Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network

Huthaifa AL-Khazraji, Ahmed R. Nasser, Ahmed M. Hasan, Ammar K. Al Mhdawi, Hamed Al-Raweshidy, Amjad J. Humaidi

Research output: Contribution to journalArticle (journal)peer-review

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Abstract

Remaining Useful Life (RUL) is used to provide an early indication of failures that required performing maintenance and/or replacement of the system in advance. Accurate RUL prediction offers cost-effective operation for decision-makers in the industry. The availability of data using intelligence sensors leverages the power of data-driven methods for RUL estimation. Deep Learning is one example of a data-driven method that has a lot of applications in the industry. One of these applications is the RUL prediction where DL algorithms achieved good results. This paper presents an Autoencoder-based Deep Belief Network (AE-DBN) model for Aircraft engines' RUL estimation. The AE-DBN DL model is utilized the feature extraction characteristic of AE and superiority in learning long-range dependencies of DBN. The efficiency of the proposed DL algorithm is evaluated by comparison between the proposed AE-DBRN and the state-of-the-art related method for RUL perdition for four datasets. Based on the Root Mean Square Error (RMSE) and Score indices, the outcomes reveal that the AE-DBN RUL prediction model is superior to other DL approaches.
Original languageEnglish
Pages (from-to)1-1
Number of pages1
JournalIEEE Access
DOIs
Publication statusE-pub ahead of print - 5 Jul 2022

Keywords

  • General Engineering
  • General Materials Science
  • General Computer Science
  • Electrical and Electronic Engineering

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