PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE

Journal article

Asmaa Motrani, Rachid Noureddine, Farid Noureddine
Journal of the Serbian Society for Computational Mechanics/Vol, vol. 15, 2021, pp. 37--50

DOI: 10.24874/jsscm.2021.15.01.03

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APA Click to copy
Motrani, A., Noureddine, R., & Noureddine, F. (2021). PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE. Journal of the Serbian Society for Computational Mechanics/Vol, 15, 37–50. https://doi.org/10.24874/jsscm.2021.15.01.03

Chicago/Turabian Click to copy
Motrani, Asmaa, Rachid Noureddine, and Farid Noureddine. “PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE.” Journal of the Serbian Society for Computational Mechanics/Vol 15 (2021): 37–50.

MLA Click to copy
Motrani, Asmaa, et al. “PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE.” Journal of the Serbian Society for Computational Mechanics/Vol, vol. 15, 2021, pp. 37–50, doi:10.24874/jsscm.2021.15.01.03.

BibTeX Click to copy

@article{motrani2021a,
  title = {PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE},
  year = {2021},
  journal = {Journal of the Serbian Society for Computational Mechanics/Vol},
  pages = {37--50},
  volume = {15},
  doi = {10.24874/jsscm.2021.15.01.03},
  author = {Motrani, Asmaa and Noureddine, Rachid and Noureddine, Farid}
}

Abstract

The Prognostic and Health Management (PHM) becomes a research topic in its own right and tends to be more and more visible within the scientific community such as in Nasa Society, which has provided datasets for experiments. The purpose of this paper is to evaluate the performance of a data-driven prognostic technique used for predicting Remaining Useful Life (RUL). The methodological support of the proposed approach integrates all data-driven prognostic sequential steps merged in offline and online part. To design the predictive degradation model on the offline part, the Relevance Vector Machine (RVM) algorithm was applied. On the online part, prediction of the RUL is based on the Similarity-Based Interpolation (SBI) algorithm. The different steps of the methodology are described and their implementation undertaken through a case study involving the degradation dataset of turbofan engines from the Commercial Modular AeroPropulsion System Simulation (C-MAPSS). Finally, results are compared with other techniques applied on the same dataset.

Keywords:

Data-driven prognostic, prognostic and health management, relevance vector machine, remaining useful life, similarity-based interpolation

PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE

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Abstract

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