Fault Diagnosis of Functional Circuit in Avionics System Based on BPNN

Shi, Lei; Zhou, Zhenwei; Jia, Hanguang; Yu, Pengfei; He, Shilie; Cheng, Lixin; Huang, Yun

doi:10.1109/phm-qingdao46334.2019.8942817

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…For fair comparison the hyperparameters of other models are also adjusted to the optimal conditions by Bayesian optimization. For the selection of BPNN, RNN, and DBN structure, this paper referred to the methods in literature [ 38 , 39 , 40 ], respectively. At the same time, considering the dimensions of data input and the layer structure, the final structure selections are as follows: (1) The structure of BPNN is selected as 5120-500-7.…”

Section: Experimental Verificationmentioning

confidence: 99%

Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series

Wang

Sun

Zhao

et al. 2020

Sensors

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In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization.

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Section: Experimental Verificationmentioning

confidence: 99%

Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series

Wang

Sun

Zhao

et al. 2020

Sensors

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show abstract

A comprehensive solution for inspecting cable brackets in aircraft based on augmented reality and deep learning

Zhao

Xiao

2022

2022 8th International Conference on Mechanical Engineering and Automation Science (ICMEAS)

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Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution Network

Du,

Sheng,

et al. 2024

Entropy

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Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods.

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Fault Diagnosis of Functional Circuit in Avionics System Based on BPNN

Cited by 3 publications

References 5 publications

Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series

Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series

A comprehensive solution for inspecting cable brackets in aircraft based on augmented reality and deep learning

Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution Network

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