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.     

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。     

故障检测与分离的广义一致性算子法

本文提出了故障检测与分离的广义一致性算子法的一般形式。得出了两个故障可分离的充要条件和多个故障可分离的充要条件。最后给出了检测和分离故障的过程。     

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     

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。     

基于神经网络的导弹姿态控制系统故障诊断

本文提出了一种多层前向神经网络快速误差向后传播学习算法ＦＢＰ（ＦａｓｔｅｒｒｏｒＢａｃｋＰｒｏｐａｇａｔｉｏｎＬｅａｒｎｉｎｇＡｌｇｏｒｉｔｈｍ），通过某导弹姿态控制系统故障诊断的仿真研究，验证了ＢＰ（误差后向传播学习算法）和ＦＢＰ用于导弹姿态控制系统故障诊断的有效性，ＦＢＰ学习算法较之ＢＰ学习算法学习速度的快速性     

八作动器隔振平台的通道耦合分析及解耦控制

分析一种对称结构的八作动器隔振平台的通道耦合特性。分析表明由于结构对称，当有效载荷质心位于平台纵轴的延长线上，且有效载荷的惯性主轴与平台对称轴平行时，平台可分解成两个独立的单自由度系统和两个2阶耦合子系统。利用矩阵同时对角化方法对平台的2阶耦合子系统进一步解耦，仿真结果显示解耦控制能够衰减被动隔振的谐振峰，改进平台的隔振性能。     

FY—1C卫星高分辨率云图传输技术

介绍了FY－1C星传输系统的任务、方案、主要技术指标和组成。系统采用极化隔离加带外频谱抑制的方法，达到了信道隔离的技术指标。概述了星载记录器由磁带记录器向固态记录器转变的技术因素以及磁带记录器、FLASH和DRAM固态记录器的研制过程。简述了传输系统在轨测试和运行状况。