小波分析技术在陀螺故障诊断中的应用研究

小波分析是一种全新的时一频两维分析技术，其对高频突变信号和低频缓变信号的分析有其独特的优点。本文分析了小波分析技术的原理，提出了将其引入机械设备故障诊断中的方法。通过对有故障陀螺振动信号的多尺度分析，研究故障信号小波变换轴心轨迹分量图，建立典型故障特征模型，从而对单故障及多故障问题进行分析与诊断。     

用GPS卫星信号探测空中目标

利用GPS卫星信号全天候、全球实时覆盖的特点,研究以GPS卫星信号为辐射源,连续接收空中目标飞经GPS信号场的全息信号,设计了基于特征点触发采集的信号采集系统,提取目标的前向散射信号,根据目标的飞行速度确定信号的频带。在此基础上,进行目标前向散射信号的频谱分析,检测由于前向散射效应引起的信号电平突变,从而实现了对空中目标的探测。模型飞机和民航客机的探测试验结果表明,以GPS卫星信号作为辐射源,可以有效地探测到低空和高空目标飞行物。     

基于奇异谱分析的BDS卫星钟差周期项提取

在建立星座自主时间基准时，必须扣除卫星钟差的周期波动，以避免将其引入系统时间。为准确地扣除周期波动，提出一种基于奇异谱分析（SSA）的BDS卫星钟差周期项提取方法。首先，经SSA分解获得钟差信号的多个重构成分；然后，引入重标极差分析方法计算各重构成分的Hurst指数，根据Hurst指数辨识钟差的低频信号主导分量和高频信号随机分量，从而实现周期项的重建提取。对不同类型的BDS卫星钟进行了分析和研究，结果表明：所提方法能够准确提取钟差周期项，所提取周期项的频谱特征比钟差多项式拟合残差的频谱更为清晰；利用多项式拟合方法扣除周期项后，万秒稳和日稳平均分别提升4.1%和2.4%，而利用所提方法扣除周期项后，万秒稳和日稳平均分别提升20.9%和34.1%，提高了卫星钟频率稳定度，为星座自主时间基准的建立提供了基础。     

基于奇异值分解的航空发动机转子碰摩故障特征提取方法

提出了利用奇异值分解(SVD)提取航空发动机转子碰摩故障特征信号的方法,通过数值仿真得到,奇异值分解方法可以非常有效地将各种不同特征信号完全提取分离,论证了该方法的可行性。然后以某型国产航空发动机在试车台开车过程中某稳定状态的转子振动测量数据为基础,利用奇异值分解的优选差分谱理论对振动信号进行了降噪处理;根据傅里叶变换频谱图中各频率点峰值与奇异值分解差分谱峰值序列相对应的特点,对降噪后振动信号进行了特征提取,实现了转子系统的碰摩故障特征信号的提取。实际结果表明,该方法能够有效地诊断转子系统碰摩故障及提取相应的故障特征信号。     

压电智能结构冲击定位方法的实验研究

基于压电智能结构，构成冲击荷载监测系统，冲击样本的应力波信号经Hilbert变换变为连续的瞬态时序信号后，提取时间和峰值等特征信息训练BP神经网络，在线监测时由神经网络根据压电响应信号映射冲击荷载的位置。实验表明，该方法定位精度高，稳定性好，用于层合板结构低能量冲击的健康监控是可行的。     

基于改进MP-WVD算法的核电厂建设爆破振动信号处理方法

针对爆破施工产生的振动问题，结合现有信号分析方法优缺点，引入MP-WVD组合算法解决信号分析过程中交叉项干扰问题，可提高时频分辨率，满足精确提取核电爆破振动响应信号时频分布特征的需求；将HHT算法引入MP算法数据的预处理，成功降低了MP算法运算复杂度，可为大数据量分析奠定基础。运用该算法对漳州核电一期工程场地平整土石方爆破工程振动监测信号进行分析，精确提取了爆破振动信号的时频特征。     

一种基于多参考信号ICA的重力信号提取方法

海洋重力测量包含大量噪声,其中的低频噪声与重力信号频率相近,常用算法难以有效抑制测量噪声提取重力信号。为了有效地消除海洋重力测量信号的噪声,提高信号的获取精度,根据独立分量分析理论,提出一种基于多参考信号独立分量分析的重力信号提取方法。采用经验模态分解将海洋重力测量信号分解为固有模态分量,同时采用卡尔曼滤波以及小波分解等算法处理重力测量信号,将卡尔曼滤波器和小波分解的滤波结果以及由固有模态分量重构的信号作为独立分量分析算法虚拟通道的参考信号,应用基于负熵的独立分量分析算法估计重力信号。基于实测重力信号对该方法进行了去噪试验,理论分析和试验结果表明,多参考信号独立分量分析的重力信号提取方法能有效的抑制干扰和恢复重力信号波形,与常用的重力信号处理算法相比,海洋重力数据的获取精度大约提高了30%。     

