Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED) method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA), finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.
In this study, the synergistic impact of boron, oxygen and titanium on growing large single-crystal diamonds was studied using different concentrations of B2O3 in a solvent-carbon system under 5.5-5.7 GPa and 1300-1500 ℃. The study found that it was difficult for boron atoms to enter the crystal when boron and oxygen impurities were doped using B2O3 without the addition of Ti. However, a high boron content was achieved in the doped diamonds that were synthesised with the addition of Ti. Additionally, boron-oxygen complexes were found on the surface of the crystal, and oxygen-related impurities appeared in the crystal interior when Ti added in the FeNi-C system. The results showed that the introduction of Ti in the synthesis cavity could effectively control the amount of boron and oxygen in the crystal. This not only has important scientific significance for understanding the synergistic influence of boron, oxygen and titanium on the growth of diamond in the earth, but also for the preparation of high-concentration boron or oxygen containing semiconductor diamond technologies.
Emerging quantum technologies require the fabrication of diamonds that contain single defects with specific optical, electronic, and magnetic properties. We report on the results of an annealing study performed at a pressure of 3.5 GPa with a temperature range of 1500–1900 °C on Ni-containing, nitrogen-rich (up to 640 ppm) synthetic diamonds and the formation of NE8 (N2NiN2) centers. This work examines the nitrogen-vacancy defects and nickel-related defects in detail. The results shown that high nitrogen concentration is more conducive to the formation of high-intensity NE8 centers compared with diamonds having a low nitrogen content. The aggregation of the A-center nitrogen facilitated the formation of the NE8 center. Furthermore, the intensity of the negatively charged nitrogen-vacancy (NV–) center decreased at higher annealing temperatures in the range of 1500–1800 °C. Our experimental results help increase the understanding of the formation of various centers in diamonds and their associated relationships to realize the effective control of the NE8 center concentration.
In this paper, an intelligent multiple Quality of Service (QoS) constrained traffic path allocation scheme with corresponding algorithm is proposed. The proposed method modifies deep Q-learning network (DQN) by graph neural network (GNN) and prioritized experience replay to fit the heterogeneous network, which is applied for production management and edge intelligent applications of smart factory. Moreover, through designing the reward function, the learning efficiency of the agent is improved under the sparse reward condition, and the multi-object optimization is realized. The simulation results show that the proposed method has high learning efficiency, and strong generalization ability adapting the changing of topological structure of network caused by network error, which is more suitable than the compared methods. In addition, it is also verified that combining the field knowledge and deep reinforcement learning (DRL) can improve the performance of the agent. The proposed method can achieve good performance in the network slicing scenario as well.
To reduce the adverse effect of incorrect parameters for the traditional iterative tunable Q-factor wavelet transform, this paper proposes an iterative tunable Q-factor wavelet transform method for fault feature extraction. Firstly, before decomposing the bearing vibration signal by an iterative tunable Q-factor wavelet transform, the initial values of 3 basic factors should be set: the quality factor Q, redundancy r and the number of decomposition level J. Secondly, the kurtosis of a high resonance component, which is the result of an iterative tunable Q-factor wavelet transform, is calculated through multistep iteration until it meets the iteration stop condition. Finally, the envelope spectrum of the final low resonance component is calculated, and the type of bearing fault can be recognized according to the frequency of extreme points. The results show that this method can effectively suppress noise and in-band interference and avoid fault identification inaccuracies caused by improper parameters and can also identify the fault feature frequency more clearly. 1
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