A gear fault diagnosis method for wind turbines based on HDDL and ConvNeXt under limited sample conditions

A novel wind turbine fault diagnosis method based on RTH algorithm and lightweight ShuffleNet with dense structure

Addressing the challenges of extracting fault features from non-stationary and noisy vibration signals in wind turbines, this paper proposes a novel wind turbine fault diagnosis method based on the Red-Tailed Hawk (RTH) algorithm and a lightweight ShuffleNet with a dense structure, which can realize rapid and accurate...

Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence - by multi-source data analysis and deep learning

The proposed method improves diagnostic accuracy, robustness, and engineering applicability for offshore wind turbine gearbox fault diagnosis.

MSFormer: Multi-Scale Transformer for Robust Fault Diagnosis of Machines Under Complex Conditions

The proposed MSFormer incorporates a parallel multi-scale Convolutional Neural Network architecture and hierarchical Transformer modules to comprehensively process 1D vibration signals to provide a powerful and precise intelligent solution for mechanical fault diagnosis.

Fault diagnosis of small sample wind turbine blade in ice-covered and damage condition based on ResNet50-SVM and transfer learning

Wind turbine blade failures, such as icing and damage, risk safety and efficiency, but limited fault data hinders diagnosis. This study proposes a hybrid framework combining ResNet50-SVM and transfer learning for small-sample fault diagnosis. A coupled simulation model first generates comprehensive dynamic fault data....

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...

Dynamic wavelet optimization and hierarchical reinforcement learning for fault diagnosis in wind turbines under variable operating conditions