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Deep Transfer Learning Based on Convolutional Neural Networks for Intelligent Fault Diagnosis of Spacecraft

机译：基于卷积神经网络智能故障诊断的宇宙飞船的深度转移学习

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摘要

The on-orbit operation and all-weather duty of spacecraft lead to the rapid increase of test data, which brings severe challenges to fault diagnosis. In recent years, deep learning has shown excellent performance in feature extraction and pattern recognition. More and more attentions have been paid to the application of deep learning in spacecraft fault diagnosis. However, the success of deep learning largely relies on sufficient labeled data. Due to the high reliability of spacecraft, the test data usually contains lots of normal sample points, while the faulty sample points are extremely scarce. Firstly, this paper designs a fault diagnosis model based on 1-D convolutional neural network, which directly processes the 1-D raw data and extracts features; then, for the first time, the transfer learning technology is introduced into the field of spacecraft fault diagnosis, and a domain adaptive deep transfer model is proposed. MMD is used to reduce the discrepancy of data probability distribution between the source and target domain. The results of the experiments show that the proposed model could accurately diagnose and identify the faults of spacecraft in different application scenarios.
机译：航天器的轨道运行和全天候义务导致测试数据的快速增加，这带来了对故障诊断的严重挑战。近年来，深入学习在特征提取和模式识别方面表现出优异的性能。在航天器故障诊断中，已经支付了越来越多的关注。然而，深度学习的成功在很大程度上依赖于足够的标记数据。由于航天器的高可靠性，测试数据通常包含大量的正常样本点，而故障的采样点非常稀缺。首先，本文设计了基于1-D卷积神经网络的故障诊断模型，直接处理1-D原始数据并提取特征;然后，首次将转移学习技术引入航天器故障诊断领域，提出了域自适应深度传输模型。 MMD用于降低源和目标域之间的数据概率分布的差异。实验结果表明，所提出的模型可以准确地诊断和识别不同应用场景中航天器的故障。

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