小波分析在强流直线感应加速器信号处理中的应用

 应用小波变换具有良好的时频局部特性，通过对强流直线感应加速器(LIA)脉冲信号的去噪声、信号突变点检测以及时间间隔测量等处理，表明小波变换在LIA信号处理中有广泛的应用前景；利用小波包分析的每个节点都代表了对应频带的信号特征的特点，对“神龙一号”快脉冲波形数据进行小波包变换，以各频带信号能量为元素构造特征向量，实现了高维波形数据的特征值提取，达到了数据压缩和降维的目的，为进一步实现LIA故障智能诊断、预测维护提供了一种可行的途径。     

猝发双脉冲直线感应加速器组元研究

直线感应加速器（LIA）是产生强流、高能电子束的重要装置之一。现有的直线感应加速器均以单次方式工作，加压一次只能加速一个束流脉冲打靶，而实际应用常希望其能工作在高重复频率的猝发多脉冲模式下。对原单脉冲LIA进行猝发双脉冲改造，无疑是获得多脉冲LIA最经济最简单的方法。     

基于小波包分解和EMD-SVM的轴承故障诊断方法

针对轴承振动信号具有的非平稳和故障诊断样本数据难以按需获取的问题,设计了一种基于小波包分解和EMD-SVM的故障诊断方法。首先,采用Mallat塔式算法对信号进行降噪,实现信号的小波分解,获得重构后的故障诊断子频带信号。然后,在经典的EMD算法的基础上定义了改进的EMD算法,采用改进的EMD算法对经过小波包降噪的故障诊断子频带信号进行特征提取,从而获得故障诊断特征向量。最后,采用适合小样本分类的SVM进行故障诊断,将经过小波包降噪和EMD特征提取的样本数据用于训练SVM,得到用于故障诊断的多个二分类SVM故障诊断模型,通过投票机制来确定样本数据最终对应的故障诊断类别。在Matlab环境下对轴承故障诊断进行实验,实验结果证明了文中基于小波包和EMD-SVM的方法一种适用于小样本的故障诊断方法,且与其它方法相比,具有诊断效率高和精度高的优点。     

提升小波包和改进BP神经网络相融合的新故障诊断算法

针对传统的小波变换和BP神经网络应用于故障诊断中存在自适应性差、效率低等问题,提出一种提升小波包和改进BP神经网络相融合的新故障诊断算法;利用插值细分思想,设计了提升小波包的预测算子和更新算子,结合传统小波包算法和提升模式的原理,完成了提升小波包算法的设计,并将该算法应用于故障信号的消噪和能量特征量的提取;利用遗传算法优化标准BP神经网络的初始权值和阈值,采用L-M算法优化标准BP神经网络的搜索方式;以美国凯斯西储大学提供的滚动轴承实验数据,将新算法应用于实验中,分析结果表明:新故障诊断算法比传统的BP神经网络算法具有收敛速度快、诊断精度高等实效性。     

强流电子束时间分辨系统瞬态脉冲干扰的抑制

强流电子束时间分辨测量系统在直线感应加速器(LIA)环境中会受到一些短暂的高能脉冲干扰,这些瞬态脉冲干扰既针对电路又针对测量系统,这对测量系统电子设备危害很大。介绍了时间分辨测量系统的原理,分析了瞬态脉冲干扰的成因和抑制方法,给出了束参数测量系统的实验布局和特点,分析瞬态脉冲干扰对LIA中测量系统电子器件电性能的影响和变化规律,并进一步探讨电子器件电性能受瞬态脉冲干扰后的抑制措施。通过采用光纤传输控制信号的措施,能很好地传输窄脉冲,信号延时抖动小,达到了高速信号的可靠传输要求,利用紧凑嵌入式方法,提高了抗电磁干扰的能力,这样可以更好地保护束参数测量电子器件,提高了整个系统的抗干扰能力及可靠性。     

非线性时间序列的小波分频预测

基于噪声的小波变换特点，结合小波包分解和模极大重构来抽取含噪信号的主分量，提出了一种基于最佳尺度分解和Volterra自适应滤波的分频预测算法，使用较少的模型训练样本，同时具有强的抗噪能力.该算法克服了传统小波分解尺度选取的盲目性及单纯Volterra预测器抗噪性能的不足，数值仿真表明，针对含强噪声的非线性信号可进行有效预测. 关键词： 小波分解 Volterra自适应滤波器 分频预测     

