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.     

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。     

一种可伸缩空间机械臂及其应用分析

介绍一种能够完成多关节型空间机械臂所能承担的大部分空间任务 ,而且应用更简便的可伸缩空间机械臂 ,对其空间应用做出了一些设想 ,简单讨论了其应用的一些相关问题 ,肯定了其可行性和实用性。     

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。     

一类挠性航天器的变结构控制

本文研究在控制器能量受限条件下，一类挠性航天器的姿态控制问题。考虑刚性主体上带有挠性梁的航天器，并假定系统在一平面内作旋转运动。针对航天控制工程中执行机构的工作模式，基于系统的无穷维模型，本文设计了简单易行的变结构控制方案，并证明了相应的闭环系统的渐近稳定性。数值仿真和物理实验结果显示了所设计的控制算法的有效性。     

采用共位配置的分布式压电敏感器和致动器的挠性结构振动控制

研究采用共位配置的分布式压电敏感器和致动器的挠性悬臂梁的振动控制问题，其中敏感器由压电聚乙二烯氟化物薄膜（PVDF）制成，致动器由压电陶瓷（PZT）或PVDF制成。本文首先建立系统的模型，设计了一种线性反馈控制方案，并应用无穷维空间的LaSalle不变原理，证明了相应闭环系统的渐近稳定性。     

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。