数据驱动的航天器故障诊断研究现状及挑战

Data-driven method for spacecraft fault diagnosis:State of art and challenge

总结了基于数据驱动的故障诊断技术理论、方法及其在航天器上的应用现状,指出其面临的挑战并提出一种解决方案。将基于数据驱动的故障诊断方法按照技术发展顺序分为基于传统机器学习的方法、基于深度学习的方法以及基于迁移学习的方法。基于传统机器学习的方法需要大量的人工参与和丰富的专家经验,在小样本数据上有着优异的性能,但不适合处理大数据。在大数据背景下,重点介绍了卷积神经网络、循环神经网络、深度自编码器以及深度信念网络的基本概念以及原理,对其在航天器故障诊断领域的研究现状进行了阐述和总结。针对深度学习严重依赖于带标签数据这一问题,介绍了基于迁移学习的故障诊断技术,并提出适应航天器应用的场景,为数据驱动的故障诊断技术工程应用提供了一种方法和思路。

Data-driven fault diagnosis(DFD) refers to applications of machine learning and deep learning theories as machine fault diagnosis. This paper systematically summarizes the state of art theories and methods of DFD as well as their applications in spacecraft following the progress of machine learning, then offers a future perspective. DFD could be divided into three categories, traditional machine learning-based methods, deep learning-based methods, and transfer learning-based methods, according to the development of technology. Traditional machine learning based methods adopt advanced signal processing method and feature extraction, which requires a lot of contribution from human labor and extensive expert experience. Although it has excellent performance on small sample data, it is not suitable for processing big data. Over the recent years, the advent of deep learning, which encourages to construct an end-to-end diagnosis model, further releases the human labor. Four deep learning models: Stack autoencoder, deep belief network, convolutional neural network, and recurrent neural network are introduced, their applications in diagnosing spacecraft faults are also summarized. Aiming to release the challenge that deep learning relies heavily on labeled data, transfer learning which attempts to use the diagnosis knowledge from one or multiple diagnosis tasks to other related ones, is introduced, and scenarios adapted to spacecraft applications are proposed, picturing a roadmap for the engineering application of DFD.