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  • 1.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges-Muro, Luis
    UniZar, Instituto de Educación Secundaria Corona de Aragón, Zaragoza, Spain.
    Lambán-Castillo, Pilar
    UniZar, Instituto de Educación Secundaria Corona de Aragón, Zaragoza, Spain.
    Huertas-Talón, Jose Luís
    UniZar, Instituto de Educación Secundaria Corona de Aragón, Zaragoza, Spain.
    Tormos-Martínez, Bernardo
    Universitat Politècnica de València (UPV), Spain.
    Cálculo de la vida útil remanente mediante trayectorias móviles entre hiperplanos de máquinas de de soporte vectorial: [Rul prediction using moving trajectories between svm hyper planes]2013In: Interciencia, ISSN 0378-1844, Vol. 38, no 8, p. 556-562Article in journal (Refereed)
    Abstract [en]

    We propose a new method for predicting remaining useful life ( RUL ) classifiers inspired by support vector machines ( SVM). Historical data of condition of a system during its lifetime are used to create a classification by hyperplanes in SVM. To estimate the RUL of a system , the degradation rate was assessed by calculating the minimum distance defined based on the degradation pathways , ie , the progressive approach hyperplane segregates information from both good and bad for different time horizons.One can estimate the final life of a specific component , or the information in the RUL of a population be calculated by aggregating multiple RUL estimates using a density estimation method . The degradation of a system is affected by many unknown factors , besides complicating degradation behaviors , difficult to collect quality data.Due to lack of knowledge and incomplete measurements , normally lacks important information on the context of the data collected . Therefore historical data of the system with a variety of degradation patterns are grouped , with the search for a global model RUL prediction is extremely difficult. This leads them to seek advanced forecasting techniques beyond traditional models. The proposed model develops an effective RUL prediction method that addresses multiple challenges in forecasting complex systems. The similarities between degradation pathways can be contrasted to enrich existing methodologies prognosis.

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