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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