One-dimensional vibration signals are widely used for gearbox fault diagnosis to perform maintenance timely and then reduce various losses. The fault diagnosis accuracy of those classifiers is determined by the features extracted from the vibration signals. These typical deep neural networks (DNNs), e.g., convolutional neural network (CNN) and convolutional autoencoder (CAE) have been applied in machinery fault diagnosis. However, the feature learning of DNNs on the nonlinear vibration signals is still a big challenge for gearbox fault diagnosis. In this article, a new CNN, multiscale fusion global sparse network (MFGSNet) is proposed for feature extraction from vibration signals and gearbox fault diagnosis. First, a novel kernel dynamic fusion method based on multiscale convolution is proposed to extract defect features of vibration signals; second, a global dense connection method is proposed to generate features from shallow and deep layers; finally, a spare feature selection layer is embedded in the deep network to perform feature selection and dimension reduction on the shallow and deep features. The experimental results show that MFGSNet has good fault feature extraction performance on gearbox vibration signals. It performs much better on gearbox fault diagnosis than these typical DNNs, e.g., residual network (ResNet) and dense connection network (DenseNet).

Wind power utilization is attracting worldwide attention in the renewable energy field, and as wind power develops from land to sea, the size of the blades is becoming incredibly larger. The fatigue test, especially the biaxial synchronous fatigue test for the blades, is becoming an indispensable step to ensure the blade’s quality before mass production, which means the biaxial independent test presently used may have difficulty reproducing the real damage for large-sized blades that oscillate simultaneously in flap-wise and edgewise directions in service conditions. The main point of the fatigue test is to carry out accelerated and reinforced oscillations on blades in the experimental plan. The target moments of critical blade sections are reached or not during the test are treated as one significant evaluation criterion. For independent tests, it is not hard to realize moment matching using additional masses fixed on certain critical blade sections, which may be not easy to put into effect for biaxial synchronous tests, since the mechanical properties and target moments in the flap-wise and edgewise directions are widely varied. To realize the mechanical decoupling for loading force or additional mass inertia force in two directions is becoming one of the key issues for blade biaxial synchronous fatigue testing. For this problem, the present paper proposed one mechanical decoupling design concept after a related literature review. After that, the blade moment design and target matching approach are also proposed, using the Transfer Matrix Method (TMM) for moment quick calculation and Particle Swarm Optimization (PSO) for case optimization.

The throttling characteristics of the diaphragm valve are numerically studied in this paper. Firstly, the diaphragm deformation performance is analyzed by a finite element method, while the upper boundary morphology of the internal flow field under different valve openings was obtained. Then the two-dimensional simulation of the weir diaphragm valve flow field is carried out in order to explore the optimal design of flow path profile. The study shows that the throttling characteristics can be improved by flatting the ridge side wall, widening the top of the ridge and gently flatting the internal protruding of the flow path. In addition, using the local grid encryption techniques based on velocity gradient adaptive and y⁺ adaptive can improve the accuracy of simulation results. Finally, a cavitation two-phase flow simulation is carried out. The results show that cavitation may occur below 50% opening of diaphragm valve in ultra-pure water system, which becomes more intense with the increase of inlet pressure and even leading to flow saturation on the micro-orifice state.

The oscillating flow field of the double nozzle flapper servo valve pre-stage is numerically analyzed through Large Eddy Simulation (LES) turbulent modeling with the previous grid independence verification. The vortex street flow phenomenon can be observed when the flow passes through the nozzle flapper channel, the vortex alternating in each side produces the periodical flow oscillation. The structural and flow parameter effects on the oscillating flow are emphasized, and it could be determined that the pressure on the flapper is nearly proportional to the flow velocity and inversely proportional to the actual distance between the flapper and the nozzle. On the other hand, the main frequency of oscillation decreases with the velocity and increases with the distance between the nozzle flapper. The main stage movement is further considered with a User Defined Function (UDF), and it could be determined that the influences of the structural and flow parameters on the flow oscillation are rarely changed, but the main frequencies drop, generally.

The high pressures in gasoline direct injection technology lead to structural damage in some hydraulic components, especially annular damage on the contact