A K-Nearest Clamping Force Classifier for Bolt Tightening of Wind Turbine Hubs
Emanuele Lindo Secco, Christian Deters, Helge A. Wurdemann, Hak-Keung Lam,
Journal of Intelligent Computing, Vol. 7, No. 1, 2016.
DOI: 
Link: http://www.dline.info/jic/fulltext/v7n1/jicv7n1_2.pdf
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.