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.     

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     

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。     

悬停状态下旋翼模型桨叶气弹稳定性试验研究

详细介绍了旋翼桨叶气弹稳定性试验采用的试验模型，试验设备，试验状态和试验方法。重点论述了先进旋翼的结构形式，以及简单可靠的周期变矩激振方法。试验用液压作动简对自动倾斜器的不动环激振，共进行４６种状态结果，其结果符合要求，与理论分析结果有较好的一致性。该项试验的激振方法和数据采集处理技术可直接用于直升机型号研制中的旋翼气弹稳定性试验，为进一步发展我国旋翼气弹稳定性试验技术打下了良好的基础。     

转动旋翼模型动柔度的试验研究

介绍测定转动旋翼模型动柔度的试验方案、试验程序，综述以作者自行设计的旋翼模型进行试验所得到的结果及其与理论计算结果的比较。结果表明，试验方案与方法是可行的，理论计算公式是正确的。     

惯性式低频振动传感器畸变波形恢复问题初探

水电机组振动是表征水电机组运行稳定性的主要指标之一。然而，水电机组转频太低，适合于使用测量低频振动信号的惯性式低频振动传感器。由于传感器本身频率特性，造成其输出波形的低频段必然产生较大的畸变。为了获得实测低频振动信号的真实波形，对振动传感器输出的畸变波形恢复问题进行了初步探讨。