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

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 Levy Process. The proposed method is applied to remaining useful life prediction of integrated modular avionics.