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.

集成的模块化航空电子设备是现代飞机的核心系统，它承载着几乎所有的电子功能。集成模块化航空电子设备的性能直接影响飞行任务。剩余使用寿命的预测是保证飞机安全性和可靠性的有效方法。为了满足集成模块化航空电子设备的实时性要求，预测算法应具有快速的学习速度。本文提出了一种综合增强的在线顺序并行极限学习机，以预测集成模块化航空电子设备的剩余使用寿命。首先，设计了具有并行隐藏层的网络以改善特征提取。其次，为了提高学习的稳定性，使用极限学习机自动编码器确定网络的输入权重。第三，开发了一种在线预测的更新方法，并设计了一种自适应权重来构建整体在线顺序预测方法。通过标准数据集验证了该方法的有效性和优越性。最后，本文将间歇性故障作为集成模块化航空电子设备的特征，并通过LévyProcess建立了退化模型。所提出的方法应用于集成模块化航空电子设备的剩余使用寿命预测。本文将间歇性故障视为集成模块化航空电子设备的特征，并使用LévyProcess建立了退化模型。所提出的方法应用于集成模块化航空电子设备的剩余使用寿命预测。本文将间歇性故障视为集成模块化航空电子设备的特征，并使用LévyProcess建立了退化模型。所提出的方法应用于集成模块化航空电子设备的剩余使用寿命预测。