool, the resultant clamping force and possible combinations of those parameters. Two classes and five classes configurations are considered: classifiers are initially asked to discriminate between fault and no fault scenarios (e.g. two classes); then, five classes are considered according to five different fault situations (i.e. regular tightening, bolt misalignment, dissimilar threads of bolt and nut, missing nut and small bolt). Classifiers performances are estimated in terms of resubstitution and cross-validation loss. Confusion matrixes of actual and predicted classification are also evaluated for each classifier. The low computational cost of the proposed classifiers suggests directly implementing these algorithms on micro-controller and physical computing, which may be straight integrated within the tightening tool.

Type: Article
Title: A K-nearest clamping force classifier for bolt tightening of wind turbine hubs
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.dline.info/jic/fulltext/v7n1/jicv7n1_2....
Language: English
Additional information: Copyright © 2016 DLINE. All Rights Reserved
Keywords: Bolt tightening classifier; bolt tightening error detection; self-adaptive manufacturing; wind turbine manufacturing
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1485900
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