Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score
Year:2017
Research Areas
  • Applications for ingineerings and sciences,
  • Artificial intelligence,
  • Dataprocessing
Information
Abstract
Today, failure modes characterization and early detection is a key issue in complex assets. This is due to the negative impact of corrective operations and the conservative strategies usually put in practice, focused on preventive maintenance. In this paper anomaly detection issue is addressed in new monitoring sensor data by characterizing and modeling operational behaviors. The learning framework is performed on the basis of a machine learning approach that combines constrained K-means clustering for outlier detection and fuzzy modeling of distances to normality. A final score is also calculated over time, considering the membership degree to resulting fuzzy sets and a local outlier factor. Proposed solution is deployed in a CBM+ platform for online monitoring of the assets. In order to show the validity of the approach, experiments have been conducted on real operational faults in an auxiliary marine diesel engine. Experimental results show a fully comprehensive yet accurate prognostics approach, improving detection capabilities and knowledge management. The performance achieved is quite high (precision, sensitivity and specificity above 93% and ?=0.93?=0.93), even more so given that a very small percentage of real faults are present in data.
International
Si
JCR
Si
Title
Neurocomputing
ISBN
0925-2312
Impact factor JCR
2,392
Impact info
Datos JCR del año 2015
Volume
10.1016/j.neucom.2017.02.024
Journal number
241
From page
97
To page
107
Month
SIN MES
Ranking
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
  • Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)