Generative Adversarial Networks for Gearbox of Wind Turbine With Unbalanced Data Sets in Fault Diagnosis
Generative Adversarial Networks for Gearbox of Wind Turbine With Unbalanced Data Sets in Fault Diagnosis
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DOI：
10.1109/jsen.2022.3178137
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发表时间：
2022-07
影响因子：
4.3
通讯作者：
Yuxuan Su;Liang Meng;Xiaojian Kong;Tongle Xu;Xiaosheng Lan;Yunfeng Li
中科院分区：
文献类型：
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作者：
Yuxuan Su;Liang Meng;Xiaojian Kong;Tongle Xu;Xiaosheng Lan;Yunfeng Li
风电涡轮机齿轮箱信号的测量与诊断对设备的维护至关重要。生成对抗网络（GAN）由于其博弈机制，在数据生成方面表现尤为突出。提出了一种改进的基于遗传神经网络的非平衡数据集齿轮故障诊断方法。首先对故障数据进行基于峰度感知器的二值矢量化编码。其次，通过确定性选择选择符合适应度的个体，并对宏因子编码串进行多点交叉;然后，高斯变异的重点是寻找局部故障点。最后，利用逻辑回归辅助分类器建立非线性决策边界。通过三组对比实验验证了该方法的有效性。与现有方法相比，该方法在非平衡数据集下具有更好的故障特征生成能力、分类能力和诊断精度。
Signal measurement and diagnosis of wind turbine gearbox are very important for equipment maintenance. Generative adversarial networks (GAN) are particularly outstanding in data generation due to its game mechanism. An improved gear fault diagnosis method based on GAN for unbalanced data sets is proposed in this paper. Firstly, the fault data is encoded by binary vectorization based on kurtosis perceptron. Secondly, the individuals that fit the fitness are selected by the deterministic selection and the macro factor code string is crossed by multiple points. Then, gaussian mutation is focused on searching for local fault points. Finally, the nonlinear decision boundary is established by the logistic regression auxiliary classifier. The effectiveness of the proposed method was verified by three groups of comparison experiments. Compared with the existing methods, the proposed method has a better ability for fault feature generation, classification, and diagnosis accuracy under unbalanced data sets.