LIA注入器发射度增长机理研究及其抑制措施

闪光X光照相是强流直线感应加速器（LIA）目前最重要的应用之一。为了获得更小的焦斑尺寸以达到精密照相的目的，减小LIA的发射度成为主要的技术途径之一，因此首先要求作为LIA束源的注入器能提供低发射度的带电粒子束。本文从非线性作用力导致束剖面电流密度分布变化的角度对注入器发射度的演变进行研究，根据研究揭示的规律提出相应的抑制发射度增长的措施。     

直线感应加速器输运磁场遗传算法优化

以直线感应加速器（LIA）匹配磁场设计和束线调谐为背景,提出解决强流相对论电子束长距离、小波动、多元件磁约束的输运优化问题的数值优化办法,建立基于遗传算法的优化程序。结合束质心轨迹及束包络耦合模型,设计描述束传输半径波动大小的评价函数,采用励磁元件馈入电流为优化对象,解决LIA磁场配置组合爆炸优化问题。计算结果表明:优化程序可依据不同的初始束流,有针对性地快速给出一组符合束输运要求的励磁电流配置。研究成果为在建的LIA装置束线调谐提供一种重要的数值分析工具。     

强流质子加速器PXIe控制器的子控制系统

针对一台用于加速器驱动次临界系统(ADS)的强流质子加速器,即ADS注入器Ⅱ,开展其定时系统和快保护系统的研究工作,设计了一款可在这两个系统中应用的PXIe控制器。该控制器基于现场可编程门阵列设计,可实现光纤通信、PXIe总线通信、延时触发信号输出等功能。其中,光纤通信功能用于实现各PXIe控制器之间的数据传输;PXIe总线通信功能主要是为了实现控制器与工控机之间的数据交互;延时触发信号输出功能可以给出精确的低电压晶体管-晶体管逻辑电平标准的时序触发信号,从而保证加速器的各子系统和设备能够按照一定的时序协调一致地工作。     

神龙-Ⅲ直线感应加速器高稳定度恒流源控制系统

大功率恒流源是强流直线感应加速器(LIA)的关键设备之一,用于为加速器电感线圈提供大功率准直流驱动电流,其稳定度、纹波系数等指标要求极高。神龙-Ⅲ LIA恒流源采用串联线性双闭环回路双参量电流调控技术,同时综合应用了以PLC控制器为核心的本地控制、以ARM控制器和工控机为核心的远程控制以及以太网网络通讯技术,实现了强电磁干扰环境下远程控制高稳定性运行。该恒流源在负载0.5~0.6 Ω之间变化、输出电流在50~170 A之间变化时调整管压降控制在8 V±2 V范围内,输出电流纹波和电流稳定度均优于0.5‰。     

Heath monitoring of a glass transfer robot in the mass production line of liquid crystal display using abnormal operating sounds based on wavelet packet transform and artificial neural network

This paper presents the fault detect method of a moving transfer robot in the mass production line of liquid crystal display (LCD) manufacturers based on the wavelet packet transform (WPT) for feature extraction and the artificial neural network (ANN) for fault classification. Most of fault detection methods in a mechanical system have been researched based on the vibration signal. Unlike the existing methodologies, this study aims to minimize the uncertainty of a field engineer's decision making process for determining whether a fault is present or not based on the human auditory perception by developing a fault diagnosis system that uses the abnormal operating sound radiated from a moving transfer robot as a source signal. Abnormal operating sound radiated from a moving transfer robot has been used for this work instead of other source signals such as vibration, acoustic emission, electrical signal, etc. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone despite a relatively low sensitivity. In the application of ANN, since it is important to minimize the error of trained ANN in terms of the accuracy of fault diagnosis logic, in the paper, the number of input and target data samples was increased through a regeneration process based on statistical properties, and then the uncorrelated nodes in the input vector were also removed to improve the orthogonality of the input vector based on the entropy based feature selection method. Consequently, it can be concluded that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.     

基于成像机理的小波包变换多聚焦图像融合

由于可见光成像系统的聚焦范围有限,因而在成像过程中,除聚焦良好的物体能生成清晰的图像外,该物体前后一定距离外的所有物体都将呈现不同程度的模糊.为了获得场景内所有物体均清晰的图像,在分析了多聚焦图像成像机理的基础上,提出了一种基于小波包变换的融合方法.它是将成像系统先聚焦在一部分对象上,得到其清晰的图像;然后再将其聚焦在另一部分对象上,得到另一清晰的图像;最后把这两幅实验图像加以融合,从而获得场景内所有物体均清晰的图像.实验结果表明,基于小波包变换的融合方法能够将信号的频带进行多层次划分,对高频成分也能进一步地分解,可有效综合多聚焦图像.     

