基于部分域适应的旋转机械跨工况故障诊断

CROSS-WORKING CONDITIONS FAULT DIAGNOSIS OF ROTATINGMACHINERY BASED ON PARTIAL DOMAIN ADAPTATION

为解决风电机组运行工况变化导致数据分布差异且标签空间不一致场景下的诊断问题,提出一种基于融合权重领域对抗的部分域适应方法(FWDAN),用于旋转机械跨工况故障诊断。FWDAN的核心思想是在样本和类别双重层面施加训练权重,以在对抗训练过程中弱化外部类样本的作用,强化对共享类样本的学习,从而促进领域间共享诊断知识的迁移和提高对目标任务的诊断性能。对于样本级权重生成,将标签信息耦合进样本数据中充分挖掘样本的特征表示,进一步根据源域和目标域数据特性差异,应用不同的统计方法生成权重用于辅助模型训练,实现促进模型积极迁移和降低负迁移风险的目的。建立在滚动轴承和齿轮箱数据集上的两个诊断案例的实验结果表明,所提方法相比其他方法具有更高的诊断准确度和更强的泛化能。

To address the diagnosis problem in the scenario of varying data distribution and inconsistent label space due to the change of wind turbine working conditions,a partial domain adaptation method(FWDAN)based on fusion weights domain adversarial is proposed for cross-working condition fault diagnosis of rotating machinery.The core idea of FWDAN is to apply the training weights at both the sample and category to weaken the role of outlier category samples in the adversarial training process and enhance the learning of shared category samples,thus facilitating the transfer of shared diagnostic knowledge between domains and improving the diagnostic performance.For sample-level weight generation,the label information is coupled into the sample data to fully explore the feature representation.Further,different statistical methods are applied to generate weights for assisting model training according to the differences between source and target domain data to achieve the purpose of promoting positive model transfer and reducing the risk of negative transfer.The experimental results of two diagnostic cases built on rolling bearing and gearbox datasets show that the proposed method has higher diagnostic accuracy and stronger generalization ability than other methods.