Kernel Density-Based Pattern Classification in Blind Fasteners Installation

Diez Oliván, Alberto and Penalva, Mariluz and Veiga, Fernando (2017). Kernel Density-Based Pattern Classification in Blind Fasteners Installation. In: "12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017", June 21-23, 2017, La Rioja (SPAIN). ISBN 978-3-319-59650-1. pp. 195-206.

Description

Title: Kernel Density-Based Pattern Classification in Blind Fasteners Installation
Author/s:
  • Diez Oliván, Alberto
  • Penalva, Mariluz
  • Veiga, Fernando
Item Type: Presentation at Congress or Conference (Article)
Event Title: 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017
Event Dates: June 21-23, 2017
Event Location: La Rioja (SPAIN)
Title of Book: Hybrid Artificial Intelligent Systems (Lecture Notes in Artificial Intelligence)
Date: 2017
ISBN: 978-3-319-59650-1
Subjects:
Freetext Keywords: Kernel density estimator, Behavioral patterns, Unsupervised classification, Outlier detection, Blind fasteners installation · Machine learning
Faculty: E.T.S.I. Industriales (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this work we introduce a kernel density-based pattern classification approach for the automatic identification of behavioral patterns from monitoring data related to blind fasteners installation. High density regions are estimated from feature space to establish behavioral patterns, automatically removing outliers and noisy instances in an iterative process. First the kernel density estimator is applied on the fastener features representing the quality of the installation. Then the behavioral patterns are identified from resulting high density regions, also considering the proximity between instances. Patterns are computed as the average of related monitoring torque-rotation diagrams. New fastening installations can be thus automatically classified in an online fashion. In order to show the validity of the approach, experiments have been conducted on real fastening data. Experimental results show an accurate pattern identification and classification approach, obtaining a global accuracy over 78% and improving current detection capabilities and existing evaluation systems.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020EU/H2020/686827UnspecifiedUnspecifiedUnspecified

More information

Item ID: 46843
DC Identifier: http://oa.upm.es/46843/
OAI Identifier: oai:oa.upm.es:46843
Official URL: http://www.springer.com/gp/book/9783319596495#aboutBook
Deposited by: Memoria Investigacion
Deposited on: 16 Jun 2017 18:03
Last Modified: 16 Jun 2017 18:03
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