Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Effect of bristle pack position on the rotordynamic characteristics of brush-labyrinth seals at various operating conditions

Over the last few decades, the research on the effect of bristle pack position on the rotordynamic characteristics of the brush-labyrinth seals is not sufficient. To this end, two kinds of brush-labyrinth seals for the bristle pack element installed upstream of the labyrinth teeth named BSU and installed downstream of the labyrinth teeth called BSD were used to investigate the effect of bristle pack position on the rotordynamic characteristics of the brush-labyrinth seals. Using the numerical model combining the porous medium model and the whirling rotor method, the rotordynamic characteristics of the BSU and BSD at various operating conditions including four kinds of pressure ratios, five kinds of inlet preswirl speeds and four kinds of rotor spinning speeds were conducted. The obtained results show that the effects of operating conditions on rotordynamic coefficients for the different seal configurations are different. The direct stiffness, cross-coupled stiffness and direct damping of the BSU are lower than those of the BSD. The rotordynamic coefficients of the BSU are more insensitive to the operating conditions variation. From the perspective of the seal stability, the BSU is a better brush-labyrinth seal configuration at high pressure ratio, high positive preswirl or high rotor spinning speed conditions. While in the case of low pressure ratio, low positive preswirl or low rotor spinning speed conditions, the BSD is a better choice.     

变势能阱双稳态气动弹性能量收集的性能增强研究

提出一种新型的磁耦合变势能阱双稳态压电颤振能量收集器，设计了外部磁场作用下颤振能量收集系统的双稳态构型，并利用弹性支撑的外部磁铁的运动实现了变势能阱技术，解释了变势能阱双稳态对颤振能量收集系统的性能增强机理。建立了磁力-压电-气动弹性耦合的颤振能量收集系统的动力学分析模型，根据非线性磁偶极模型以及平衡点稳定性理论，讨论了系统出现双稳态构型的参数条件。对磁耦合双稳态颤振能量收集系统的动态特性进行了数值仿真研究，结果显示，双稳态构型能够使无磁力颤振能量收集系统的超临界颤振行为转变为亚临界颤振，发生极限环振动的风速能够降低50%以上，拓宽了能量收集器的有效工作风速范围，并分析了磁铁间距、磁偶极矩对能量收集性能的影响规律。采用弹性支撑的外部磁铁的运动来自适应调节内外部磁铁之间的距离，达到变势能阱的目的，有效地降低了双稳态的势能阱深度，使系统更容易发生双稳态势能阱间的跃迁运动，从而在双稳态的设计基础上，实现了能量收集工作风速范围和输出电功率的同步提升，为低风速下的能量收集提供了一种有效的设计途径。     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

Influence of thickness and initial groove angle in M–K model on limit strain of 7B04 by considering through-thickness stress

Marciniack–Kuczinski (M–K) model is widely used to predict material’s forming limit curve (FLC). The prediction of FLC traditionally neglected through-thickness normal stress. However, it cannot be neglected in some forming processes. Much work has been done to study the effect of through-thickness normal stress on FLC with constant through-thickness normal stress or constant ratio of through-thickness normal stress and maximum principal stress. In addition, based on Nakazima test process, the ratio of through-thickness normal stress and maximum principal stress has been derived, which was a function of instantaneous thickness and loading path. Here, initial groove angle in M–K model was not considered. In this paper, uniaxial tension tests and Nakazima tests were performed on 7B04 aluminum alloy. Based on Hill 48 yield criterion and M–K model, the prediction model of FLC was established. The increase of thickness can enhance FLC. Meanwhile, it is necessary to consider through-thickness normal stress and initial groove angle in prediction model. On the left side of FLC, the effect of initial groove angle on FLC is weakened by increasing sheet thickness. On the right side of FLC, the effect of initial groove angle on FLC is strengthened by increasing sheet thickness. On the right side of FLC, the relation between limit strain points with different thicknesses is linear under one certain loading path. Thickness has decisive effect on through-thickness normal stress level and the changing trendy of through-thickness normal stress during calculation is different under different stress condition.     

基于CFD高精度算法的雷电电磁场研究

雷电空间电磁场一直是电磁领域研究的热门，成熟的算法例如有限时域差分法（FDTD），传输线矩阵法（TLM）以及频域的矩量法（MoM）在计算雷电问题方面都有广泛的应用。由于计算流体力学（CFD）中的Euler方程与电磁学中的Maxwell方程有着相同的守恒形式，而且采用间断伽辽金方法（DG）已经在流场问题上得到广泛的尝试，因此引入了基于计算流体力学的DG方法来离散时域Maxwell方程，并采用网格分区并行技术加速计算，使用基于DG的圆球雷达散射截面积（RCS）算例进行测试，数值结果一致表明DG算法在求解电磁场问题上的可行性，之后通过计算一段近场雷电通道的电场分布并与解析解、某算法仿真解对比，数据基本吻合，说明该方法适合于雷电电磁场的计算。     

A progressive approach to predict shot peening process parameters for forming integral panel of Al7050-T7451

In this paper, a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed. Firstly, the invariable parameters in the forming process including shot size, mass flow, peening distance and peening angle are determined according to the empirical and machine type. Then, the optimal value of air pressure for the whole shot peening is selected by the experimental data. Finally, the feeding speed for every shot peening path is predicted by regression equation. The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object, and four experiments are conducted. In order to design specimens for acquiring the forming data, one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens, which were peened along the middle of the two stringers. The most striking finding of this experiment is that the outer shape error range is below 3.9%, so the plate specimens can be used in predicting feeding speed of the integral panel. The second experiment is performed and results show that when the coverage reaches the limit of 80%, the minimum feeding speed is 50 mm/s. By this feeding speed, the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object, and the optimal air pressure is 0.15 MPa. Then, the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment, and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method. The algorithm is validated by forming a test part and the average deviation is 0.496 mm. It is shown that the approach can realize the forming of the integral panel precisely.     

基于DLatLRR与VGG Net的红外与可见光图像融合

针对红外与可见光图像融合中特征损失严重、显著目标不突出的问题，提出了一种低秩表示分解与深度神经网络相结合的图像融合算法。首先，对源图像进行潜在低秩表示分解（DLatLRR），得到相应的低秩部分、显著部分及稀疏噪声。然后，分别采用16层的VGG Net模型和联合特征加权算法对低秩部分与显著部分进行融合，舍弃二者的稀疏噪声。最后，对融合得到的低秩部分和显著部分进行图像重建，得到最终的融合图像。实验结果表明:与其他算法进行比较，所提算法能够对图像的深层次细节特征进行融合，突出场景中的感兴趣区域，且融合图像的相关差异和、结构相似性、线性相关度等多种客观指标均有所提升，提升最大值分别为0.73、0.15、0.11，噪声产生率的最大缩减值为0.041 2。      

基于鸽群优化算法的实时避障算法

为保证机器人能安全无碰撞地抵达目标位置，提出一种在改进版圆形扩张（CSE+）法中融合鸽群优化算法的实时避障算法。所提算法引入对障碍物密集程度的判断机制，在障碍分布密集时选择最安全的路径，在障碍物分布稀松的环境中，利用鸽群优化算法在安全范围内寻找下一目标最优位置。此外，还引入了搜索树，可实现死角的检测与避免。仿真结果显示:所提避障算法能提高路径规划的性能，在障碍物分布稀松时效果更加明显，且可实现死角检测并能通过狭长通道。