基于改进生成对抗网络的风机行星齿轮箱故障诊断方法

Fault Diagnosis Method of Wind Turbine Planetary Gearbox Based on Improved Generative Adversarial Network

行星齿轮箱是风电机组传动系统中的重要部件,其故障发生率较高且难以直接识别故障情况,此外故障样本难以直接获取、样本环境噪声大等问题增加了故障诊断的难度。针对这些问题,提出基于贝叶斯优化及Wasserstein距离改进辅助分类生成对抗网络模型的齿轮箱故障诊断方法。首先,以辅助分类生成对抗网络为基础,针对振动信号时序特征构建一维卷积层替代二维卷积,提高信号特征提取效率;同时,在生成器和判别器中加入批归一化层和Dropout层,规范数据结构特征。然后,利用贝叶斯优化策略自适应调节判别器参数,提升判别器的性能,并引入Wasserstein距离改进模型的目标函数,通过博弈对抗机制同时优化生成器和判别器,显著提高模型的泛化能力和故障特征提取能力。设计行星齿轮箱在定速和变速运行下不同故障状态的实验,在不同非平衡样本集情况下,该方法可实现样本数据增强,并且保持良好的故障识别准确率,验证了该方法的先进性。

The planetary gearbox is an important component of the wind turbine transmission system.Its failure rate is high and it is difficult to directly identify the failure situation.In addition,problems such as the difficulty of obtaining fault samples directly and the large environmental noise of the samples increase the difficulty of fault diagnosis.In response to these problems,a gearbox fault diagnosis method based on Bayesian optimization and Wasserstein distance improvement auxiliary classifier generative adversarial network(WAC-GAN)model was proposed.First of all,based on the auxiliary classifier generation adversarial network,a one-dimensional convolutional layer was constructed for the timing characteristics of vibration signals to replace two-dimensional convolutions to improve the efficiency of signal feature extraction;at the same time,batch normalization layers and dropout were added to the generator and discriminator Layer,standardize data structure characteristics.Then,the Bayesian optimization strategy was used to adaptively adjust the discriminator parameters to improve the performance of the discriminator,and the Wasserstein distance was introduced to improve the objective function of the model,and the generator and the discriminator are optimized simultaneously through the game confrontation mechanism,which significantly improves the generalization ability of the model and fault feature extraction capability.Experiments with different fault states of planetary gearboxes under constant speed and variable speed operation are designed.sample sets,this method can achieve sample data enhancement and maintain a good fault recognition accuracy rate,which verifies the advanced nature of the method.