A neural network framework for similarity-based prognostics

Bektas O., Jones J. A. , Sankararaman S., Roychoudhury I., Goebel K.

METHODSX, vol.6, pp.383-390, 2019 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 6
  • Publication Date: 2019
  • Doi Number: 10.1016/j.mex.2019.02.015
  • Journal Name: METHODSX
  • Journal Indexes: Emerging Sources Citation Index, Scopus
  • Page Numbers: pp.383-390
  • Keywords: Similarity based RUL calculation, Artificial neural networks, Data-driven prognostics

Abstract

Prognostic performance is associated with accurately estimating remaining useful life. Difficulty in accurate prognostic applications can be tackled by processing raw sensor readings into more meaningful and comprehensive health condition indicators that will then provide performance information for remaining useful life estimations. To that end, typically, multiple tasks on data pre-processing and predictions have to be carried out such that tasks can be assessed using different methodological aspects. However, incompatible methods may result in poor performance and consequently lead to undesirable error rates.