This article introduces a new hybrid prognosis method to predict a remaining useful lifetime (RUL) of multi-functional spoiler (MFS) systems. The MFS is vital to the healthy operation of aircraft spoiler control systems, and any fault or failure in these systems could compromise the safe operation of the aircraft. The proposed prognosis methodology is a hybrid framework composed of a failure parameter estimation unit and an RUL unit. The failure parameter estimation unit observes the failure parameters using distributed neural networks via available measurements of the MFS system. Simultaneously, the remaining useful life is anticipated by the RUL unit employing the estimated failure parameter with a recursive Bayesian algorithm. Moreover, a relative accuracy (RA) measure is invoked to validate the effectiveness of the proposed method. Simulink model of the MFS system is verified by experimental data of the LJ200 series aircraft under fight condition. Furthermore, simulation test results indicate a high accuracy of the distributed structure compared to a centralized network.

中文翻译：

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基于分布式神经网络和递归贝叶斯算法的MFS系统混合故障预测新方法

本文介绍了一种新的混合预测方法，以预测多功能扰流板（MFS）系统的剩余使用寿命（RUL）。MFS对于飞机扰流板控制系统的正常运行至关重要，这些系统中的任何故障或故障都可能损害飞机的安全运行。所提出的预测方法是由故障参数估计单元和RUL单元组成的混合框架。故障参数估计单元使用分布式神经网络通过MFS系统的可用测量来观察故障参数。同时，RUL单元使用递归贝叶斯算法使用估计的故障参数来预期剩余使用寿命。此外，调用相对精度（RA）量度以验证所提出方法的有效性。通过战斗条件下的LJ200系列飞机的实验数据验证了MFS系统的Simulink模型。此外，仿真测试结果表明与集中式网络相比，分布式结构具有较高的准确性。