曹芳菲, 朱怀石, 杜长坤, 路平立. 基于深度学习的高超声速飞行器执行器零样本故障辨识[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.09.27.001
引用本文: 曹芳菲, 朱怀石, 杜长坤, 路平立. 基于深度学习的高超声速飞行器执行器零样本故障辨识[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.09.27.001
CAO Fangfei, ZHU Huaishi, DU Changkun, LU Pingli. Deep learning based actuator fault identification for hypersonic vehicles: A zero-shot case[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.09.27.001
Citation: CAO Fangfei, ZHU Huaishi, DU Changkun, LU Pingli. Deep learning based actuator fault identification for hypersonic vehicles: A zero-shot case[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.09.27.001

基于深度学习的高超声速飞行器执行器零样本故障辨识

Deep learning based actuator fault identification for hypersonic vehicles: A zero-shot case

  • 摘要: 近年来,基于深度学习的故障诊断已经通过大量数据进行了深入的研究. 然而,深度学习技术的巨大成功是基于可以获取大量带标签的训练样本的假设. 在实际问题中,经常面临数据不平衡、标记的数据太少或没有数据的情况. 基于此,本文研究了高超声速飞行器在零样本情况下的故障辨识问题. 考虑飞控系统执行器故障,运用深度学习技术来识别特定故障类型(失效故障或卡死故障). “零样本”指的是在故障诊断的深度学习模型构建中,未曾包含或引入任何与目标故障相关的样本数据. 因此,该模型必须依赖于其他方法和特征来推断和准确识别这些未知故障,以实现有效的故障辨识. 针对这一问题,使用人工定义的故障描述来表征未知故障. 具体而言,即利用关系网络学习将已知故障样本与定义的未知故障描述进行比较. 进一步,为实现特征提取,结合卷积神经网络及长短期记忆神经网络,构建深度神经网络结构. 最后,在Winged-cone (翼椎体)构型的高超声速飞行器上进行零样本故障辨识实验,结果表明在没有目标故障样本的情况下,所设计的算法可以完成对目标故障的诊断工作.

     

    Abstract: Hypersonic vehicles play a crucial role in various applications and are complex systems that integrate aviation, electronics, computer control, electrical information, and sensing technologies. Owing to this complexity and their harsh working environment, hypersonic vehicles frequently face various faults or failures. Furthermore, these vehicles face a more challenging flight environment and complex dynamic characteristics than traditional aircraft. Building an accurate system model for hypersonic vehicles is considerably difficult. In recent years, extensive research has been conducted on fault diagnosis using deep learning and large datasets. However, the substantial success of deep learning techniques relies on the assumption that sufficient labeled training samples are available. In practical scenarios, problems such as data imbalance, insufficient labeled data, or even the absence of data are frequently encountered. This study investigates zero-shot fault identification for ultra-hypersonic aircraft. In particular, this study focuses on the diagnosis of faults in the flight control system actuators. This study aims to employ deep learning techniques to distinguish whether a specific fault is a loss-of-effectiveness (LoE) or locked-in-place (LiP) fault. In the context of this study, “zero-shot” indicates that no sample data related to the target faults has been included or introduced during the construction of the deep learning model for fault diagnosis. Therefore, the model must rely on alternative methods and features to infer and accurately identify unknown faults for effective fault recognition. To address this problem, artificial descriptions of faults are employed to characterize unknown faults. In particular, a relational network is used to compare the definitions of known fault samples with the descriptions of unknown faults. Furthermore, a deep neural network structure is built by combining convolutional neural networks with long short-term memory networks for feature extraction. Finally, zero-shot fault identification experiments are conducted on a high-hypersonic aircraft with a Winged-cone configuration. Fourteen types of faults, including seven types of LoE faults and seven types of LiP faults, are considered. The highest accuracies range from 81.44% to 89.92% over different types of faults. This demonstrates that it is possible to diagnose and classify different types of faults without training samples, realizing the initial objectives of the fault description-based method. This diagnosis is based on human-defined fault descriptions that allow for fault classification. The proposed zero-shot fault identification method aircraft can mitigate risks, enhance operational reliability, and improve safety in high-hypersonic aircraft operations.

     

/

返回文章
返回