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Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm

机译：基于逻辑回归和新型OS-ELM算法的飞机发动机退化预测

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摘要

Online sequential extreme learning machine (OS-ELM) learns data one-by-one or chunk-by-chunk, and the recursive least square (RLS) algorithm is commonly employed to train the topological parameters of OS-ELM. Since it is hard to guarantee the smallest estimation error of the state variable by the RLS, the regression performance of the OS-ELM easily fluctuates in practical applications. To address this gap, a new training approach of the OS-ELM using Kalman filter called KFOS-ELM is proposed, and state propagation is combined into extreme learning process to obtain the OS-ELM's topological parameters. Besides, an adaptive-weighted ensemble mechanism is developed and used to dynamically tune the weight coefficients of each KFOS-ELM in the learning network. The regression performance of the proposed methodology is evaluated using benchmark datasets. The simulation results show that proposed methods are superior to the OS-ELM and EOS-ELM in terms of the regression accuracy and stability without additional computational efforts. Furthermore, an enhanced multi-sensor prognostic model based on KFOS-ELM and logistic regression (LR) model is designed for remaining useful life (RUL) prediction of aircraft engine. The experimental results confirm our viewpoints. (C) 2018 Elsevier Masson SAS. All rights reserved.
机译：在线顺序极限学习机（OS-ELM）可以一对一或逐块学习数据，而递归最小二乘（RLS）算法通常用于训练OS-ELM的拓扑参数。由于难以通过RLS保证状态变量的最小估计误差，因此OS-ELM的回归性能在实际应用中容易波动。为了解决这一差距，提出了一种新的使用卡尔曼滤波器的OS-ELM训练方法，称为KFOS-ELM，并将状态传播结合到极限学习过程中以获得OS-ELM的拓扑参数。此外，还开发了一种自适应加权集成机制，用于动态调整学习网络中每个KFOS-ELM的权重系数。使用基准数据集评估了所提出方法的回归性能。仿真结果表明，所提出的方法在回归精度和稳定性方面均优于OS-ELM和EOS-ELM，无需额外的计算工作。此外，设计了一种基于KFOS-ELM和逻辑回归（LR）模型的增强型多传感器预测模型，用于飞机发动机的剩余使用寿命（RUL）预测。实验结果证实了我们的观点。 （C）2018 Elsevier Masson SAS。版权所有。

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  • 来源
    《Aerospace science and technology》 |2019年第1期|661-671|共11页
  • 作者

    Lu Feng; Wu Jindong; Huang Jinquan; Qiu Xiaojie;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Jiangsu Prov Key Lab Aerosp Power Syst, Nanjing 210016, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Jiangsu Prov Key Lab Aerosp Power Syst, Nanjing 210016, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Jiangsu Prov Key Lab Aerosp Power Syst, Nanjing 210016, Peoples R China;

    Aviat Ind Corp China, Aviat Motor Control Syst Inst, Wuxi 214063, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Aircraft engine; Remaining useful life; Prognostics; Online sequential learning; Kalman filter;

    机译：飞机发动机;剩余使用寿命;预测;在线顺序学习;卡尔曼滤波器;

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