复杂工况下风力机机械部件存在振动损伤识别困难,故障信号特征依赖人为经验提取等问题。通过完全自适应噪声集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)方法处理原始信号,通过Pearson系数筛选特征信号,基于混沌理论运用相空间重构方法以获取更为纯净信号,将重构信号输入卷积神经网络(Convolutional Neural Network,CNN)实现故障特征提取。结果表明：在不同的信噪比下,该诊断模型与现有方法对比,针对不同类型轴承故障具有更高的识别准确率,且具有良好的泛化性和鲁棒性,可为风力机机械部件的健康监测与管理提供参考。

It is difficult to identify the vibration damage of wind turbine mechanical components under complex working conditions, and the fault signal characteristics depend on human experience extraction. The original signal is processed by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise ( CEEMDAN ) method, and the characteristic signal is screened by the Pearson coefficient. Based on the chaos theory, the phase space reconstruction method is used to obtain a more pure signal, and the reconstructed signal is input into the Convolutional Neural Network ( CNN ) to realize fault feature extraction. The results show that compared with the existing methods, the diagnosis model has higher recognition accuracy for different types of bearing faults under different signal-to-noise ratios, and has good generalization and robustness, which can provide reference for the health monitoring and management of wind turbine mechanical components.

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国家自然科学基金资助项目(51976131,52006148,52106262)；上海市Ⅳ类高峰学科-能源科学与技术-上海非碳基能源转换与利用研究院建设项目资助Fund-support Project: National Natural Science Foundation of China ( 51976131,52006148,52106262 ) ; shanghai IV peak discipline-Energy Science and Technology-Shanghai Non-carbon-based Energy Conversion and Utilization Research Institute Construction Project Funding第一作者苏欢欢,(1997－),硕士研究生．研究方向故障诊断及信号处理．联系方式13193732542．E-maillafe_shh@163.com.通讯作者岳敏楠,(1982－),副教授．研究方向流体控制、风能利用等方面研究工作．E-mailymn@usst.edu.cn.