To research the problems of the rolling bearing fault diagnosis under different noises and loads, a dual-input model based on a convolutional neural network (CNN) and long-short term memory (LSTM) neural network is proposed. The model uses both time domain and frequency domain features to achieve endto-end fault diagnosis. One-dimensional convolutional and pooling layers are utilized to extract the spatial features and retain the sequence features of the data. In addition, an LSTM layer is employed to extract the sequence features. Finally, a dense layer is applied for fault classification. To enhance recognition accuracy under different noises and loads, three techniques are applied to the proposed model, including taking timefrequency domain signals as input, using the CNN-LSTM model, and adopting the mini-batch and batch normalization methods. The Case Western Reserve University and Drivetrain Diagnostics Simulator data sets are used to construct experiments under different conditions, including varying loads and different noises. The proposed model can achieve a high fault recognition rate under variable load and noise conditions as well as satisfactory anti-noise and load adaptability.

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Fault diagnosis of rolling bearing has been the focus of research. Bearing signals are often accompanied by similar information, resulting in redundancy between data. Moreover, rolling bearing is often used in situations with large background noise, so extracting the characteristic value of rolling bearing signal and removing noise from the signal are of great significance. This paper presents a fault diagnosis model combining NAdam(Natural Adaptive Moment Estimation) algorithm and improved octave convolution. First, natural exponential decay function is proposed to replace the exponential decay function for parameter updating of Adam(Adaptive Moment Estimation). Compared with the exponential decay function, the natural exponential decay function can accelerate the convergence rate of the model. The internal structure in octave convolution is then improved. The improved structure can improve feature extraction and eliminate data redundancy. Finally, the dilated gate convolution layer is used to filter and classify the data. According to the simulation test of the case western reserve university data set and laboratory power equipment data set, the accuracy rate can reach more than 98%. Experiments with variable load and signal noise ratio are carried out to verify the noise resistance and generalization performance of the proposed method. INDEX TERMS NAdam; natural exponential decay function; exponential decay function; octave convolution; dilated gate convolution;

Using molecular dynamic simulation, the effect of vacancy clusters on the interstitial helium atoms was studied in the early stages of helium bubble formation in the vessel of fission reactor, aluminum. The simulation shows, that there is a slight propensity of helium interstitial clustering without initial vacancies in aluminum. When vacancy cluster was introduced, the behavior of interstitial helium atoms was strongly dependent on the ratio of vacancy to helium. The interstitial helium atoms will be attracted in the center of the vacancy cluster when the ratio of vacancy to helium is much larger than 1, and when the ratio approaches 1, the helium will recombine with the vacancies, and, form in substitutions. In the case of the ratio of vacancy to helium less than 1, some aluminum interstitials will be created. The result shows, that the vacancy cluster plays a role of a nucleation center for helium atoms to accelerate the helium bubble growth.