Heinz Welsch
What shapes cognitions of climate change in Europe? Ideology, morality, and the role of educational attainment
Journal of environmental studies and sciences
recent years research bearing fault diagnosis assumed source domain target domain data come machine differences equipment lead decrease diagnostic accuracy address issue unsupervised domain adaptation techniques introduced however crossdevice fault diagnosis models overlook discriminative information marginal distribution restricts performance models paper propose bearing fault diagnosis method based envelope spectrum conditional metric learning first envelope spectral analysis used extract frequency domain features fully utilize discriminative information label distribution construct deep siamese convolutional neural network based conditional metric learning eliminate data distribution differences extract common features source target domain data finally dynamic weighting factors employed improve convergence performance model optimize training process experimental analysis conducted 12 crossdevice tasks compared relevant methods results show proposed method achieves best performance three evaluation metrics