摘要
目前大多数旋转组件深度学习剩余使用寿命(RUL)预测方法通常是假设训练数据和测试数据有相同的数据分布,造成模型在不同工况下的预测准确率比较低。为了解决上述问题,以旋转多组件RUL预测模型为迁移对象,针对源域与目标域工况存在差异,目标域缺乏标签样本的迁移场景,引入了域分类器,结合源域的标签数据目标域的无标签数据重新训练RUL预测模型中的特征提取网络,在训练过程中加入自关联性及对应性约束,提升其对公共特征的提取能力,从而实现模型在不同场景的迁移应用。利用XJTU-SY公开数据集对迁移模型测试结果表明,相对于原预测模型,本文所述方法在新工况下的预测准确率更高;相比于其他迁移方法,本文方法预测误差更小,在变工况下的旋转组件剩余使用寿命预测迁移问题上具有更好的效果。
Most methods for predicting the Remaining useful life of deep learning of rotating units usually assume that the data distribution of training data and test data is the same,resulting in low prediction accuracy of the model under different working conditions.For the above problems,this paper proposed a model transfer method based on adversarial training,where the transfer object is a rotating multi-unit RUL prediction model.Aiming at the transfer scenario where the source domain and target domain have different working conditions and the target domain lacks label samples,a domain classifier was introduced to extract the common features of the source domain and target domain data.The feature extraction network in the RUL prediction model was retrained by combining the labeled data in the source domain with the unlabeled data in the target domain.In the training process,auto association and correspondence constraints were added to improve the ability to extract common features,thus realizing the migration application of the model in different scenarios.The test results of the transfer model using the XJTU-SY public dataset revealed that the prediction accuracy of the method described in this paper is higher than that of the original prediction model under the new working conditions.Compared with other transfer methods,the prediction error of this method is smaller,and it has a better effect on predicting the remaining useful life of rotating units under variable working conditions.
作者
刘夏丽
邓耀华
郭承旺
Liu Xiali;Deng Yaohua;Guo Chengwang(School of Electro-Mechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China)
出处
《电子测量技术》
北大核心
2023年第14期1-8,共8页
Electronic Measurement Technology
基金
国家自然科学基金(52175457)
广东省重点领域研发计划(2019B010154001)项目资助
关键词
旋转组件
剩余使用寿命预测
迁移学习
对抗训练
rotating unit
prediction of remaining useful life
transfer learning
adversarial training
