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The Derivative as a Probabilistic Synthesis of Past and Future Data and Remaining Useful Life Prediction

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Probabilistic Prognostics and Health Management of Energy Systems

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Abstract

The concept of remaining useful life (RUL) is crucial when dealing with mechanical systems. RUL is taken into account in a system through a series of prognostics and the study of stability in a related data series. This paper is focused on a powerful optimal technique in prognosis coming from the Grünwald–Letnikov definition of derivative.

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Author information

Authors and Affiliations

  1. Department of Mathematics, São Paulo State University (UNESP), Ilha Solteira, SP, Brazil

    Berenice Camargo Damasceno & Luciano Barbanti

  2. Department of Electrical Engineering, São Paulo State University (UNESP), Ilha Solteira, SP, Brazil

    Hadamez Kuzminskas

  3. Department of Mechanical Engineering, São Paulo State University (UNESP), Ilha Solteira, SP, Brazil

    Márcio Antonio Bazani

Authors
  1. Berenice Camargo Damasceno
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  2. Luciano Barbanti
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  3. Hadamez Kuzminskas
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  4. Márcio Antonio Bazani
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Corresponding author

Correspondence to Berenice Camargo Damasceno .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Texas Tech University , Lubbock, Texas, USA

    Stephen Ekwaro-Osire

  2. Faculdade de Engenharia de Ilha Solteira, Universidade Estadual Paulista , Centro, Ilha Solteira, São Paulo, Brazil

    Aparecido Carlos Gonçalves

  3. School of Engineering and Computer Science and Mathematics, West Texas A&M University, Canyon, Texas, USA

    Fisseha M. Alemayehu

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Damasceno, B.C., Barbanti, L., Kuzminskas, H., Bazani, M.A. (2017). The Derivative as a Probabilistic Synthesis of Past and Future Data and Remaining Useful Life Prediction. In: Ekwaro-Osire, S., Gonçalves, A., Alemayehu, F. (eds) Probabilistic Prognostics and Health Management of Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-55852-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-55852-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55851-6

  • Online ISBN: 978-3-319-55852-3

  • eBook Packages: EnergyEnergy (R0)

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