Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score

Diez Oliván, Alberto and Pagán, José A. and Sanz Bravo, Ricardo and Sierra, Basilio (2017). Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score. "Neurocomputing" (n. 241); pp. 97-107. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2017.02.024.

Description

Title: Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score
Author/s:
  • Diez Oliván, Alberto
  • Pagán, José A.
  • Sanz Bravo, Ricardo
  • Sierra, Basilio
Item Type: Article
Título de Revista/Publicación: Neurocomputing
Date: 2 June 2017
ISSN: 0925-2312
Subjects:
Freetext Keywords: Behavior characterization; Condition monitoring; Constrained k-means clustering; Fuzzy modeling; Local outlier factor; Machine learning
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

More information

Item ID: 46796
DC Identifier: http://oa.upm.es/46796/
OAI Identifier: oai:oa.upm.es:46796
DOI: 10.1016/j.neucom.2017.02.024
Official URL: http://www.sciencedirect.com/science/article/pii/S0925231217302941
Deposited by: Memoria Investigacion
Deposited on: 14 Jun 2017 13:49
Last Modified: 07 Jun 2019 22:30
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