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.

Data representation and prediction model design play an important role in mid- to long-term runoff prediction. However, it is challenging to extract key factors that accurately characterize the changes in the runoff of a river basin because of the complex nature of the runoff process. In addition, the low accuracy is another problem for mid- to long-term runoff prediction. With an aim to solve these problems, two improvements are proposed in this paper. First, the partial mutual information (PMI)-based approach was employed for estimating the importance of various factors. Second, a deep learning architecture was introduced by using the deep belief network (DBN) with partial least-squares regression (PLSR), together denoted as PDBN, for mid- to long-term runoff prediction, which solves the problem of parameter optimization for the DBN using PLSR. The novelty of the proposed method lies in the key factor selection and a novel forecasting method for mid- to long-term runoff. Experimental results demonstrated that the proposed method can significantly improve the effect of mid- to long-term runoff prediction. Also, compared with the results obtained by current state-of-the-art prediction methods, i.e., DBN, backpropagation neural networks, and support vector machine models, our prediction results demonstrate the performance of the proposed method.

Coastal areas are vulnerable to floods caused by rainstorms and typhoons. It is necessary to ascertain the risk of floods caused by both of these extreme weather events. A conceptual risk model is proposed to evaluate the rainstorm risk, typhoon surge risk, and the compound risk in the coastal areas of Jiangsu Province during the period of 1960–2012. The results of the model show that the typhoon surge risk in the study region is greater than the rainstorm risk. Three Archimedean copulas were used to fit the joint probability distributions of the compound events. The Frank copula and the Gumbel copula proved to be the best-fitting joint distribution function for the Huaibei plain district and the Lixiahe district, respectively. The probability of the extreme compound events not happening is less than 90% in the study region. This means that the flood risk is mainly subject to the encounter of a low-level rainstorm and a low-level typhoon surge. The study shows that the northern region of Jiangsu Province is more vulnerable to the compound risk, and that we should pay more attention to the floods caused by the compound events of rainstorm and typhoon surge.

The accuracy of medium- and long-term runoff forecasting plays a significant role in several applications involving the management of hydrological resources, such as power generation, water supply and flood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability.

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.