An improved transfer learning method for rotor unbalance position identification from simulated data to experimental data

Abstract

In the study of dynamic balancing for flexible rotors operating at high speeds, determining the unbalanced position of the rotor has consistently posed significant challenges. Accurate identification of the unbalanced position enables low-speed dynamic balancing to serve as a viable alternative to high-speed methods, ultimately reducing costs and enhancing operational efficiency. Although deep learning techniques utilizing limited labeled data have shown promising results in identifying unbalanced positions, the challenge of gathering a sufficient amount of labeled data for effectively training diagnostic models remains substantial. To address this issue, we propose a Pre-Adaptive Transfer Learning (PATL) approach that employs sample reconstruction through frequency domain correlation analysis. This technique facilitates cross-domain deep transfer recognition of rotor unbalanced positions by transferring insights from simulated dynamic model data to experimental datasets. Compared to existing methodologies, the proposed approach demonstrates significant improvements in both the accuracy and generalization of unbalanced position identification. The novelty of this research lies in the introduction of pre-adaptive transfer learning, which effectively minimizes the disparity between experimental and simulation data, thereby enhancing the model's recognition capabilities. Experimental results indicate that the proposed method achieves effective balancing outcomes across various operating conditions.