Machine learning-based CPS for clustering high throughput machine cycle conditions

Díaz Rozo, Javier and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2017). Machine learning-based CPS for clustering high throughput machine cycle conditions. "Procedia Manufacturing", v. 10 ; pp. 997-1008. ISSN 2351-9789. https://doi.org/10.1016/j.promfg.2017.07.091.

Description

Title: Machine learning-based CPS for clustering high throughput machine cycle conditions
Author/s:
  • Díaz Rozo, Javier
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: Procedia Manufacturing
Date: 2017
ISSN: 2351-9789
Volume: 10
Subjects:
Freetext Keywords: Clustering; Cyber-physical system; IIoT; Behavior pattern; Knowledge discovery
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

Cyber-physical systems (CPS) have opened up a wide range of opportunities in terms of performance analysis that can be applied directly to the machine tool industry and are useful for maintenance systems and machine designers. High-speed communication capabilities enable the data to be gathered, pre-processed and processed for the purpose of machine diagnosis. This paper describes a complete real-world CPS implementation cycle, ranging from machine data acquisition to processing and interpretation. In fact, the aim of this paper is to propose a CPS for machine component knowledge discovery based on clustering algorithms using real data from a machining process. Therefore, it compares three clustering algorithms —k-means, hierarchical agglomerative and Gaussian mixture models— in terms of their contribution to spindle performance knowledge during high throughput machining operation.

Funding Projects

TypeCodeAcronymLeaderTitle
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
Government of SpainTIC-20150093UnspecifiedUnspecifiedUnspecified

More information

Item ID: 50946
DC Identifier: http://oa.upm.es/50946/
OAI Identifier: oai:oa.upm.es:50946
DOI: 10.1016/j.promfg.2017.07.091
Official URL: https://www.sciencedirect.com/science/article/pii/S2351978917302731
Deposited by: Memoria Investigacion
Deposited on: 21 Jun 2019 10:38
Last Modified: 21 Jun 2019 10:39
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