用改进的信号分解+多尺度网络，精准识别风电机组齿轮箱故障。
CEEMDAN-Based Multiscale CNN for Wind Turbine Gearbox Fault Detection
- 先用CEEMDAN分解振动信号，提取多尺度特征。
- 再用MSCNN网络分类，实现98.95%的故障检测准确率。
- 适合需要高精度、快速诊断的风电运维场景。
风力发电在可持续能源转型中至关重要，其运行依赖于多个相互关联的部件，任一部件故障都可能影响整个系统功能。由于振动信号具有复杂的非线性、非平稳特性，受动态载荷、环境变化和机械相互作用影响，故障精确检测面临挑战。为此，本文提出一种融合完全集成经验模态分解自适应噪声（CEEMDAN）与多尺度卷积神经网络（MSCNN）的混合故障检测方法。首先利用CEEMDAN将振动信号分解为固有模态函数，分离出不同时间-频率尺度下的关键特征；随后将这些特征输入MSCNN，进行深层层级特征提取与分类。该方法在真实数据集上取得98.95%的F1分数，相较于现有方法，在检测准确率与计算速度方面均表现更优。该框架为风电机组系统的可靠高效故障诊断提供了平衡解决方案。
原文摘要 · Abstract (English)
Wind turbines play a critical role in the shift toward sustainable energy generation. Their operation relies on multiple interconnected components, and a failure in any of these can compromise the entire system's functionality. Detecting faults accurately is challenging due to the intricate, non-linear, and non-stationary nature of vibration signals, influenced by dynamic loading, environmental variations, and mechanical interactions. As such, effective signal processing techniques are essential for extracting meaningful features to enhance diagnostic accuracy. This study presents a hybrid approach for fault detection in wind turbine gearboxes, combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Multiscale Convolutional Neural Network (MSCNN). CEEMDAN is employed to decompose vibration signals into intrinsic mode functions, isolating critical features at different time-frequency scales. These are then input into the MSCNN, which performs deep hierarchical feature extraction and classification. The proposed method achieves an F1 Score of 98.95\%, evaluated on real-world datasets, and demonstrates superior performance in both detection accuracy and computational speed compared to existing approaches. This framework offers a balanced solution for reliable and efficient fault diagnosis in wind turbine systems.
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