基于小波检测的捷联惯导系统初始对准抗干扰新方法

针对车载捷联导航系统初始对准中的人为或阵风干扰等问题，提出了一种基于多尺度小波系数内积的干扰信号检测算法。该算法利用惯性敏感器的输出残差来建立用于检测突变信号的跟踪信号，并利用信号边沿或快变部分小波系数表现出的特性，构造了一种小波检测信号，通过小波检测有效地抑制了外界的干扰。粗对准实验证明该算法的有效性。     

基于同心压电传感器的单模态Lamb波信号提取方法

Lamb波具有传播范围远、对小损伤敏感等优点,适用于飞机机翼、壁板等大面积结构的损伤检测,然而Lamb波的频散和多模态特性使得单模态难以识别和解析信号。本文提出一种适用于板壳结构损伤检测的单模态提取方法,设计并使用一种同心压电传感器,根据所激励各模态的幅值比来分解信号并补偿时间差,从而提取出单一模态的信号。通过仿真分析和铝板实验验证,结果显示本方法能够有效提取单一A_0模态信号,并在实验中成功识别了隐藏的裂纹损伤反射回波信号,使信号相对于传统方法变得更易解释和分析。     

提取壁湍流相干结构的数字滤波法

以三丝热线探头测得平板湍流边界层的数据为对象，提出用数字滤波技术将湍流信号分解为接近各向同性的小尺度涡和非各向同性的大尺度涡的方法。并用条件采样技术从大涡信号中提取相干结构。     

Unknown bearing fault diagnosis under time-varying speed conditions and strong noise background

The bearing vibration signal shows strong non-stationary property under time-varying speed conditions. In addition, the weak bearing fault characteristic is often submerged in strong background noise. How to accurately extract the unknown fault characteristic from the non-stationary vibration signal is the primary problem of bearing fault diagnosis. Stochastic resonance has been proved to be an effective weak signal enhancement method. Therefore, an unknown bearing fault detection technology of speed variation is proposed, which breaks through the periodicity limitation of the classical stochastic resonance on the input signal. It enables stochastic resonance suitable for the enhancement of non-stationary fault signal. Firstly, the non-stationary vibration signal is processed by the computed order tracking to obtain the stationary signal in angular domain. To extract the potential feature information, the bearing imaginary fault order index is constructed from the angular domain order spectrum. Then, the resonance response at the imaginary fault order is obtained. Finally, the coherence resonance theory is introduced to judge the bearing fault pattern through the resonance factor index of response order spectrum. The proposed method overcomes the fuzzy mapping relationship between the signal symptom and the bearing fault caused by speed variation. The experimental data analysis results provide effective support for the proposed method.

     

Rotation characteristic and granular temperature analysis in a bubbling fluidized bed of binary particles

In this work, a discrete particle model (DPM) was applied to investigate the dynamic characteristics in a gas–solid bubbling fluidized bed of binary solid particles. The solid phase was simulated by the hard-sphere discrete particle model. The large eddy simulation (LES) method was used to simulate the gas phase. To improve the accuracy of the simulation, an improved sub-grid scale (SGS) model in the LES method was also applied. The mutative Smagorinsky constant case was compared with the previously published experimental data. The simulation by the mutative Smagorinsky constant model exhibited better agreement with the experimental data than that by the common invariant Smagorinsky constant model. Various restitution coefficients and different compositions of binary solids were investigated to determine their influences on the rotation characteristics and granular temperatures of the particles. The particle translational and rotational characteristic distributions were related to certain simulation parameters.     

Rotation characteristic and granular temperature analysis in a bubbling fluidized bed of binary particles

In this work,a discrete particle model(DPM) was applied to investigate the dynamic characteristics in a gas-solid bubbling fluidized bed of binary solid particles.The solid phase was simulated by the hardsphere discrete particle model.The large eddy simulation(LES) method was used to simulate the gas phase.To improve the accuracy of the simulation,an improved sub-grid scale(SGS) model in the LES method was also applied.The mutative Smagorinsky constant case was compared with the previously published experimental data.The simulation by the mutative Smagorinsky constant model exhibited better agreement with the experimental data than that by the common invariant Smagorinsky constant model.Various restitution coefficients and different compositions of binary solids were investigated to determine their influences on the rotation characteristics and granular temperatures of the particles.The particle translational and rotational characteristic distributions were related to certain simulation parameters.     

