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.     

Fault mode probability factor based fault-tolerant control for dissimilar redundant actuation system

This paper presents a Fault Mode Probability Factor (FMPF) based Fault-Tolerant Control (FTC) strategy for multiple faults of Dissimilar Redundant Actuation System (DRAS) composed of Hydraulic Actuator (HA) and Electro-Hydrostatic Actuator (EHA). The long-term service and severe working conditions can result in multiple gradual faults which can ultimately degrade the system performance, resulting in the system model drift into the fault state characterized with parameter uncertainty. The paper proposes to address this problem by using the historical statistics of the multiple gradual faults and the proposed FMPF to amend the system model with parameter uncertainty. To balance the system model precision and computation time, a Moving Window (MW) method is used to determine the applied historical statistics. The FMPF based FTC strategy is developed for the amended system model where the system estimation and Linear Quadratic Regulator (LQR) are updated at the end of system sampling period. The simulations of DRAS system subjected to multiple faults have been performed and the results indicate the effectiveness of the proposed approach.     

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。     

纵向短周期模态试飞方法和选择

对几种纵向短周期模态试飞方法及其适用性进行了初步分析。结合实际飞行经验,提出了初步判断飞机短周期特性及选择试飞方法。     

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。     

基于模糊粗糙集的飞机远程故障诊断模型研究

粗糙集和模糊集结合在飞机故障诊断领域有巨大的发展潜力。针对当前飞机故障诊断中所存在的问题,借助C/S模式,将粗糙集和模糊集结合,提出一种新型的飞机远程故障诊断模型,并且开发出远程诊断系统框架。系统的可行性和有效性得到了飞机故障诊断实例的证实。该系统具有客户端响应快、系统效率高、诊断及时的特点,对于当前民用飞机远程故障诊断具有重要应用价值。     

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     

动态差压检测系统的共模误差研究

主要针对以差压变送器为核心的动态差压检测系统的特点 ,应用流体网络分析方法 ,提出了动态差压检测系统中的共模误差问题。理论分析和仿真计算结果表明 :共模误差与检测系统两侧引压管路的不一致性直接相关 ,为了获得动态差压信号的准确检测 ,必须对动态差压检测系统的共模误差予以抑制     

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。     

正—反激组合式变换器与单端式变换器的比较

以５０Ｗ变换器为例，分别从磁性元件的大小，功率管上的电应力、磁恢复问题，输出纹波及输出特性等方面，详细分析比较了组合式变换器与单端式变换器的优缺点，并给出了具体数据，提出了正－反激组合式变换器具有电压应力低，磁性元件小，体积小，重量轻，输出纹波小及可靠性高的结论。