发动机微差速双转子系统不平衡参数辨识与振动自愈调控方法研究

  • 卢加乔 ,
  • 潘鑫 ,
  • 陈萌 ,
  • 魏锴 ,
  • 吴昊 ,
  • 韩林材
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  • 1. 北京化工大学
    2. 中国航发沈阳发动机研究所

收稿日期: 2026-01-04

  修回日期: 2026-03-12

  网络出版日期: 2026-03-16

基金资助

国家自然科学基金;国家自然科学基金

Unbalance parameter identification and self-recovery regulation methods for differential-speed dual-rotor systems

  • LU Jia-Qiao ,
  • PAN Xin ,
  • CHEN Meng ,
  • WEI Kai ,
  • WU Hao ,
  • HAN Lin-Cai
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Received date: 2026-01-04

  Revised date: 2026-03-12

  Online published: 2026-03-16

摘要

针对桨扇发动机前后排桨扇双转子不平衡参数辨识难和自愈调控技术无的问题,开展了微差速双转子系统不平衡参数辨识与振动自愈调控方法研究。首先,基于“分子靶向治疗”思想提出不平衡振动的仿生自愈原理,设计了发动机双转子不平衡振动自愈调控系统。其次,针对内外桨扇转子微差速工况导致的不平衡信号解耦难的问题,提出了复解析细化傅里叶变换与离散傅里叶变换(Zoom Fast Fourier Transform+Discrete Fourier Transformation, ZFFT+FT)相结合的细化频谱方法。然后,针对桨扇双转子系统不平衡振动在线控制问题,将灰狼优化算法(Grey Wolf Optimizer,GWO)与自适应线性二次调节器(Adaptive Linear-Quadratic Regulator,ALQR)结合,形成了GWO-ALQR靶向自愈调控方法。最后,在建立的双转子自愈调控实验台上开展了微差速信号辨识实验和不平衡自愈调控实验,结果表明:基于ZFFT+FT的信号辨识算法能够实现频率差为0.5Hz以上的近频信号的辨识与解耦,所形成的自愈调控方法可实现双转子系统微差速工况下不平衡振动降幅≥85%。本研究所建立的发动机微差速双转子系统不平衡参数辨识与振动自愈调控方法对该发动机的研制和运维方面具有一定的工程价值。

本文引用格式

卢加乔 , 潘鑫 , 陈萌 , 魏锴 , 吴昊 , 韩林材 . 发动机微差速双转子系统不平衡参数辨识与振动自愈调控方法研究[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33318

Abstract

Aiming at the challenges in identifying the unbalance parameters of the dual-rotor system with front and rear propfans in propfan engines and the lack of self-recovery regulation techniques, this study focuses on the research of unbalance parameter identification and self-recovery regulation methods for the micro-differential dual-rotor system.Firstly, inspired by the concept of "molecular targeted therapy", a bionic self-recovery principle for unbalanced vibration is proposed, and a self-recovery regulation system for unbalanced vibration of the dual-rotor system is designed accordingly. Secondly, to address the challenge of unbalanced signal decoupling caused by the micro-differential speed operating condition of the inner and outer propfan rotors, a refined spectrum analysis method is presented by combining the complex analytical Zoom Fast Fourier Transform (ZFFT) with the Discrete Fourier Transform (DFT). Then, aiming at the online control issue of unbalanced vibration in the propfan dual-rotor system, the Grey Wolf Optimizer (GWO) is integrated with the Adaptive Linear-Quadratic Regulator (ALQR) to develop the GWO-ALQR targeted self-recovery regulation method. Finally, experiments on micro-differential speed signal identification and unbalanced self-recovery regulation are conducted on the established dual-rotor self-recovery regulation test bench. The experimental results demonstrate that the ZFFT+FT-based signal identification algorithm can effectively identify and decouple adjacent frequency signals with a frequency difference of more than 0.5 Hz, and the proposed self-recovery regulation method can achieve a reduction rate of unbalanced vibration of no less than 85% for the dual-rotor system under micro-differential speed conditions. The unbalanced parameter identification and vibration self-recovery regulation method established in this study holds certain engineering significance for the development, operation, and maintenance of this type of propfan engine.

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