國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Remaining Useful Life Prediction of Aircraft Engines Based on Transfer Learning./
作者:	Arathi, Arathi.
面頁冊數:	1 online resource (78 pages)
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Aerospace engineering. -
電子資源:	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:

686400
Aerospace engineering.
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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