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A novel distributed extended Kalman filter for aircraft engine gas-path health estimation with sensor fusion uncertainty

机译：具有传感器融合不确定性的飞机发动机气路健康估计的新型分布式扩展卡尔曼滤波器

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摘要

This paper is concerned with state estimation approach to track aircraft engine gas-path health condition in an advanced distributed architecture. The sensor measurements are divided into several subsets by installation position along gas path, and they are integrated to estimate engine health state changes with sensor fusion uncertainty. The uncertain sensor fusion is characterized by time delay and packet dropout in the fusion behavior of sensor measurements, and the delay steps occur randomly. A novel distributed extended Kalman filter with the data buffer bank (DEKF) is developed, and self-tuning buffer strategy of recursive fusion estimation is combined to the DEKF to form the self-tuning DEKF (SDEKF) algorithm for improving state estimation performance. The lengths of data buffer bank related to the local filters of SDEKF are different, and they are independently adaptive to the information loss level and local estimation accuracy. Local states are calculated using the measurements collected at the latest steps in self-tuning buffer banks, and then sent to master filter to yield global state and covariance by fusion estimation. The contribution of this study is to propose a novel EKF algorithm for state estimation in the distributed framework with sensor fusion uncertainty, and it achieves better trade-off between the estimation accuracy and computational efforts. The systematical comparisons of basic EKF, constant buffer DEKF and SDEKF algorithms are carried out for aircraft engine gas-path health estimation with sensor fusion uncertainty. The simulation results show the superiority of the SDEKF, and it confirms our viewpoints in this paper. (C) 2018 Elsevier Masson SAS. All rights reserved.
机译：本文涉及在先进的分布式体系结构中跟踪飞机发动机气路健康状况的状态估计方法。传感器的测量值按照沿气体路径的安装位置分为几个子集，并且将它们集成在一起，以估计具有传感器融合不确定性的发动机健康状态变化。传感器融合不确定性的特征在于传感器测量值融合行为中的时间延迟和数据包丢失，并且延迟步骤是随机发生的。提出了一种新型的带有数据缓冲库（DEKF）的分布式扩展卡尔曼滤波器，并将递归融合估计的自整定缓冲策略与DEKF相结合，形成了自整定DEKF（SDEKF）算法，以提高状态估计性能。与SDEKF的局部滤波器有关的数据缓冲区的长度不同，并且它们独立地适应信息丢失级别和局部估计精度。使用在自调整缓冲区中最新步骤收集的测量值来计算局部状态，然后将其发送到主滤波器以通过融合估计产生全局状态和协方差。这项研究的目的是提出一种新的EKF算法，用于具有传感器融合不确定性的分布式框架中的状态估计，并且可以在估计精度和计算工作之间取得更好的折衷。对具有传感器融合不确定性的飞机发动机气路健康估计进行了基本EKF，恒定缓冲区DEKF和SDEKF算法的系统比较。仿真结果表明了SDEKF的优越性，证实了本文的观点。 （C）2018 Elsevier Masson SAS。版权所有。

著录项

  • 来源
    《Aerospace science and technology》 |2019年第1期|90-106|共17页
  • 作者

    Lu Feng; Gao Tianyangyi; Huang Jinquan; Qiu Xiaojie;

  • 作者单位

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

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

    Nanjing Univ Aeronaut & Astronaut, Jiangsu Prov Key Lab Aerosp Power Syst, Nanjing 210016, Jiangsu, 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; Health estimation; Extended Kalman filter; Sensor fusion uncertainty; Distributed system;

    机译：飞机发动机;健康评估;扩展卡尔曼滤波器;传感器融合不确定性;分布式系统;

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