Due to the fact that measured vibration signals from a bearing are complex and non-stationary in nature, and that impulse characteristics are always immersed in stochastic noise, it is usually difficult to diagnose fault symptoms manually. A novel hybrid fault diagnosis approach is developed for the denoising signals and fault classification in this work, which combines successfully the variational mode decomposition (VMD) and one dimensional convolutional neural network (1-D CNN). VMD is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the modal number and penalty parameter are very important in VMD, a particle swarm mutation optimization (PSMO) as a novel optimization method and the weighted signal difference average (WSDA) as a new fitness function are proposed to optimize the parameters of VMD. The reconstructed signals of mode components decomposed by optimized VMD are used as the input of the 1-D CNN to obtain fault diagnosis models. The performance of the proposed hybrid approach has been evaluated using the sets of experimental data of rolling bearings. The experimental results demonstrate that the VMD can eliminate signal noise and strengthen status characteristics, and the proposed hybrid approach has a superior capability for fault diagnosis from vibration signals of bearings.
The deposition of synthesized zinc oxide nanoparticles on the surface of cotton fiber under ultrasonic irradiation was reported in this study. The presence of the hexagonal phase of the crystalline metal oxide was confirmed by the XRD results. We also studied the electrochemical features of ZnO employed as an anode material for lithium-ion batteries. ZnO showed favorable lithium storage properties.
In order to improve the anti‐penetration performance of gradient armor, the constitutive models of B4C/Al composites with different compositions were determined according to the bending curves. The anti‐bullet simulation of B4C/Al gradient armor was carried out by ANSYS‐DYNA finite element software, and the stress state of B4C/Al gradient material under 7.62‐mm bullet penetration was analyzed. Meanwhile, the propagation law of stress wave in armor was studied by Hopkinson bar simulation and the improved internal stress wave model, which further revealed the ballistic mechanism of B4C/Al gradient armor. The simulation results showed that, compared with the traditional laminated armor, the toughness of B4C/Al gradient armor material increased with the change of layer thickness, resulting in the fact that the whole armor could withstand greater stress without breaking, and the anti‐penetration time was prolonged. In addition, the performance difference between the layers of the gradient armor was slight, and the spallation phenomenon of the relative double‐layer armor decreases, which enhanced the multiple hit performance of the armor and the absorption capacity of the stress wave. The performance of B4C/Al gradient armor specimens and double‐layer specimens were tested by drop hammer impact test. The test results were consistent with the simulation results.
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