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Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine

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Abstract

Integrated modular avionics is the core system of modern aircraft, which hosts almost all kinds of electrical functions. The performance of integrated modular avionics has an immediate influence on flight mission. Remaining useful life prediction is an effective manner to guarantee the safety and reliability of airplane. To satisfy the real-time requirement of integrated modular avionics, the prediction algorithm should have fast learning speed. This paper proposes an ensemble enhanced online sequential parallel extreme learning machine to predict the remaining useful life of integrated modular avionics. Firstly, a network with parallel hidden layers is designed to improve feature extraction. Secondly, to enhance the learning stability, the input weights of the network are determined by using extreme learning machine autoencoder. Thirdly, an updating method is developed for online prediction and an adaptive weight is designed to construct the ensemble online sequential prediction method. The effectiveness and superiority of the proposed method are verified through the standard datasets. Finally, this paper regards intermittent faults as the feature of integrated modular avionics and builds a degradation model by using Lévy Process. The proposed method is applied to remaining useful life prediction of integrated modular avionics.

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Abbreviations

IMA:

Integrated modular avionics

PHM:

Prognostics and health management

RUL:

Remaining useful life

LSTM:

Long short-term memory

ELM:

Extreme learning machine

OS-ELM:

Online sequential-extreme learning machine

EEOS-PELM:

Ensemble enhanced online sequential-parallel extreme learning machine

ELM-AE:

Extreme learning machine autoencoder

RMSE:

Root mean square error

ReLU:

Rectified linear unit

OFLN:

Online fast learning network

POS-ELM:

Parallel online sequential extreme learning machine

Std:

Standard deviation

MAE:

Mean absolute error

EM:

Electromigration

HCI:

Hot carrier injection

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Authors and Affiliations

  1. State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi’an, Shaanxi, China

    Gao Zehai & Zhang Jianfeng

  2. School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Ma Cunbao

  3. Development Dept of Device OS, Huawei Consumer Business Group, Xi’an, Shaanxi, China

    Xu Weijun

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  1. Gao Zehai
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  2. Ma Cunbao
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  3. Zhang Jianfeng
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  4. Xu Weijun
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Correspondence to Gao Zehai.

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Zehai, G., Cunbao, M., Jianfeng, Z. et al. Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine. Int. J. Mach. Learn. & Cyber. 12, 1893–1911 (2021). https://doi.org/10.1007/s13042-021-01283-y

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