TY  - EJOU
AU  - He, Lifu 
AU  - Huang, Zhongchu 
AU  - Shao, Haidong 
AU  - Hu, Zhangbo 
AU  - Wang, Yuting 
AU  - Mei, Jie 
AU  - Zhang, Xiaofei 

TI  - Fault Diagnosis of Wind Turbine Blades Based on Multi-Sensor Weighted Alignment Fusion in Noisy Environments
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 3
SN  - 1546-2226

AB  - Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction. However, several challenges remain. First, signal noise collected during blade operation masks fault features, severely impairing the fault diagnosis performance of deep learning models. Second, current blade fault diagnosis often relies on single-sensor data, resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states. To address these issues, a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed. Specifically, a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions, while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information. Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input, enabling comprehensive and effective fault diagnosis.
KW  - Wind turbine blade; multi-sensor fusion; fault diagnosis; CNN-transformer coupled architecture

DO  - 10.32604/cmc.2025.073227