Observatorio de I+D+i UPM

Memorias de investigación
Other publications:
A Machine Learning Based Methodology for Automated Fault Prediction in Monitoring Sensor Data
Year:2016
Research Areas
  • Artificial intelligence (neuronal nets, expert systems, etc),
  • Naval engineering,
  • Dataprocessing,
  • Engeeniring of maintenance
Information
Abstract
Today, accurate fault detection and prediction in modern industrial assets machinery is a key issue raised by maintenance. In particular, automatically modeling the normal behaviour of the assets from monitoring sensor data is probably one of the most challenging problems, specially in industrial scenarios in which there is limited information of real faults and conservative strategies are commonly adopted. To this aim, a novel methodology based on a workflow that combines a set of Machine Learning based methods is proposed. It can be efficiently used to generate normality and behaviour models that are able to predict potential failures in an online fashion, preventing costly corrective interventions. In order to illustrate its applicability, the proposed methodology has been integrated in a CBM+ (CBM+RCM+AI) platform and a NAVANTIA MTU 12V-396 auxiliary diesel engine has been analyzed. Experimental results show promising advantages over traditional strategies, detecting deviation patterns and degradation symptoms at an early stage. Therefore, critical faults can be anticipated and serious damages can be avoided, improving reliability and availability of the assets.
International
No
Entity
U. ETSII, Industriales Research Meeting
Place
ETSI Industriales, Madrid, 2016
Pages
Reference/URL
http://oa.upm.es/40073/
Publication type
Póster
Participants
  • Autor: Alberto Diez Oliván (UPM)
  • Autor: Ricardo Sanz Bravo (UPM)
  • Autor: Khoa Nguyen (NICTA)
Research Group, Departaments and Institutes related
  • Creador: Centro o Instituto I+D+i: Centro de Automática y Robótica (CAR). Centro Mixto UPM-CSIC
  • 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)