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.     

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.     

某型装备能源电池异常爆破故障分析

对一起因电池设计缺陷引发装备战训失利的故障进行分析,重点对电池工作机理和设计缺陷展开解析,明确故障定位,并对国内外装备弹上不同体系电池的优缺点进行比较,旨在为产品维护保障、国产化科研等工作提供参考。     

预测悬停旋翼跨音速流动的一种组合自由尾流/CFD方法

一种新的组合自由尾流 / CFD方法用于悬停旋翼流动的 CFD解中以考虑实际尾流的作用 .用文中描述的尾流分析方法研究了螺旋尖涡的运动 .首先从广义尾流模型开始 ,用半经验公式模化了涡核对旋翼尾流的作用 ;然后在环量收敛和尾迹收敛的条件下完成了自由尾迹计算 ;最后应用 Jameson有限体积龙格 -库塔推进格式求解了欧拉方程 .所得结果与相关文献和实验数据进行了比较     

基于关联分析的机载电子设备SRU级故障诊断方法

针对机载设备二类检测设备将故障隔离到SRU的要求,对LRU的内部结构进行了分析,提出了点、底项、组底项的概念,用关联值表示点与点之间、点与SRU之间及组底项之间的关联关系;针对多SRU结构UUT,提出逐级排除法故障诊断方法.     

一种用于人体早期病变诊断的仪器

根据探针在人体穴位电流量的变化情况 ,具体分析了该仪器电路设计的工作原理、电路结构及具体的使用方法。     

现场动平衡技术在生产中的应用

利用美国恩泰克公司提供的DP1500数据采集仪及现场动平衡软件,以实例说明了现场动平衡技术在锅炉引风机、排粉风机、电动机、机泵等转动设备不平衡故障排除中的应用,介绍了不平衡故障的识别方法,揭示出该技术在生产中的实用性、先进性和经济性.     

基于磁场映像的印制电路板故障诊断方法研究

简要分析了印制电路板的电磁场，提出了用电磁感应法测量磁场的电咱故障诊断新方法，并给出了测试系统组成方案。最后通过验证实验证明了该方法是确实可行的。     

通用模拟示波器故障的分析与诊断

本文将通用模拟示波器电路分成五部分，总结和分析了每一部分电路故障可能引起的几种异常显示，给出了每一部分电路故障诊断方法及在相应电路中出现故障的常见器件，对有些故障与故障现象之间的因果关系做了进一步分析。