Journal of Computational Design and Engineering, 2022, qwac070, DOI: 10.1093/jcde/qwac070

A Siamese Hybrid Neural Network Framework for Few-shot Fault Diagnosis of Fixed-wing Unmanned Aerial Vehicles

Chuanjiang Li1, Shaobo Li1, Ansi Zhang1, Lei Yang1, Enrico Zio2, Michael Pecht3 and Konstantinos Gryllias3
1 School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China
2 Department of Energy, Politecnico di Milano, Milano 20133, Italy
3 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, 20742, USA
4 Department of Mechanical Engineering, KU Leuven, Leuven 3001, Belgium

For more information about this article and it's related research, please contact Prof. Michael Pecht


As fixed-wing unmanned aerial vehicles (FW-UAVs) are used for diverse civil and scientific missions, failure incidents are on the rise. Recent rapid developments in deep learning (DL) techniques offer advanced solutions for fault diagnosis of UAVs. However, most existing DL-based diagnostic models only perform well when trained on massive amounts of labeled data, which are challenging to collect due to the complexity of the FW-UAVs systems and service environments. To address these issues, this paper presents a novel framework, Siamese Hybrid Neural Network (SHNN), to achieve few-shot fault diagnosis of FW-UAVs in an intelligent manner. “State map” strategy is firstly proposed to transform raw flight data into similar and dissimilar sample pairs as input. The proposed SHNN framework consists of two identical networks that share weights with each other, and each subnetwork is designed with a hybrid one dimensional conventional neural network and long short-term memory (1D CNN-LSTM) model as feature encoder, whose generated feature embedding is used to measure the similarity of input pairs via a distance function in the metric space. In comprehensive experiments on a real flight dataset of an FW-UAV, the SHNN framework achieves competitive results compared to other models, demonstrating its effectiveness in both binary and multi-class few-shot fault diagnosis.

This article is available online here.


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