Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

In many practical fault diagnosis applications, the acquisition of fault data labels requires substantial manpower and resources, which are sometimes impossible to achieve. To address this, an unsupervised bearing fault diagnosis method based on deep clustering is proposed. In this method, an autoencoder is initially applied to the signal spectrum to learn the initial representation. Then, its potential manifold is further searched, and a Gaussian mixture model is finally used for clustering. Experiments conducted on the Case Western Reserve University bearing datasets show that the proposed method can find the optimal clusterable manifold. Moreover, its clustering performance is better than those of the current advanced baseline methods, and it is only slightly complex. Thus, the effectiveness of the proposed method is verified.

In many real-world fault diagnosis applications, due to the frequent changes in working conditions, the distribution of labeled training data (source domain) is different from the distribution of the unlabeled test data (target domain), which leads to performance degradation. In order to solve this problem, an end-to-end unsupervised domain adaptation bear fault diagnosis model that combines Riemann metric correlation alignment and one-dimensional convolutional neural network (RMCA-1DCNN) is proposed in this study. Second-order statistic alignment of the specific activation layer in source and target domains is considered to be a regularization item and embedded in the deep convolutional neural network architecture to compensate for domain shift. Experimental results on the Case Western Reserve University motor bearing database demonstrate that the proposed method has strong fault-discriminative and domain-invariant capacity. Therefore, the proposed method can achieve higher diagnosis accuracy than that of other existing experimental methods.

The planar waveguide was formed in an x-cut Nd:YVO₄ crystal by 3.0 MeV Si⁺ ion implantation at a dose of 1times10¹⁵ ions/cm² at room temperature. The effective refractive indices of the waveguide propagation modes were measured by using a prism-coupling method after the annealing at 240degC for 60 min in air. Reflectivity calculation method (RCM) was applied to simulate the refractive index profile of the waveguide. It is found that relatively large positive change of ordinary refractive index happens in the guiding region. According to the reconstructed refractive index distribution, it is demonstrated that the fundamental mode operating at 1.06 mum can be confined in the Si⁺ ion-implanted Nd:YVO₄ waveguide. Meanwhile, the pump threshold of laser oscillation is theoretically estimated.

Though using estimating the model for the deaf on Web-Based distance education on the features and requirements of Web-education, this paper designs a teaching evaluation model for the deaf on Web-based distance education. It is in order to introduce the model, and introduces it on various aspects of web-teachingpsila features and evaluation process.