Gompertz binary particle swarm optimization and support vector data description system for fault detection and feature selection applied in automotive pedals components

Gompertz binary particle swarm optimization and support vector data description system for fault... This work presents an improved fault detection by reference space optimization and simultaneous feature selection applied in a manufacturing complex process of automotive pedals components. Support vector data description (SVDD) one-class classification method uses a hypersphere with the minimum volume to find an enclosed boundary containing almost all target objects. Gompertz binary particle swarm optimization algorithm (GBPSO) is applied to optimize kernel hyperparameters for SVDD and simultaneously solve the feature selection problem. In order to justify and validate the results, also the genetic algorithm (GA) and binary particle swarm optimization algorithm (BPSO) are presented to compare the performances of the three approaches in terms of the misclassification function. The experimental results showed that the proposed approach can correctly select the influencing input variables in order to achieve an efficient fault detection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Gompertz binary particle swarm optimization and support vector data description system for fault detection and feature selection applied in automotive pedals components

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0333-y
Publisher site
See Article on Publisher Site

Abstract

This work presents an improved fault detection by reference space optimization and simultaneous feature selection applied in a manufacturing complex process of automotive pedals components. Support vector data description (SVDD) one-class classification method uses a hypersphere with the minimum volume to find an enclosed boundary containing almost all target objects. Gompertz binary particle swarm optimization algorithm (GBPSO) is applied to optimize kernel hyperparameters for SVDD and simultaneously solve the feature selection problem. In order to justify and validate the results, also the genetic algorithm (GA) and binary particle swarm optimization algorithm (BPSO) are presented to compare the performances of the three approaches in terms of the misclassification function. The experimental results showed that the proposed approach can correctly select the influencing input variables in order to achieve an efficient fault detection.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Mar 31, 2017

References

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