High-accuracy ultrasonic temperature measurement based on MLS-modulated continuous wave

Ultrasonic temperature measurement has the potential to improve measurement accuracy by increasing the length of a received signal due to its excellent performance with noise resistance. However, when the distance between the transmitter and receiver is limited, the received signal can be polluted by strong multiple echoes, which can significantly degrade temperature accuracy. This paper proposes a method for high-resolution ultrasonic temperature measurement. With the use of a maximum length sequence (MLS)-modulated continuous wave, the obstructive effect of echoes is effectively suppressed. A hybrid method is employed for accurate time-of-flight (TOF) estimation by incorporating both cross-correlation and phase shift (PS), which is the basis of highly accurate temperature measurement. The experimental results in distilled water show that the proposed method estimates TOF with a standard deviation of less than 0.3 ns, and temperature errors consistently remain within ±0.04 °C.     

Estimating ultrasonic time of flight using envelope and quasi maximum likelihood method for damage detection and assessment

Time of flight (TOF) is widely used to locate and size faults in ultrasonic non-destructive evaluation (NDE). In this paper, we propose a novel model-based method for ultrasonic echo parameter estimation. The ultrasonic signal is assumed to be composed of an unknown number of Gaussian echoes corrupted by white Gaussian noise. Firstly, the Hilbert transform is used to extract the envelope of the signal. It is shown that the parameter estimation of the signal is improved by using the envelope. To estimate the parameters of the envelope of the signal, quasi maximum likelihood method is used. The number of echoes is estimated using consistent Akaike information criterion. Two measures are used to evaluate the performance of the proposed method: (a) probability of detection of backscattered echoes and (b) the error of estimated time of flights. The proposed method is then compared to the cross-correlation method and the maximum likelihood method which uses the original signal. Simulated and experimental signals are used to evaluate the performance of each method. Both experimental and simulated results show that the proposed method can improve the parameter estimation which ultimately enhances the damage detection and assessment.     

波长轮换与相移扫描相结合的表面形貌测量系统

用光学显微干涉法进行表面形貌测量时其深度测量范围的扩大和形貌测量精度的提高是一对矛盾。为此,本文设计出了一种基于波长轮换与相移扫描相结合的三波长表面形貌测量系统,并提出了一种基于椭圆拟合与相位差大小尺度相结合的相位提取与识别算法。将这种算法运用于多波长干涉图像的数据处理,有效地提高了形貌的整体测量精度,并拓展了深度测量范围。实验结果表明:在深度测量范围扩大近15倍的条件下,采用粗糙度国家基准校准的方波多刻线样板得到的表面粗糙度数据与校准数据的相对误差仅为4.12%,表明该系统在一定的深度范围内能够实现表面形貌的高精度测量。另外,针对该系统设计的多波长相位识别算法对环境噪声要求不高,可以支持系统的高噪声或在线测量。     

基于单测头误差相位偏移法的嵌入式角位移传感器自校准研究

针对嵌入式角位移传感器长期使用出现精度损失问题,结合传感器结构特点提出一种基于单测头误差相位偏移法的自校准方法。首先,依据圆周空间封闭性原则,基于傅里叶变换分析传感器误差特性;然后,以起始测量值序列作为误差偏移起始基准,根据误差特性等间隔变换误差序列并解算误差与起始基准的函数关系,构建传感器误差模型;最后,搭建在线校准实验平台进行验证实验。实验结果表明,单个对极内误差结果与测量误差特性一致,整周测量误差大幅度降低,误差峰峰值由127.80″降低至5.90″;该方法校准效率相比于比较校准法提升了80.69%,校准后误差分布集中,同时具有良好的测量稳定性。本文所述自校准方法在不依赖高精度基准器具前提下,有效解决了传感器精度损失问题,对实现嵌入式角位移传感器自校准具有重要的应用意义。     