A wavelet approach to fault diagnosis of a gearbox under varying load conditions

Varying load can cause changes in a measured gearbox vibration signal. However, conventional techniques for fault diagnosis are based on the assumption that changes in vibration signal are only caused by deterioration of the gearbox. There is a need to develop a technique to provide accurate state indicator of gearbox under fluctuating load conditions. This paper presents an approach to gear fault diagnosis based on complex Morlet continuous wavelet transform under this condition. Gear motion residual signal, which represents the departure of time synchronously averaged signal from the average tooth-meshing vibration, is analyzed as source data due to its lower sensitiveness to the alternating load condition. A fault growth parameter based on the amplitude of wavelet transform is proposed to evaluate gear fault advancement quantitatively. We found that this parameter is insensitive to varying load and can correctly indicate early gear fault. For a comparison, the advantages and disadvantages of other measures such as kurtosis, mean, variance, form factor and crest factor, both of residual signal and mean amplitude of continuous wavelet transform waveform, are also discussed. The effectiveness of the proposed fault indicator is demonstrated using a full lifetime vibration data history obtained under sinusoidal varying load.     

光学小波包变换及其滤波器的研究

基于对光学小波变换必要条件的分析，提出光学小波包变换的概念.选出虹膜图库的联合最优小波包基，利用最优基的线性组合生成相应的复合光学小波包滤波器.将滤波器用于光电混合虹膜识别系统中对待识别输入进行小波包特征提取预处理，模拟结果不仅证明引入该滤波器可明显提升系统的识别效果，也证明了光学小波包变换提出的意义.     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

Wayside acoustic defective bearing detection based on improved Dopplerlet transform and Doppler transient matching

The diagnosis of train bearing defects plays a significant role in maintaining the safety of railway transport. However, the phenomenon of Doppler Effect in the acoustic signal recorded by the wayside Acoustic Defective Bearing Detector (ADBD) system leads to the difficulty for fault diagnosis of train bearings with a high moving speed. This paper proposes a double-searching solution based on improved Dopplerlet transform and Doppler transient matching to overcome the difficulty in wayside acoustic bearing diagnosis. In the solution, the first searching procedure is to extract necessary parameters of Doppler Effect under the situation with very low signal-to-noise ratio (SNR) based on an improved Dopplerlet transform. Using the obtained parameters, the Doppler Effect can be embedded into the constructed periodic Laplace wavelet transient models. Subsequently, the second searching procedure is conducted to search fault impact period of the defective bearing through an operation, called Doppler transient matching, which is to calculate the correlation coefficient between the Doppler transient model and the filtered raw signal with the Doppler Effect. The proposed double-searching algorithm can adapt to the real Doppler Effect situation and extract the exact fault impact period from the Doppler distorted signal, and thus shows powerful capability to analyze wayside acoustic signals from train bearings. The proposed wayside acoustic diagnostic scheme is verified by means of a simulated Doppler distorted signal with a very low SNR (−20 dB) and the experiments conducted on train bearings. The results indicate that the proposed algorithm is effective and has obvious advantages for ADBD system.     

Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing

In ultrasonic non-destructive testing of materials with a coarse-grained structure the scattering from the grains causes backscattering noise, which masks flaw echoes in the measured signal. Several filtering methods have been proposed for improving the signal-to-noise ratio. In this paper we present a comparative study of methods based on the wavelet transform. Experiments with stationary, discrete and wavelet packet de-noising are evaluated by means of signal-to-noise ratio enhancement. Measured and simulated ultrasonic signals are used to verify the proposed de-noising methods. For comparison, we use signal-to-noise ratio enhancement related to fault echo amplitudes and filtering efficiency specific for ultrasonic signals. The best results in our setup were achieved with the wavelet packet de-noising method.     

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.     

结点阈值小波包变换图像去噪新算法

小波包变换是小波变换的推广,可视为普通小波函数的线性组合,具有灵活的时频分析能力,随着分解层数的增加,小波包分解能够在所有的频率范围聚焦。提出一种应用结点阈值小波包变换的新型图像去噪算法。利用小波包变换对含噪图像进行分解,在图像信号的子带层次上进行结点阈值操作,采用软阈值的方法进行阈值处理,结点噪声采用谱熵法估计,并使用峰值信噪比评估去噪后的图像质量。实验结果表明,相比于使用其它阈值方法的小波包图像去噪算法,该算法具有更好的图像去噪性能。