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.     

Research on navigation of satellite constellation based on an asynchronous observation model using X-ray pulsar

Pulsar navigation is a promising navigation method for high-altitude orbit space tasks or deep space exploration. At present, an important reason for restricting the development of pulsar navigation is that navigation accuracy is not high due to the slow update of the measurements. In order to improve the accuracy of pulsar navigation, an asynchronous observation model which can improve the update rate of the measurements is proposed on the basis of satellite constellation which has a broad space for development because of its visibility and reliability. The simulation results show that the asynchronous observation model improves the positioning accuracy by 31.48% and velocity accuracy by 24.75% than that of the synchronous observation model. With the new Doppler effects compensation method in the asynchronous observation model proposed in this paper, the positioning accuracy is improved by 32.27%, and the velocity accuracy is improved by 34.07% than that of the traditional method. The simulation results show that without considering the clock error will result in a filtering divergence.     

杆的纵向振动分析

杆的纵向振动是连续系统的内容，一个连续系统具有分布质量，分丰弹性，分布阻尼，杆的纵向振动可用波动方程来描述，是用空间与时间坐标来描述的，运动方程是偏微方程。     

一万氧压机轴振动控制分析与实践

本文介绍了一种非常规的高速轴振动抑制方法，主要是用振源分析技术，通过确诊设备故障原因来分析影响轴振动的主要因素，调节机器的系统刚度，再通过理论计算修订安装参数，制定设备故障处理对策，在涟源钢铁集团有限公司一万氧压机组上的实践取得了相当满意的效果，很有推广价值。     

飞行器振动智能分析与诊断

本文通过综合多个领域知识，引进功能特征描述概念，运用证据理论，提出了对飞行器等复杂系统进行分析诊断的一个新方法并初步完成了专家系统原型，通过具体实例，再现了故障现象     

超声振动体的耦合振动研究

对一些典型的大尺寸超声振动体如圆柱体、空心圆柱体、长条形六面体和大矩形六面体。本文用通用有限元软件对耦合系数的分布进行了分析，并在耦合振动频率方程的基础上研究了耦合系数与耦合振动的强度之间的关系。     

多体系统振动的传递矩阵法

本文是文献工作的继续。导得了空间运动的任意刚体的场传递矩阵，建立了多体系统振动的传递矩阵法，为多体系统振动特性的研究提供了新的理论和方法，从而有效地简化多体系统振动特性的研究，大大减小了计算工作量，并且便于解析讨论。     

基于特征模型的挠性悬臂板控制

针对航天器上太阳帆板这种悬臂外伸薄板结构的挠性附件 ,存在环境扰动下所引起的振动 ,本文采用压电智能结构作为执行器对悬臂板进行主动振动控制。基于板系统的特征建模 ,并结合自适应控制对挠性板的主动控制进行了研究。通过仿真研究结果与应变律反馈控制比较 ,表明该方法的有效性     

弹性机翼对机载导弹子惯导系统导航精度的影响分析

弹性机翼的振动及杆臂效应会引起机载导弹加速度计输出中的附加干扰加速度，从而引起导弹子惯导系统的导航参数误差。本文通过建立机翼弹性振动模型及惯导误差传播模型，从理论上分析了机翼弹性振动及杆臂效应对子惯导系统导航精度的影响，并进行了计算机仿真研究。     

随振动量级增加卫星结构频率下移的分析

对卫星振动频率随量级增加而出现下降，即振动频率“漂移”的现象进行了初步分析。发现随振动量级的增加。结构的非线性表现突出。结构的刚度下降和阻尼增加，可认为主要是由蜂窝材料的非线性因素所致。