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.     

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.     

航空电子产品电磁环境效应模块化设计方法

对高强度辐射场（HIRF）环境特点与干扰类型进行分析，采用理论计算、计算机仿真技术（CST）的仿真分析及实测等方法分别对某型飞机航空电子系统综合显示单元的外部强电磁辐射场孔缝耦合、场线耦合及芯片前端电路影响关键芯片的相关规律进行了研究，给出了相关设计建议。结果表明，可通过分析孔缝、场线耦合获得进入关键芯片前端电路的电磁干扰能量，再结合关键芯片前端电路网络对干扰的插入损耗分析，获得关键芯片在外部强场激励下的感应电压。     

晶体管h参数微变等效电路的研究

晶体管h参数微变等效电路是电子技术中求解低频、小信号放大电路常用的分析方法.本文通过具体分析,将模拟放大电路进行了新的分类,并且明确而系统地给出运用微变等效电路求解各类模拟放大电路动态指标Av、ri和γ0公式、解题难点及规律.     

电工电子线路虚拟实验室的建设与应用

虚拟技术的发展使电子线路的分析设计过程得以在计算机上轻松、准确、快捷地完成。这样,一方面克服了实验室在元器件和规格上的限制,避免了损坏仪器等不利因素,另一方面使得实验不受时间及空间的限制,从而促进电子线路实验教学的现代化。     

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。     

对高频电路原理图绘制问题的探讨

本文结合Protel 99 SE在高频PCB设计中的应用,着重探讨在电路原理图的绘制上需要重点注意的一些特殊问题,并提出解决问题的方法,尽可能地减小对高频PCB设计的影响,提高PCB设计的效率。     

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。