基于循环平移和DTCWT的声呐图像滤波方法

声呐图像散斑噪声的存在严重影响声呐图像的人工判读和自动识别。声呐图像散斑噪声滤波是声呐图像处理领域的一个重要难题。双树复小波变换(DTCWT)因其具有近似的平移不变性、良好的方向选择性已被应用于图像滤波。但这种平移不变性的近似性使得DTCWT不能较为彻底地抑制伪吉布斯现象,而导致滤波后图像的边缘存在一定程度的模糊。为了减小伪吉布斯现象和声呐图像边缘模糊,将循环平移与DTCWT结合使用,提出一种基于循环平移和DTCWT的声呐图像滤波方法。实验结果表明,该方法在滤除声呐图像散斑噪声的同时能够较好地保持声呐图像的边缘。     

基于超声透射时差法的金属棒缺陷检测研究

本文提出了一种基于超声透射时差法检测缺陷的方法。首先对超声探头的声场特性分析研究,完成回波信号的转换和调理;然后控制时间数字转换器,得到回波信号的时差值;将采集到的不同时差值进行算法分析,并用数字信息量化出缺陷值,最终转化为可视的二维数据图。通过设计的系统对黄铜棒进行缺陷检测,采集的回波信号时差值的分辨率可达125 ps,缺陷测量误差小于±0.01 mm,径向和轴向分辨率可达0.007°,最大测量直径30 mm。实验结果表明缺陷检测系统具有较高的稳定性、可靠性和灵敏度。     

基于平均密度投影和平移高斯模型的肺结节检测与分割算法

针对 CT图像中肺结节与血管粘连导致分割困难的问题,提出了一种基于平均密度投影和平移高斯模型的肺结节检测与分割算法。首先通过对二维CT序列图像作平均密度投影(AIP),融合局部三维特征生成AIP图像,然后利用阈值分割和形态学方法对结节轮廓进行粗分割,最后通过建立平移高斯模型来拟合肺结节,从而实现对肺结节的精确分割。对30个血管粘连性肺结节CT图像的实验结果表明,本文算法与专业医师标记区域的面积交迭度达到91%,能够实现对粘连型肺结节的有效分割,但对于灰度较弱且体积较小的肺结节仍存在漏检的风险,需要后续进一步研究。     

基于变型波的超声TOFD近表面检测新方法

为了解决传统衍射时差法超声检测技术(TOFD)法对埋藏在近表面的缺陷波无法辨识及近表面区域的测量误差大、深度分辨力差等近表面盲区问题,提出了一种新的近表面检测方法。该方法利用变型波波速较慢的特点,通过计算变型波、直通波及底面反射波,得出了变型波在纵波检测窗口出现的条件,并利用变型波对缺陷进行检测,以减小测量误差并提高检测分辨力,有效地解决了TOFD近表面盲区问题。在10个不同深度的人工缺陷中,对采用基于变型波的检测方法和传统检测方法的实验结果进行了比较和分析,并分别计算了其测量误差及检测分辨力。实验结果表明,该方法能够识别传统方法无法辨识的近表面缺陷,可有效检测到埋藏深度2.0 mm的人工缺陷,同时,该方法具有较高的测量精度和分辨力。     

A novel method of Coriolis flowmeter phase difference measurement based on improved correlation analysis method

The shortcomings traditional phase difference extraction algorithms have such as being susceptible to noise interference, requiring full cycle conditions, leads to a decrease in phase difference extraction accuracy. To improve the accuracy of the phase difference extraction, two improved correlation analysis methods are proposed in this paper, one of which is based on the relationship between the extreme value of the cross-correlation function and the time difference, the other of which combines correlation analysis method with Hilbert transform. A simulation and experiment were set up to explore the influence of sequence length and signal-to-noise ratio on the extraction accuracy, as well as the influence of Kalman filtering. An experiment is set to compare the extraction mean error and variance of different algorithms under different phase differences. Experimental result shows that the improved algorithms have higher accuracy and stability. The feasibility of the two improved algorithms is verified.     

