计算机科学
涡轮机
学习迁移
卷积神经网络
断层(地质)
风力发电
人工智能
传递函数
人工神经网络
深度学习
模式识别(心理学)
控制理论(社会学)
机器学习
工程类
机械工程
电气工程
控制(管理)
地震学
地质学
作者
Peiming Shi,Linjie Jia,Steven Yi,Peiming Shi
标识
DOI:10.1088/1361-6501/ad1fcd
摘要
Abstract With the wide application of wind turbines, the bearing fault diagnosis of wind turbines has become a research hotspot. Under complex variable working conditions, the vibration signals of bearing components show non-stationary characteristics. Therefore, it is challenging to extract fault features using typical fault diagnosis methods. This paper proposes Adaptive Multivariate Variational Mode Decomposition combined with an improved Deep Discrimination Transfer Learning Network (AMVMD-IDDTLN) for bearing fault diagnosis of wind turbines under variable working conditions. First, the AMVMD method is used for the adaptive decomposition of the original signal, and use SE-ResNet18 convolutional neural network to obtain the transfer features of the source domain and target domain. Then, marginal distribution differences and conditional differences are assessed by DDM measures. The whole model is optimized by cross-entropy and improved joint distribution adaptation loss function, and the identification and classification of cross-working fault characteristics of the wind turbine- bearings are realized. The model achieves 99.48% transfer learning for the ten classifications of CWRU data set, 97% transfer learning for the four classifications of UPB data set, and 90% transfer learning for wind turbine bearing data across working conditions and across equipment. It is concluded that: Compared with similar models, the AMVMD-IDDTLN model proposed in this paper has higher diagnostic accuracy and faster convergence rate, which has certain practicality.
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