一种用于管道泄漏声定位的可调Q因子小波变换分析方法

针对泄漏声发射信号的非平稳随机特性,进行了泄漏声信号可调Q因子小波变换(Tunable Q-Factor Wavelet Transform,TQWT)的理论分析和实验研究。首先通过对典型泄漏声信号的TQWT分析,研究品质因子和分解尺度对泄漏声特征提取的影响。然后根据峭度最大原则,优选出适合泄漏声信号分析的TQWT参数。在此基础上,提出了一种基于TQWT提取管道泄漏声特征分量的方法,并将提取的特征分量进行互相关分析用于管道泄漏定位。实验结果表明,利用该方法可以有效提取出泄漏声信号中的特征分量,从而更好实现泄漏定位,定位误差较小。该项研究工作为实际工程管道泄漏检测提供了可行的解决方案。      

自来水铸铁管道泄漏声信号频率特征研究

针对基于声发射技术的自来水管网泄漏检测定位方法，研究因泄漏而形成的声信号频率分布及不同泄漏量对频率分布的影响。实际泄漏检测时，通常采用互相关法估计泄漏信号到达不同传感器间的时间延迟实现漏点定位，因此，借助互相关分析法研究了管道的不同口径及泄漏信号传播距离对泄漏信号频率分布的影响。同时，泄漏声信号的传播不可避免要经过管道间的接口，因此分析了两种管道接口对信号频率成分的影响。进而为设计合理的管道泄漏检测过程提供依据，并为泄漏声信号形成及多种因素对泄漏声信号特征产生影响的机制研究奠定基础。     

炉内管道泄漏声检测与定位系统的研究现状

电站锅炉管道泄漏的声检测与定位系统,已经越来越广泛地应用于电站锅炉。该系统主要利用声学技术检测炉内管道的泄漏状态,并利用多传声器定位机理对泄漏点进行定位,有效地减少了由管道泄漏引起的非计划停炉,这对电站锅炉来说意义尤为重要。本文分析了炉内管道泄漏声检测与定位系统的关键技术,介绍了该系统的发展概况及应用现状,并提出了改进方向。     

三维声发射源定位实验简易实现方法

本文针对声发射技术中的三维定位问题提出一种新的简易实验方案.通过传感器接收的时差信息和空间坐标实现三维立体结构中的声发射源定位,操作和计算简单,降低了实验成本.该实验方法不仅可以直观地实现三维声发射源定位过程,还可以实现声速的测量.本设计加深了学生对物理问题的理解,提高了学生对声发射技术和声源定位技术的兴趣,适合普及.     

混凝土中的声发射波速特性及其在源定位中的应用

利用声发射技术监测受载混凝土裂缝发展特征的首要任务是对声发射源的准确定位.为了解混凝土中声发射波速特性,提高声发射源定位精度,该文开展了一系列室内试验,构建了考虑衰减现象的声发射波速修正模型,优化建立了基于修正波速的区域穷举定位法.结果表明:水灰比越小,基准波速越大,波速随距离的衰减越大.骨料粒径越大,基准波速越大,波...     

近场子空间聚焦的碰摩故障声发射定位方法

针对宽带多源声发射信号的相干、多模态和能量衰减快问题,提出一种近场多重相干信号子空间聚焦的定位算法用于碰摩故障声发射源的定位检测。首先,为滤除干扰模态波、减小频散效应,采用基于模态声发射传播特性分析的小波分解滤波方法,从碰摩初期的声发射信号中获取零阶模态波及波速用于定位计算;其次,为实现信号解相干,提出基于双边相关变换(TCT)的近场聚焦矩阵估计方法;最后,针对声发射信号的能量衰减快问题,利用近场基于特征分解的多重信号分类(N-MUSIC)的空间谱估计方法来实现声源的精确定位。理论分析和实验结果表明:该方法定位精度高、计算复杂度低、稳定性强,能有效识别多个相干碰摩声源。相比传统相干子空间算法(CSM),该方法减少了信号初值和聚焦频点的计算量,对双声源的分辨概率较现有修正近场多重信号分类算法提高了17%,是一种有效的碰摩故障源检测方法。     

流化床风帽故障的声发射检测

针对气固流化床中风帽故障影响流化质量问题,本文提出一种能够快速精确地检测出风帽故障位置和类型的声发射检测方法;声发射技术是一种实时、在线、非侵入流场的声波检测方法,利用均匀安装在流化床分布板下方的声发射传感器进行定位测量;采集气固流化床内颗粒撞击分布板产生的声信号,对该信号进行多尺度小波包分解,找出特征频段,由各个测点声发射信号总能量对比可以直观反应风帽故障存在位置,而各尺度能量分率的变化能进行故障类型判断;这种实时测量方法能更早,更精确的对风帽破损情况进行准确的判断。     

