Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Kernel Density-Based Pattern Classification in Blind Fasteners Installation
Year:2017
Research Areas
  • Artificial intelligence (neuronal nets, expert systems, etc),
  • Dataprocessing,
  • Process modeled
Information
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.
International
Si
Congress
Hybrid Artificial Intelligent Systems. 12th International Conference, HAIS 2017
960
Place
La Rioja (SPAIN)
Reviewers
Si
ISBN/ISSN
978-3-319-59649-5
10.1007/978-3-319-59650-1
Start Date
21/06/2017
End Date
23/06/2017
From page
195
To page
206
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)
Participants
  • Autor: Alberto Diez Oliván (UPM)
  • Autor: Ricardo Sanz Bravo (UPM)
  • Autor: Basilio Sierra Araujo (UPV/EHU)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Laboratorio de Sistemas Autónomos
  • Centro o Instituto I+D+i: Centro de Automática y Robótica (CAR). Centro Mixto UPM-CSIC
  • Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
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