A Siamese hybrid neural network framework for few-shot fault diagnosis of fixed-wing unmanned aerial vehicles

Abstract

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.

A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets

“…The reasons for the slight downward trend with A-B-C are as follows: according to the speed of A, B and C, we think that the working condition of A is more complex than that of B, and that of B is more complex than that of C. The model can learn more obvious fault features under more complex working conditions, so as to better complete the transfer task. In [ 18 , 21 , 39 ], the authors have also obtained a similar conclusion.…”

Fault diagnosis of drone motors driven by current signal data with few samples

“…This paper focuses on the motor of a quadrotor UAV and investigates a data driven fault diagnosis method based on current signals. Considering the requirements for UAV flight stability and the limited training data due to the UAV's sensitivity to component health status, traditional machine learning and deep learning methods struggle to identify representative features and suffer from low fault classification accuracy when dealing with a small number of training samples [45,48]. Treating the fault diagnosis of UAV motors as a small sample classification problem, a hybrid neural network with small sample learning capabilities is proposed, leveraging the broad learning system (BLS) [49] and convolutional neural network (CNN) [50] for the analysis of current signal data to address the challenges of small sample fault diagnosis in UAV motors.…”

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

“…Besides, Park et al [4] presented a method based on stacked pruning sparse denoising auto-encoder for UAV fault detection, which demonstrated good performance in noisy scenarios thanks to the use of the auto-encoder. Li et al [13] also presented an approach using siamese neural network [14] for diagnosing fixed-wing UAVs with limited training samples, but its performance advantage was limited to such scenarios. Its performance became comparable to conventional classifiers like support vector machine [15] when the training sample size increased.…”