Transformer Encoder Enhanced by an Adaptive Graph Convolutional Neural Network for Prediction of Aero-Engines’ Remaining Useful Life

Author:

Ma Meng12,Wang Zhizhen12,Zhong Zhirong3

Affiliation:

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

2. National Key Lab of Aerospace Power System and Plasma Technology, Xi’an Jiaotong University, Xi’an 710049, China

3. School of Future Technology, Xi’an Jiaotong University, Xi’an 710049, China

Abstract

Accurate prediction of remaining useful life (RUL) plays a significant role in ensuring the safe flight of aircraft. With the recent rapid development of deep learning, there has been a growing trend towards more precise RUL prediction. However, while many current deep learning methods are capable of extracting spatial features—those along the sensor dimension—through convolutional kernels or fully connected layers, their extraction capacity is often limited due to the small scale of kernels and the high uncertainty associated with linear weights. Graph neural networks (GNNs), emerging as effective approaches for processing graph-structured data, explicitly consider the relationships between sensors. This is akin to imposing a constraint on the training process, thereby allowing the learned results to better approximate real-world situations. In order to address the challenge of GNNs in extracting temporal features, we augment our proposed framework for RUL prediction with a Transformer encoder, resulting in the adaptive graph convolutional transformer encoder (AGCTE). A case study using the C-MAPSS dataset is conducted to validate the effectiveness of our proposed model.

Funder

National Natural Science Foundation of China

Postdoctoral Science Fund

Basic Research Program of China

Basic Research Fund of Xi’an Jiaotong University

Publisher

MDPI AG

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