Dynamic Bayesian network-based anomaly detection for in-process visual inspection of laser surface heat treatment

Ogbechie Condes, Alberto and Díaz Rozo, Javier and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2016). Dynamic Bayesian network-based anomaly detection for in-process visual inspection of laser surface heat treatment. In: "international Conference ML4CPS – Machine Learning for Cyber Physical Systems", 29 Sep 2016, Karlsruhe, Alemania. ISBN 978-3-662-53806-7. pp. 1-10. https://doi.org/10.1007/978-3-662-53806-7_3.

Description

Title: Dynamic Bayesian network-based anomaly detection for in-process visual inspection of laser surface heat treatment
Author/s:
  • Ogbechie Condes, Alberto
  • Díaz Rozo, Javier
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Presentation at Congress or Conference (Article)
Event Title: international Conference ML4CPS – Machine Learning for Cyber Physical Systems
Event Dates: 29 Sep 2016
Event Location: Karlsruhe, Alemania
Title of Book: Machine Learning for Cyber Physical Systems
Date: 2016
ISBN: 978-3-662-53806-7
Volume: 1
Subjects:
Freetext Keywords: Dynamic Bayesian network; Anomaly detection; Laser heat treatment; Visual inspection; Cyber-physical system
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We present the application of a cyber-physical system for inprocess quality control based on the visual inspection of a laser surface heat treatment process. To do this, we propose a classification framework that detects anomalies in recorded video sequences that have been preprocessed using a clustering-based method for feature subset selection. One peculiarity of the classification task is that there are no examples with errors, since major irregularities seldom occur in efficient industrial processes. Additionally, the parts to be processed are expensive so the sample size is small. The proposed framework uses anomaly detection, cross-validation and sampling techniques to deal with these issues. Regarding anomaly detection, dynamic Bayesian networks (DBNs) are used to represent the temporal characteristics of the normal process. Experiments are conducted with two diferent types of DBN structure learning algorithms, and classification performance is assessed on both anomalyfree examples and sequences with anomalies simulated by experts.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIC-20150093-PUnspecifiedUniversidad Politécnica de MadridUnspecified
Madrid Regional GovernmentS2013/ICE-2845CASI - CAMUniversidad Politécnica de MadridConceptos y Aplicaciones de los Sistemas Inteligentes

More information

Item ID: 46652
DC Identifier: http://oa.upm.es/46652/
OAI Identifier: oai:oa.upm.es:46652
DOI: 10.1007/978-3-662-53806-7_3
Official URL: http://www.springer.com/de/book/9783662538050
Deposited by: Memoria Investigacion
Deposited on: 07 Nov 2017 13:42
Last Modified: 07 Nov 2017 13:42
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