Open Access

ARTICLE

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

Lifu He¹, Zhongchu Huang¹, Haidong Shao^2,*, 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:

(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 2026, 86(3), 59 https://doi.org/10.32604/cmc.2025.073227

Received 13 September 2025; Accepted 28 October 2025; Issue published 12 January 2026

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

---