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.     

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.     

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。     

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。     

基于PETRI网的导弹控制系统故障诊断梯形图求解法

故障诊断是保证航空航天等大型复杂系统可靠性的重要措施，故障树为此提供了一种有效的方法，但故障树是一种系统静态行为的描述方法，如何描述系统状态的动态变迁过程成为人们追求的目标，本文在论述逻辑关系的PETRI网表示的基础上，提出了系统状态动态变迁过程的PETRI网模型梯形图求解算法，进而提出了基于PERTI网的故障诊断的梯形图求解方法，并用一实例说明了这一过程。     

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     

面向航天器控制系统故障诊断的实时专家系统通用框架

本文介绍了一个可用于航天器控制系统故障诊断的实时专家系统框架，阐述了系统的设计思想和实现方法。该框架可适用于不同的航天器控制系统。     

SOM神经网络在卫星电源系统故障诊断中的应用

研究了自组织特征映射（SOM）神经网络的结构及学习算法，提出了利用SOM神经网络对输入样本的“聚类”作用，实现对故障模式的分类，并据此对故障进行诊断的新方法，通过对某卫星电源系统故障的诊断实例，验证了该方法是有效可行的，能对卫星电源系统的故障进行准确的实时诊断。     

自动驾驶仪可靠性设计的故障树分析

以地空导弹自动驾驶仪系统可靠性的故障树分析（ＦＴＡ）为例，建立了故障树、结构函数，并进行了系统可靠度计算。实践表明，本方法具有通用性，也可用于其他弹上设备的故障树分析。     

控制系统故障诊断的模糊神经网络方法研究

控制系统的不确定故障诊断是目前尚未解决的问题。传统的神经网络方法和模糊推理方法为解决这一类故障诊断问题提出了一些算法。然而上述两种方法难以提高不确定故障诊断的性能。为此本文结合模糊理论的推理能力和神经网络学习的能力提出模糊神经网络故障检测方法。该算法同时具备模糊理论的处理不确定和不准确信息的能力和神经网络的自学习能力。本文结果应用到某运载火箭控制系统的故障诊断,仿真结果表明,本算法有效,较好地解决了控制系统不确定故障诊断问题。