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Open Access

ARTICLE

Fault Diagnosis of Wind Turbine Blades Based on Multi-Sensor Weighted Alignment Fusion in Noisy Environments

Lifu He1, Zhongchu Huang1, Haidong Shao2,*, Zhangbo Hu1, Yuting Wang1, Jie Mei1, Xiaofei Zhang3
1 CTG Wuhan Science and Technology Innovation Park, China Three Gorges Corporation, Wuhan, 430010, China
2 College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
3 College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China
* Corresponding Author: Haidong Shao. Email: email
(This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems-2nd Edition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073227

Received 13 September 2025; Accepted 28 October 2025; Published online 21 November 2025

Abstract

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.

Keywords

Wind turbine blade; multi-sensor fusion; fault diagnosis; CNN-transformer coupled architecture

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