基于Harris角点检测和聚类算法的掌纹图像ROI提取方法

为了实现从掌纹图像中提取出稳定的感兴趣的区域(ROI)图像的目标,设计了一种新的掌纹ROI提取方法。使用Harris角点检测算法对二值化后的掌纹图像进行角点提取,收集相应区域的角点,利用聚类算法得到角点簇的中心点坐标,从中寻找关键点建立坐标系,提取ROI。分别利用该方法在不同掌纹数据库中进行了ROI提取实验。实验结果表明,该方法对不同的掌纹数据库图像均能保持很好的提取效果,成功率均达到了99%以上。     

基于UC3879的移相全桥式软开关弧焊逆变焊机

为将大功率软开关逆变技术应用到弧焊逆变电源中,设计了一种基于UC3879峰值电流控制模式的移相全桥软开关弧焊逆变电源,开关频率200kHz,并试制了一台5kW的弧焊逆变电源样机.实验结果显示其满足焊机工作要求,验证了设计方案的可行性,为工程应用提供了基础.     

基于相移和颜色分光的电子散斑干涉瞬态三维变形测量方法

提出了一种基于相移和颜色分光的电子散斑干涉(ESPI)瞬态三维变形测量方法,该方法包括一个彩色CCD和红绿蓝三种不同波长的激光器,可同时采集来自三路的散斑干涉图像。物体面内水平方向、竖直方向以及离面方向的散斑干涉图像信息通过颜色分光实现分离,并利用相移算法对散斑干涉条纹图进行分析处理,分别解调出水平、竖直及离面方向的变形场相位,实现三维变形场的测量。模拟及实验分析表明,此方法能同时实现物体面内水平方向、竖直方向以及离面方向的变形测量,可用于物体表面的三维瞬态变形测量,也可单独完成面内或离面的二维变形测量。     

基于提升小波的颤振图像边缘检测方法研究

这里基于提升小波理论重点研究了图像边缘检测问题，提出了一种新的图像多尺度边缘检测方法，该检测方法适用于多种线性或非线性双正交小波，具有运算速度快、检测精度高等特点；并基于此边缘检测技术，研究了飞机结构颤振的边界预测问题，给出了一种新的稳定性参数估计方法。     

基于小波包移频算法的AR功率谱在滚动轴承故障特征信息提取中的应用

为克服经典的小波包迭代算法由于小波包分解过程中的隔点采样而发生的频率混叠现象，本文采用移频算法进行小波包分解与重构，以1#、2#、3#这3个608滚动轴承(其中1#轴承工作正常，而2#、3#轴承工作异常)进行分析。先通过小波包分解提取这3个轴承振动信号的频带能量特征以确定2#、3#轴承故障特征信息所在的频带。并按这些频带分别对2#、3#轴承的振动信号进行小波包重构。通过对重构信号的基于AR模型的功率谱分析以实现滚动轴承故障特征信息的自动提取．从而对2#、3#轴承的故障作出诊断。     

Effects of the number of pulse repetitions and noise on the velocity data from the ultrasonic pulsed Doppler method with different algorithms

The ultrasonic pulsed Doppler technique known as the ultrasonic velocity profile (UVP) method has been widely used in many engineering fields. The analysis algorithms of the UVP, the number of pulse repetitions (N_pulse), noise and reflector conditions, etc. all affect the measurement accuracy. N_pulse is related to the temporal resolution, thus to improve this resolution it must be set as low as possible. However, it is known that the measurement accuracy of the instantaneous velocity becomes worse with decreasing values of N_pulse. In this study, UVP analysis algorithms including the fast Fourier transform (FFT), autocorrelation, and the wavelet transform (WT) were compared via simulations and experiments using varying values of N_pulse and the signal-to-noise ratio (SNR). We show that there is an appropriate N_pulse for each algorithm that depends on the SNR; specifically, the value of N_pulse increases with decreasing SNR. The difference between the algorithms for the velocity data was small under low noise conditions. However, a FFT with a Gaussian interpolation produced the best result under noisy conditions. In contrast the WT was relatively unaffected by noise. Therefore, a WT is the preferred choice for measuring velocity distributions if high sampling measurement is not required.     

