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Author (up) Cho, A.D.; Carrasco, R.A.; Ruz, G.A. doi  openurl
  Title A RUL Estimation System from Clustered Run-to-Failure Degradation Signals Type
  Year 2022 Publication Sensors Abbreviated Journal Sensors  
  Volume 22 Issue 14 Pages 5323  
  Keywords prognostics; fault detection; recurrent neural networks; prophet  
  Abstract The prognostics and health management disciplines provide an efficient solution to improve a system's durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem's remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 1424-8220 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000831587200001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1614  
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