Simulation data-driven attention fusion network with multi-similarity metric: A single-domain generalization diagnostic method for tie rod bolt loosening of a rod-fastening rotor system

Abstract

The rod-fastening rotor system is the core rotating component of gas turbines and aero engines. The rod-disk joint structure of the rod-fastening rotor system is subjected to tie rod bolt loosening under complex operating conditions. Because of the uncertainty of fault occurrence, the model must possess diagnostic capability under unseen working conditions. Domain generalization methods are commonly employed to address such problems. They typically utilize multi-condition data or generate data from a single condition to train the model, enabling it to generalize to unknown operating conditions. However, difficulties in collecting complete multi-condition data and limitations in generating data, such as conflicting attributes and limited categories, make it challenging to ensure high-quality data for model training. To address these issues, this paper establishes a dynamic model of the rod-fastening rotor system to obtain high-fidelity simulation fault data, ensuring the physical accuracy, richness, and diversity of the source domain data. Meanwhile, a novel signal-domain generalization method called simulation data-driven attention fusion network with multi-similarity metric is proposed for tie rod bolt loosening in the rod-fastening rotor system. The attention feature fusion strategy is utilized to achieve the comprehensive representation of local and global features efficiently. Moreover, a multi-similarity loss based on deep metric learning is introduced to comprehensively consider the self-similarity and relative similarities between features, enabling class-level optimization of features. The proposed method is validated to have superior diagnostic performance in two cross-domain tasks compared with other methods. The proposed method can provide valuable insights into fault diagnosis in the rod-fastening rotor system.