A Transfer Learning Based Unmanned Aerial Vehicle MEMS Inertial Sensors Fault Diagnosis Method

Gao, Tong; Wei, Sheng; Yin, Yanzhao; Du, Xuejie

doi:10.1088/1742-6596/1852/4/042084

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Yang et al [21] used a sparse autoencoder to reconstruct the data to achieve the efect of data cleaning while preserving as much as possible the original fault knowledge of the data to improve the efciency of diagnosis. Gao et al [22] designed a transfer learning framework based on bidirectional long short-term memory (BiLSTM) networks using a multikernel MMD (MK-MMD) domain adaptation method to reduce the variability between two domains, applied to the case of insufcient samples in the target domain. Bondyra et al [23] proposed a fault detection algorithm based on signal processing and machine learning to use the acceleration data of IMU sensors to accurately identify rotor faults.…”

Section: Introductionmentioning

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: Introductionmentioning

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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Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System

Wang

et al. 2022

Applied Sciences

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There are various types of autonomous unmanned systems, covering different spaces of sea, land, and air, and they are comprehensively going deep into multiple fields of national security and social life. Due to the development of technology, the scale of unmanned systems is getting larger and larger, the number of components in the system is increasing, and the operating environment of the system is also becoming more and more complex. Therefore, the probability of failure of the components of the system will also be significantly increased. In order to eliminate the impact of the fault in time, the fault diagnosis method is significant. Considering the differences of components in autonomous unmanned systems, if a specific fault diagnosis algorithm is designed for each type of component, it will bring difficulties to the coordinated control of the system. Therefore, this paper analyzes the data characteristics of unmanned autonomous system devices (such as sensors) and finds that these data have time series. Therefore, the data of different devices can be converted into time series, and a general fault diagnosis algorithm suitable for most devices can be studied. The fault diagnosis algorithm is based on the clustering algorithm. In order to improve the clustering effect, the time series of different devices are represented by Gaussian mixture clustering to reduce the computational complexity of the clustering calculation. Then, a time series similarity measurement method based on the improved Markov chain is proposed. This method can better distinguish normal samples from abnormal samples so as to classify and identify faults effectively.

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A Transfer Learning Based Unmanned Aerial Vehicle MEMS Inertial Sensors Fault Diagnosis Method

Cited by 2 publications

References 7 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

Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System

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