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.     

Review and propositions for the sliding/impact wear behavior in a contact interface

A thermal-solid-liquid complex operational environment induces structural interface developing a typical coupling sliding/impact wear behavior. It results in contact damage until systems fail, which may cause significant economic losses and catastrophic consequences. The key point of solving this problem is to reveal the coupling damage mechanism of the sliding/impact behavior in typical systems and life characterization under a complicate evolving environment. This has been a hot topic in the area of mechanical reliability. The main work in this paper can be concluded as follows. Firstly, the main industries in which the “sliding/impact behavior” takes place have been introduced. Then, existing studies on the wear mechanism and degree analysis are presented, which includes surface morphology analysis, wear debris analysis, and wear degree measurement. Meanwhile, existing problems in theoretical modeling and experiments in current research are summarized, so as to point out a bright direction for future research on wear prediction. They include interface contact modeling, mathematic coupling mechanism modeling, wear equation establishment, and wear life characterization, which can provide some new ideas for improving the existing studies on the sliding/impact wear behavior.     

年薪制与国有企业经营者激励机制的研究

中国加入WTO后 ,市场化改革将逐步深入 ,完善国有企业经营者激励机制显得尤为紧迫。目前国有企业普遍推行的年薪制在实施方案上存在明显缺陷 ,必须加以改进和完善 ,而整个企业体制改革的不到位 ,也制约着经营者激励机制的形成 ,因此要尽快突破体制上存在的主要障碍。     

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。     

中小外贸企业经营机制创新分析

经营机制创新是中小型外贸企业摆脱困境的必由之路。针对目前面临的主要问题，从外部环境及企业自身经营机制缺陷方面对中小型外贸企业生存环境进行分析，并从实现经营机制创新角度探讨改善现状的方法，力图找出中小型外贸企业经营机制创新的突破点。     

论我国公民对政府监督机制的优化

公民对政府的监督是行政监督体系的重要组成部分，它主要包括公民决定政府领导人的去留，举报、检举政府违法行为和进行集体利益表达三种形式。然而，我国目前这三种形式还存在不少问题。扩大和保障公民的民主权利，健全公民的信访举报制度，完善公民的集体利益表达机制，是优化我国公民对政府监督的主要制度措施。     

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。