基于时延估计的光栅阵列管道泄漏检测与定位方法研究

城市燃气在管道运输过程中存在很大的安全隐患,一旦发生危险,后果不堪设想,燃气管道泄漏的监测与定位意义重大。为解决目前大部分管道泄漏检测与定位方法存在的易受环境干扰、精度低、适用范围窄、计算难度较高等问题,提出了一种基于时延估计的光栅阵列（wFBG）管道泄漏检测与定位方法,该方法通过光栅阵列技术采集振动信号,根据采集到的泄漏振动信号时域、频域上的特征,首先通过基于短时能量分析的方法检测管道是否泄漏,然后对满足要求的信号片段进行峰值间多项式拟合获取泄漏信息到达的时刻,最后根据时间差定位泄漏点。实验结果表明,该方法能有效检测泄漏,并且在测量距离为40 m的情况下,定位误差在1 m左右。     

基于光纤光栅的冲击激励声发射响应机理与定位方法研究

在对冲击激励声发射应力波在铝合金板上的传播机理进行分析的基础上,利用ABAQUS软件构建了钢球冲击铝合金板几何模型,仿真分析了冲击应力波传播过程.理论分析了冲击应力波与FBG传感器的作用机理,基于边缘滤波原理构建了声发射传感系统,采集冲击激励声发射应力波,建立了声发射区域定位模型,提出了基于扩散映射与支持向量机(SVM)的声发射区域定位方法并进行了实验验证.在300 mm×300 mm×2 mm的铝合金板上对36个测试区域进行了多次声发射区域定位实验,实验结果表明,扩散映射结合SVM的定位结果较优,区域定位精度为30 mm×30 mm,定位正确率为97.5%,耗时0.781 s.研究结果为声发射区域定位检测提供了一种有效方法.     

二维板声发射源定位实验的一种简易实现方法

对声发射技术中的二维平面定位问题提出一种新的简易实验方案.采用新三角时差算法实现二维板的声源定位和GPS模拟,同时利用简易实验方法,仅用两个超声波传感器完成实验,简化实验器材,降低实验成本,适合普及,计算过程简单,同时也利于物理问题深入地理解.     

Acoustic emission source location in plates using wavelet analysis and cross time frequency spectrum

In this study, the theories of wavelet transform and cross-time frequency spectrum (CTFS) are used to locate AE source with frequency-varying wave velocity in plate-type structures. A rectangular array of four sensors is installed on the plate. When an impact is generated by an artificial AE source such as Hsu–Nielsen method of pencil lead breaking (PLB) at any position of the plate, the AE signals will be detected by four sensors at different times. By wavelet packet decomposition, a packet of signals with frequency range of 0.125–0.25 MHz is selected. The CTFS is calculated by the short-time Fourier transform of the cross-correlation between considered packets captured by AE sensors. The time delay is calculated when the CTFS reaches the maximum value and the corresponding frequency is extracted per this maximum value. The resulting frequency is used to calculate the group velocity of wave velocity in combination with dispersive curve. The resulted locating error shows the high precision of proposed algorithm.     

A non-iterative partial discharge source location method for transformers employing acoustic emission techniques

Partial discharge (PD) causes premature insulation failure of transformers. It is essential to detect PD to avoid unwanted failure of transformers. Only detection of PD is not sufficient for a transformer of huge size, unless it is possible to locate. Acoustic partial discharge measurement is advantageous for PD source location. There are different algorithms for PD source location. These are iterative and require large number of acoustic emission (AE) sensors. This paper presents a non-iterative source location algorithm employing four AE sensors. This algorithm is applied to experimental data. Proposed algorithm is also applied to published data and compared with existing iterative methods. Main error for source location is due to arrival time calculation. In order to reduce the error in AE signal arrival time calculation, different arrival time calculation methods are discussed and a comprehensive method is proposed. By applying these methods, arrival time is calculated from measured signal.     

储罐底板声源辨识及定位方法研究

储罐底板腐蚀是多声源问题,即在不同位置的腐蚀源可能同时发射应力波。这些声源信号有时会重叠被传感器接收,从而影响定位的可靠性。为此本文基于平面声发射源能量定位方法的基本理论,进行了模拟储罐底板定位实验,提出了能量定位系数的修正方法。同时通过对实验数据分析,发现快速独立分量分析(FastICA)方法可以将同种声源混合信号进行有效分离,并且基本保持原有波形特征,相干系数法可以实现对分离后的同源信号进行聚类,进而应用改进能量定位方法对声发射源进行定位,从而对声源辨识,判断事件集中度提供依据。     

