Xiao-Qiang Zhao

Semi-supervised quality prediction for label-scarce industrial processes based on dynamic co-regression with multi-scale vision transformer

In industrial process quality prediction, limited labeled samples and noise interference often degrade the application performance of data-driven models. To address these issues, a Dynamic Co-regression Multi-scale Vision Transformer (DCMSViT) semi-supervised model is proposed to improve quality prediction accuracy in...

A strong noise-bearing fault diagnosis method based on A-CPO dual-stage associated parameter optimization

In practical rolling bearing fault diagnosis, the bearing vibration signals collected by sensors are often mixed with a large amount of noise, which blurs key fault features and reduces the distinguishability among different types of faults. This issue not only undermines the reliability of feature extraction but als...

An interpretable multi-sensor bearing nonlinear composite fault diagnosis method for wind power generation systems

Driven by global clean energy strategies, wind power develops rapidly. Bearings, core wind turbine transmission parts, govern system reliability and safety. Conventional diagnosis suffers three key practical limitations: single-sensor signals cannot fully characterize nonlinear composite faults; mainstream deep learnin...

A fault diagnosis method for rotating machinery based on spline-modulated wavelet and energy topology-guided graph convolution neural network