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Remaining Useful Life Prediction of Aircraft Engines Based on Transfer Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Remaining Useful Life Prediction of Aircraft Engines Based on Transfer Learning./
Author:	Arathi, Arathi.
Description:	1 online resource (78 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379919740

Remaining Useful Life Prediction of Aircraft Engines Based on Transfer Learning.
Arathi, Arathi.

Remaining Useful Life Prediction of Aircraft Engines Based on Transfer Learning. - 1 online resource (78 pages)

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.S.)--Virginia State University, 2023.

Includes bibliographical references

The goal of this study is to predict the remaining useful life (RUL) of aircraft engines by integrating a transfer learning framework with a deep learning approach. A domain-adapted deep learning model is developed that can adapt to the target domain while still retaining knowledge from the source domain. The performance of the proposed algorithm is assessed using different deep learning architectures as the base model and the Domain Adversarial Neural Network (DANN) approach. The Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset provided by NASA is used to evaluate the performance of the proposed adversarial weighting algorithm. The results show that the suggested domain adaptation algorithm with Long Short-Term Neural Network (LSTM) architecture could effectively transfer the knowledge from the source domain with a larger sample size and more operational conditions while reducing the impact of operational conditions and fault modes on the RUL prediction accuracy of the target domain.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379919740Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Aircraft enginesIndex Terms--Genre/Form:

554714
Electronic books.

Remaining Useful Life Prediction of Aircraft Engines Based on Transfer Learning.
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520 $a The goal of this study is to predict the remaining useful life (RUL) of aircraft engines by integrating a transfer learning framework with a deep learning approach. A domain-adapted deep learning model is developed that can adapt to the target domain while still retaining knowledge from the source domain. The performance of the proposed algorithm is assessed using different deep learning architectures as the base model and the Domain Adversarial Neural Network (DANN) approach. The Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset provided by NASA is used to evaluate the performance of the proposed adversarial weighting algorithm. The results show that the suggested domain adaptation algorithm with Long Short-Term Neural Network (LSTM) architecture could effectively transfer the knowledge from the source domain with a larger sample size and more operational conditions while reducing the impact of operational conditions and fault modes on the RUL prediction accuracy of the target domain.
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