HRB400焊接试样拉伸全过程声发射信号 时频能量特征*

为了深入研究螺纹钢拉伸全过程声发射信号特征,开展了完整和焊接试样的拉伸实验,通过SAEU2S型数字声发射系统对损伤特性进行实时监测。结合金属材料力学行为特性,根据计数率将全过程划分成不同的阶段,采用伪Margenau-Hill(PMH)分布进行了时频分析并利用小波包变换对信号进行了能量分析。结果表明：声发射特性参数能够很好的描述焊接对材料力学性能的影响,PMH分布具有良好的时频聚集性,频域和能量分析能够很好地描述焊接对材料力学性能的影响。实验结果为声发射技术应用于钢结构工程损伤定量、寿命预测、实时监测的研究提供了参考依据。     

早龄期荷载及低温作用下的混凝土声发射特征试验研究*

井壁混凝土由于施工与环境特殊性导致其裂缝扩展情况异常,本文通过对不同早龄期荷载、低温条件下混凝土轴压破坏过程的声发射检测,分析井壁施工环境对混凝土内部裂缝产生、发展的影响。研究表明,早龄期荷载、低温单独作用时,与标养混凝土主要差异在轴压过程中的声发射初始阶段,对混凝土内部的原有收缩裂缝有所影响。当两者耦合作用时,早龄期荷载影响比低温作用显著,并且在早龄期荷载较大时会出现声发射信号"倒置"现象。     

木塑复合材料缺陷及损伤的声发射信号特征分析及神经网络模式识别

针对木塑复合材料五种典型的缺陷及损伤机制，选择合适的木塑试样，应用三点弯曲的加载方法采集声发射信号。对主损伤区附近的声发射事件，应用小波变换提取特征参数，确定五类主要损伤机制所对应的声发射信号特征。采用B—P型反向传播神经网络构成的智能化模式分类器，对此五类声发射信号进行识别，获得了满意的效果。     

Proposed method of partial discharge allocation with acoustic emission sensors within power transformers

Employing acoustic emission sensors for detection of partial discharge, PD, introduces many advantages. Besides easy installation and replacement, they are non-invasive and immune to electromagnetic noise and interference and their sensitivity does not vary with object capacitance. For PD allocating utilizing AE sensors, distance calculations are based on the arrival time of acoustic waves to the sensors. Considering structure-borne waves of higher speed, the peaks of some of indirect path AE signals with significant contribution are mistakenly considered as peaks of direct path AE signals. Furthermore, the acoustic signals are propagating through certain parts of the transformer, such as the windings, and this complicates the partial discharge detection and allocation. These would imply an incorrect distance between the source and sensor. A method based on a heuristic algorithm has been proposed which considering all possible indirect paths with the relevant propagation times and all the barriers on the travel path of acoustic signal, calculates the more precise arrival times to sensors. A test chamber has been utilized and artificial PD signals are produced at various points. Output results of algorithm have been compared with results of classic method. It has been shown that proposed method significantly reduces the positioning errors.     

一种声发射信号方位角自适应监测算法*

四阶累积量可用于声发射信号处理,结合盲波束形成算法可对声发射信号的方位角进行估计。由于四阶累积量的计算量较大,采用迭代计算的思想,提出一种基于四阶累积量的自适应声发射信号方位角监测算法。与改进前的算法相比,自适应算法的计算复杂度和所需存贮空间均显著降低,可用于声发射信号的方位估计和监测。计算机仿真及实测数据分析均验证了算法的有效性。     

Extraction of weak crack signals based on sparse code shrinkage combined with wavelet packet filtering

Early crack signals in critical infrastructure components of major equipment are hardly to be extracted due to its low signal noise ratio (SNR). A de-noising method combined wavelet packet (WP) technology with sparse code shrinkage (SCS) is proposed in this study. Firstly, WP reconstruction technology is used to reserve the crack signal with a specified frequency range. That is, the signal is decomposed by Meyer wavelet into five layers, and the signal with the frequency range from 187.5 kHz to 609.375 kHz is reserved. Then SCS method removes noise within the specified frequency range. Namely, the probability density function (PDF) of the signal independent coefficients is estimated via the generalized Gaussian model (GGM) in the independent component analysis (ICA) space. The nonlinear de-noising is finished by utilizing maximum a posteriori (MAP) estimate. The results obtained by the combined method are compared with those generated by the SCS method and the WP de-noising method. It demonstrates that the combined method is the best one among the three methods in extracting weak signals. Its output SNR is −2.38 dB and the correlation coefficient (CC) is 0.54 when the input SNR is −20 dB. They are higher than those obtained by the SCS method (SNR −4.46 dB and CC 0.51). The WP method is the worst (SNR −3.54 dB and CC −0.003). Therefore, the combined method is quite suitable for weak signal extraction.