Remaining life prediction of turbofan engine based on multi-path feature fusion

DeQun Zhao; JiaYu Zhao

doi:10.1117/12.2640207

1 August 2022 Remaining life prediction of turbofan engine based on multi-path feature fusion

DeQun Zhao, JiaYu Zhao

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122573O (2022) https://doi.org/10.1117/12.2640207
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

A multi-path feature fusion network model and data augmentation algorithm are proposed based on the limitations of the remaining useful life prediction method of a single network model and the problem of small sample size. Firstly, the sample size of data set is expanded through data augmentation, so as to improve the accuracy of prediction. Secondly, two different paths are selected to extract features: the first path inputs the data into the cascade structure of 1- Dimensional Convolutional Neural Network and Gated Recurrent Unit, and extracts spatial and temporal features respectively; the second path inputs the data directly into the Long Short Term Memory to obtain the complete temporal features. Finally, the output features of the two paths are fused, and the result input into the full connection layer for RUL prediction. The proposition is evaluated on the publicly available health monitoring dataset C-MAPSS of aircraft turbofan engines provided by NASA. The results show that compared with the single network model, the proposed method can effectively improve the remaining useful life prediction accuracy of such equipment, and has practical application value.

Citation Download Citation

DeQun Zhao and JiaYu Zhao "Remaining life prediction of turbofan engine based on multi-path feature fusion", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122573O (1 August 2022); https://doi.org/10.1117/12.2640207

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Sensors

Neural networks

Convolutional neural networks

Data processing

Model-based design

Data fusion

RELATED CONTENT

A model-based multisensor data fusion knowledge management approach
Proceedings of SPIE (June 20 2014)

Broad learning and convolutional neural network aided principal component analysis...
Proceedings of SPIE (August 10 2023)

A network security situational awareness model based on multi source...
Proceedings of SPIE (May 25 2023)

Temperature prediction based on LSTM ELM network under wide and...
Proceedings of SPIE (October 28 2021)

Suitability of features of deep convolutional neural networks for modeling...
Proceedings of SPIE (May 13 2019)

Maximum-likelihood adaptive neural network (MLANS) application to sensor fusion
Proceedings of SPIE (June 10 1994)

WARMER: a project for water monitoring system design
Proceedings of SPIE (November 06 2008)

Subscribe to Digital Library

Receive Erratum Email Alert

Keywords/Phrases

Search In:

Publication Years