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

Xiang, Gang; Chen, Wenjing; Peng, Yu; Wang, Yuanjin; Qu, Chen

doi:10.1109/cac51589.2020.9327214

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Based on the DDC, the DAN proposed by [30] uses MK-MMD to achieve better performance. In the recent research work, the authors in [31][32][33] have used MMD directly to learn generic domaininvariant feature representations. Specifcally, reference [34] employed MK-MMD at several higher layers with varying weights to achieve efective domain feature transfer of different faults, while the authors in reference [35] applied MMD to reduce distribution diferences between training and test battery data, thereby enabling health assessment of lithium-ion batteries under diferent usage conditions, and the authors in reference [36] used MK-MMD to minimize diferences in the marginal probability distributions of metastable features to eliminate each fault diagnosis taskspecifc distribution diferences of high-level features in the discriminator.…”

Section: Unsupervised Domain Adaptationmentioning

confidence: 99%

An Intelligent Fault Detection Framework for FW‐UAV Based on Hybrid Deep Domain Adaptation Networks and the Hampel Filter

Zhang

et al. 2023

International Journal of Intelligent Systems

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Fixed-wing unmanned aerial vehicles (FW-UAVs) play an essential role in many fields, but the faults of FW-UAV components lead to severe accidents frequently; so, there is a need to continuously explore more intelligent fault detection methods to improve the safety and reliability of FW-UAVs. Deep learning provides advanced solution ideas for future UAV fault detection, but the current lack of UAV monitoring data limits the advantages of deep learning in UAV fault detection, which are both a challenge and an opportunity. In this paper, we mainly consider the data availability of deep learning under various practical flight conditions of FW-UAVs and propose a fault detection framework based on hybrid deep domain adaptation BiLSTM networks and the Hampel filter (HDBNH), the main purpose of which is to learn the knowledge of acquired data for detecting FW-UAV faults in other unknown operating conditions. HDBNH consists of three modules: feature extractor, domain adaptor, and fault detector. The feature extractor is two BiLSTM networks constructed to extract the past and future state features from the time-series flight data. The discrepancy of feature distribution between different domains is effectively reduced in the domain adaptor by a hybrid adversarial and the maximum mean discrepancy (MMD) domain adaptation method. The fault detector consists of a fault classification module and a Hampel filter. According to the continuous and dynamic characteristics of FW-UAV state changes, the Hampel filter is used to detect and correct the predicted values of the fault classification module. Meanwhile, a new state sample preparation strategy is proposed to support the work of HDBNH better. Finally, the effectiveness of HDBNH is confirmed by conducting extensive experiments in real FW-UAV flight data.

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Section: Unsupervised Domain Adaptationmentioning

confidence: 99%

An Intelligent Fault Detection Framework for FW‐UAV Based on Hybrid Deep Domain Adaptation Networks and the Hampel Filter

Zhang

et al. 2023

International Journal of Intelligent Systems

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Unsupervised domain adaptation bearing fault diagnosis method based on joint feature alignment

Xiaoliang,

Zhiwei,

Aiming

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, the issue of cross-condition fault diagnosis of bearing is studied. During actual operation, the conditions of bearing vary due to changes in factors such as rotation speed and load, and the data distribution between different working conditions varies. Deep learning models that perform well in one condition are not ideal when applied to another condition directly. To address this problem, a novel unsupervised domain adaptation fault diagnosis method based on joint feature alignment is proposed in this paper. 1D-CNN is used as a weight-shared feature extractor to extract the features from both the source and target domains. The discrepancies in marginal and conditional distributions between the source and target domains are comprehensively considered by multi-layer multi-bandwidth Cauchy kernel maximum mean discrepancy (MB-CMMD) and mutual information (MI). The domain drift is reduced by aligning the feature representations of source and target domains. The network after feature alignment demonstrates a notable enhancement in the diagnostic accuracy of unlabeled samples within the target domain. The experimental results demonstrate that, in comparison to other domain adaptation approaches, The proposed approach can significantly enhance the accuracy of fault diagnosis while realizing feature alignment.

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

Cited by 2 publications

References 8 publications

An Intelligent Fault Detection Framework for FW‐UAV Based on Hybrid Deep Domain Adaptation Networks and the Hampel Filter

An Intelligent Fault Detection Framework for FW‐UAV Based on Hybrid Deep Domain Adaptation Networks and the Hampel Filter

Unsupervised domain adaptation bearing fault diagnosis method based on joint feature alignment

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