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Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review
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  • Somayeh Bakhtiari Ramezani ,
  • Logan Cummins ,
  • Brad Killen ,
  • Richard Carley ,
  • Amin Amirlatifi ,
  • Shahram Rahimi ,
  • Maria Seale ,
  • Linkan Bian
Somayeh Bakhtiari Ramezani
Mississippi State University, Mississippi State University, Mississippi State University, Mississippi State University

Corresponding Author:[email protected]

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Logan Cummins
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Brad Killen
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Richard Carley
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Amin Amirlatifi
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Shahram Rahimi
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Maria Seale
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Linkan Bian
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Abstract

  • This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life
    (RUL)
  • It discusses challenges and open problems faced in PdM
  • This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals
  • We propose the future direction for each challenge discussed in this article
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