Research on Haar Spectrum in fault diagnosis of rotating machinery

In this paper,Haar Transform(HT)is used in the fault diagnosis of rotatingmachinery,and the“Impulse Sharpness”is presented as a diagnostic index.At present,Fourier Spectrumanalysis is most widely used Compared with FFT,HT is more rapid incomputation and more effective in discrete approximation.It’s very suitable for theextraction of pulses in the signal.However,HT has some shortcomings.It’s greatlyaffected by the starting point and length of the sample.Here,the authors present a methodto improve the stability and comparability of Haar Spectrum.The fault imitating test ofrolling bearing is carried out,and the results obtained have verified the sensitivity of HaarSpectrum and Impulse Index to the fault.     

结构设计中的混沌优化方法及其改进

在变尺度混沌优化方法研究中．结合结构优化问题的特点,提出了一种改进的混沌优化求解算法,用来解决带有多种约束条件的结构优化设计问题。在有限元分析和优化设计软件JEFIX中实现了上述算法．并通过数值算例讨论了变尺度混沌优化方法在结构优化中的可行性及存在的问题,得到了一些重要结论。     

基于贝叶斯网络的智能故障诊断方法

首先提出了基于贝叶斯网络的智能故障诊断方法，它对于解决复杂设备诊断问题中存在的不确定性，关联性具有很大的优质。然后阐述了贝叶斯网络模型的数学描述及基于贝叶斯网络的故障诊断方法的基本思路和决策算法。最后以某型SINS／GPS组合导航系统的故障诊断应用实例说明了该方法的有效性。     

Numerical solutions for the transient flow in the homogenous closed circle reservoirs

There are many fault block fields in China. A fault block field consists of fault pools. The small fault pools can be viewed as the closed circle reservoirs in some case. In order to know the pressure change of the developed formation and provide the formation data for developing the fault block fields reasonably, the transient flow should be researched. In this paper, we use the automatic mesh generation technology and the finite element method to solve the transient flow problem for the well located in the closed circle reservoir, especially for the well located in an arbitrary position in the closed circle reservoir. The pressure diffusion process is visualized and the well-location factor concept is first proposed in this paper. The typical curves of pressure vs time for the well with different well-location factors are presented. By comparing numerical results with the analytical solutions of the well located in the center of the closed circle reservoir, the numerical method is verified.     

基于最优奇偶向量检测的周跳检测

正确地检测和修复周跳是载波相位测量数据处理中的重要问题，中将周跳作为GPS卫星信号故障的一种特殊形式，把周跳检测问题转化为GPS载波相位测量的故障检测问题。这里采用鲁棒性好的最优奇偶向量方法检测周跳并进行修复。通过仿真结果验汪了该方法即使在动态情况下也可以有效地检测并修复小周跳。     

An adaptive vibrational resonance method based on cascaded varying stable-state nonlinear systems and its application in rotating machine fault detection

A weak character signal with low frequency can be detected based on the mechanism of vibrational resonance (VR). The detection performance of VR is determined by the synergy of a weak low-frequency input signal, an injected high-frequency sinusoidal interference and the nonlinear system(s). In engineering applications, there are many weak fault signals with high character frequencies. These fault signals are usually submerged in strong background noise. To detect these weak signals, an adaptive detection method for a weak high-frequency fault signal is proposed in this paper. This method is based on the mechanics of VR and cascaded varying stable-state nonlinear systems (VSSNSs). Partial background noise with high frequency is regarded as a special type of high-frequency interference and an energy source that protrudes a weak fault signal. In this way, high-frequency background noise is utilized in a VSSNS. To improve the detection ability, manually generated high-frequency interference is injected into another VSSNS. The VSSNS can be transformed into a monostable state, bistable state or tristable state by tuning the system parameters. The proposed method is validated by a simulation signal and industrial applications. The results show the effectiveness of the proposed method to detect a weak high-frequency character signal in engineering problems.

     

Local joint information based active fault tolerant control for reconfigurable manipulator

This paper is concerned with the active fault tolerant control problem for reconfigurable manipulator actuator based on local joint information. It is considered that the entire reconfigurable manipulator system consists of a couple of independent joint modules as subsystems, which are controlled using unified radial basis function neural network adaptive algorithm using local joint information when actuators are fault free. For the subsystem in actuator fault situation, fault detection is achieved through comparing the user defined threshold to the residual between actual velocity value and nonlinear velocity observation value. The unknown input state observer is exploited for fault identification. Based on the information aforementioned, a compensation term is added to the proposed control algorithm for switching to realize active fault tolerant control when subsystem in fault. The advantages of the presented scheme are that unlike the complex control structure in centralized control, this scheme possesses simple control structure, as well as could isolate and tolerant the fault in subsystem. Furthermore, it can be easily applied to different configurations without any parameters modification. It means that the local fault could not affect the joint in normal situation. In order to demonstrate the effectiveness of the proposed method, two different 2-DOF reconfigurable manipulators are employed for simulation.