Architecture for anomaly detection in a laser heating surface process

Mesonero Pérez, Javier and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2017). Architecture for anomaly detection in a laser heating surface process. In: "22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017)", 12-15 Sep 2017, Limassol, Chipre. ISBN 978-1-5090-6505-9. pp. 38-41. https://doi.org/10.1109/ETFA.2017.8247777.

Description

Title: Architecture for anomaly detection in a laser heating surface process
Author/s:
  • Mesonero Pérez, Javier
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Presentation at Congress or Conference (Article)
Event Title: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017)
Event Dates: 12-15 Sep 2017
Event Location: Limassol, Chipre
Title of Book: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Date: 2017
ISBN: 978-1-5090-6505-9
Subjects:
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

Anomaly detection is an increasingly common task in many industrial environments. Cyber-physical systems stand out in this field due to their unique position in industrial areas. This paper introduces a new architecture aimed to detect anomalies in a real laser heating surface process, which is designed for field-programmable gate arrays (FPGAs). The FPGA design offers advantages of highly parallelized and pipelined architectures. The system will classify one process into normal or abnormal taking into account spatial information about where the laser spot is. The proposed design estimates a probability density function from data; then it performs an image convolution transforming the probability density function into a kernel density estimation function. This estimated function should be able to classify in real time.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIC-20150093UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI - CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes

More information

Item ID: 51050
DC Identifier: http://oa.upm.es/51050/
OAI Identifier: oai:oa.upm.es:51050
DOI: 10.1109/ETFA.2017.8247777
Official URL: https://ieeexplore.ieee.org/document/8247777
Deposited by: Memoria Investigacion
Deposited on: 27 May 2019 11:51
Last Modified: 27 May 2019 11:51
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