基于CEEMD-排列熵的循环策略信号提取方法

针对EEMD阈值降噪处理方法时效性差且噪声难以准确估计问题,提出了基于CEEMD-排列熵的循环策略信号提取方法.原始信号经CEEMD处理,对信号叠加相反白噪声抑制白噪声引起重构误差的同时简化了计算方法,对分解得到的本征模态分量通过计算排列熵确定噪声分量和信号分量,考虑到信号中噪声先验知识未知,在奇异值分解的基础上,建立...     

一种基于频率的结构损伤识别方法及识别矩阵的建立

本文介绍了一种比较好的损伤识别方法。这种方法要求建立损伤识别矩阵,损伤识别矩阵的建立是这个方法是否能准确识别的关键,在研究中,发现根据不同的标准建立的识别矩阵识别的效果不同;并进一步指出如何建立能够准确识别损伤的识别矩阵。     

A self-sensing technology of active magnetic bearings using a phase modulation algorithm based on a high frequency voltage injection method

Conventional AMB(active magnetic bearings) systems consist of electromagnetic coils, position sensors, power amplifiers and a feedback controller. This hardware configuration can lead to a structural complexity, problems of space limitations for the installation, and position control difficulties due to the non-collocation of actuators and sensors. In this paper, a self-sensing mechanism is proposed to resolve such limitations of the general AMB system. The proposed self-sensing scheme uses a phase difference of the injected current of two opposite electromagnetic actuators while an object is levitating between the actuators. The relationship between the phase difference of injected currents and the position of a levitated object was theoretically derived and linearized. In order to realize the proposed self-sensing scheme, a signal processing algorithm was developed. The frequency response of the estimator was measured to verify the performance of the proposed self-sensing scheme. In addition, a magnetic levitation and a disturbance rejection response were experimentally obtained to verify the feasibility of the proposed self-sensing mechanism. Experimental results showed that the developed self-sensing technique has similar performance as a practical gap sensor.     

A novel measurement method for transient detection based in wavelets entropy and the spectral kurtosis: An application to vibrations and acoustic emission signals from termite activity

This paper presents a novel non-destructive method for termite detection that uses the entropy of the continuous wavelet transform of the acoustic emission signals as an uncertainty measurement, to achieve selective frequency separation in complex impulsive-like noisy scenarios, with the aid of the spectral kurtosis as a validating tool. The goal consists of detecting relevant frequencies, by looking up the minima in the curve associated to the entropy of the difference between the raw data and the wavelet-based reconstructed version. By measuring the signal’s uncertainty, the scales corresponding to the entropy minima, or pseudo-frequencies, manage to target three main types of emissions generated by termites: the modulating components (enveloping curve), the carrier signals (activity, feeding and excavating), and the communicating impulses bursts (alarms). The spectral kurtosis corroborates the location of the entropy minima (optimum uncertainty) matching them to its maxima, associated to frequencies with the highest amplitude variability, and consequently minimizing the measurement uncertainty. The method is primarily conceived to cover the acoustic-range, in order to acquire signals via standard sound cards; a broaden high-frequency study is developed for the assessment, and with the added value of discovering new and higher frequency components of the species emissions. The potential of the method makes it useful for myriads of applications in the frame of nondestructive transient detection.     

基于有限单元法的热风阀阀体模态和谐响应分析

采用有限元分析软件ANSYS对热风阀阀体进行了模态分析和谐响应分析。给出了阀体前4阶的固有频率和振型，并得出了前四阶固有频率下激振力和动应力问的关系。该分析方法和结论为阀体的消除焊接残余应力振动时效工艺提供了理论依据。