Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief Functions

  • Emmanuel Ramasso
  • Michèle Rombaut
  • Noureddine Zerhouni
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)


Forecasting the future states of a complex system is of paramount importance in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous (the value of a signal) or discrete (functioning modes). For each case, specific techniques exist. In this paper, we propose an approach called EVIPRO-KNN based on case-based reasoning and belief functions that jointly estimates the future values of the continuous signal and of the future discrete modes. A real datasets is used in order to assess the performance in estimating future break-down of a real system where the combination of both strategies provide the best prediction accuracies, up to 90%.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emmanuel Ramasso
    • 1
  • Michèle Rombaut
    • 2
  • Noureddine Zerhouni
    • 1
  1. 1.UMR CNRS 6174 - UFC / ENSMM / UTBM, Automatic Control and Micro-Mechatronic Systems DepartmentFEMTO-ST InstituteBesançonFrance
  2. 2.UMR CNRS 5216 - UJF, Signal and Images DepartmentGIPSA-labGrenobleFrance

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