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February 9, 2026Journal of Measurements in EngineeringOpen Access

A novel wind turbine fault diagnosis method based on improved TFMST and DSC-CNN-GRU model

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Authors

WLW.Y. LiuTJTongming JianLMLei Meng

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Overview

This method demonstrates improved fault diagnosis accuracy in wind turbines, suggesting enhanced robustness and feature extraction capabilities.

Key Points

  • The study aims to develop a robust fault diagnosis method for wind turbines using an optimized model.
  • Enhanced the Time-Frequency-Multisqueezing Transform (TFMST) for better feature sensitivity.
  • Constructed two datasets: one with improved TFMST data and another with raw data.
  • Developed a dual-input DSC-CNN-GRU model to process both datasets.
  • Fused information from both data branches for accurate fault diagnosis.
  • The proposed method achieved high fault diagnosis accuracy compared to existing techniques.
  • Improvements were noted in robustness and information retrieval from noisy signals.
  • The lightweight design of the model contributed to its efficiency.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/698979e9f0ec2af6756e7fe8https://doi.org/10.21595/jme.2025.25113
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