直埋热水供热管道泄漏定位检测实验研究

选用傅里叶滤波法和小波阈值法进行了管道泄漏信号降噪和泄漏定位的对比研究。在DN300管路的实验直埋热水供热系统上进行了泄漏检测实验,采用小管径支管和调节阀模拟泄漏点、加速度传感器测量声波振动信号,依次选用2种滤波方法对直埋热水供热管道泄漏的声波振动信号进行数据降噪处理,讨论了泄漏信号降噪前后的信号频域特征和定位精度。在北京市某小区直埋供热管道进行了声波降噪泄漏检测工程验证,定位偏差不超过1.0 m。研究表明,傅里叶滤波法和小波阈值法对直埋热水供热管道泄漏定位的声波振动信号降噪处理效果显著,小波阈值法保持了全频段的信号特征,可获得更高的定位精度。     

基于WE-ICA的牵引电机速度传感器微小故障检测与识别

文章提出了一种基于小波变换和集成独立成分分析(WE-ICA)的牵引电机速度传感器微小故障检测与识别方法。首先,通过小波变换对高速列车牵引电机数据进行滤波处理,减少噪声对微小故障信息的干扰;接着通过独立成分分析(ICA)方法提取故障信息并建立检测统计量;最后利用贝叶斯推理计算统计量的故障概率,并设计集成统计量用于微小故障检测;另外通过加权贡献度对故障进行识别。利用CRH2牵引电机实验平台对该方法进行验证,实验结果证明了该方法的有效性和实用性。     

地质雷达信号分析的双正交小波预测反褶积法

为了降低电磁波衰减、噪声干扰等因素对地质雷达检测效果的影响,提出一种双正交小波预测反褶积法(PDBW法)。在PDBW法中,针对地质雷达检测信号选取具有最小重构误差的双正交小波基,运用该小波基将地质雷达检测信号分解成不同频段的时域子信号,对各频段的时域子信号进行预测反褶积等滤波处理,再对处理后的子信号进行重构变换,得到PDBW法的处理结果。将PDBW法用于实验检测信号处理,并将处理结果与预测反褶积法的处理结果进行比较,结果表明:PDBW法能有效压制多次回波干扰,准确识别深部信号,显著提高深部信号信噪比,从而进一步改善地质雷达探测分辨率和图像分析的准确性。     

基于经验模态分解去噪改进主成分分析的冷水机组传感器故障检测

提出了一种基于经验模态分解(EMD)阈值去噪(TD)和主成分分析(PCA)相结合的冷水机组传感器故障检测方法(EMD-TD-PCA)。采用EMD阈值去噪法去除原始数据中的噪声来提高数据质量,针对去噪后的数据构建PCA模型。采集了武汉市某电子厂螺杆式冷水机组的实际运行数据,用于验证故障检测效果,并与传统PCA方法和小波阈值去噪(Wavelet-TD-PCA)方法的传感器故障检测结果进行了对比。结果表明:EMD-TD-PCA可以有效提高冷水机组传感器的故障检测效率,同等偏差条件下,故障检测效果优于传统PCA方法和Wavelet-TD-PCA方法。对于小偏差(-1～1℃)故障,故障检测效果提升尤为明显。     

基于联邦滤波的多传感器主动容错估计方法

针对冗余传感器系统的容错问题,改进一种有反馈式联邦滤波的容错估计算法。通过残差法对硬故障进行检测,并隔离故障子系统的估计值。若全部子系统出现硬故障,取上一时刻的联邦滤波的最优值,通过系统状态方程计算得到系统状态量的预测值进行容错补偿。定义可信度因子,设计可信度单调性法则检测子系统软故障,并设计校正策略。大量仿真实验表明,改进后的反馈式联邦滤波的容错估计算法,可在保证系统估计精度的前提下,提高多传感器系统的容错性与鲁棒性。     

基于小波分析与突变量法的故障录波突变时刻检测方法

提出了一种基于小波分析与突变量法相结合的故障线路录波突变时刻的实用检测方法。通过使用db4小波对故障录波数据进行变换,检测出可疑的奇异点集,然后通过计算可疑奇异点后的前后基波有效值,利用突变量法确定突变点。经过验证可知,这两种方法相结合能够较精确、快速地定位到故障特征时刻,为进一步利用故障录波时序信息进行故障诊断提供了技术支持,具有很强的实用性。     

电缆故障测距及检测信号去噪分析

为提高电力电缆故障定位的准确度,利用小波变换与行波测距相结合的定位方法,对故障波形的db3小波进行四层分解和信息分析。并通过搜索高频系数的模极大值来确定行波起始脉冲和反射脉冲的时间点,精确定位电缆故障的位置点。在实际铺设电缆的情况下,行波中会夹杂大量的噪声信号,选取软阈值法进行去噪,效果显著,且可保留故障信号中的有效信息。通过MATLAB/Simulink对电缆故障进行仿真,结果表明,运用小波分析能够很好地找到故障信号中的奇异点,取得较高的故障定位精度。     

基于端点检测的时频峰值滤波动液面提取技术

油套环空中会产生各种噪声,使测得的液面反射信号非常复杂,真实的液面反射波位置因受到干扰而无法准确辨识。采用时频峰值滤波(TFPF)技术结合语音信号处理中的端点检测(VAD)方法可对动液面波进行有效提取。VAD-TFPF技术先采用短时能量和过零率的双门限VAD方法对声波法测油井信号进行划分,判断出有效信号数据段和接近于零值的数据段,然后采用不同窗长的TFPF分别对两种数据段进行滤波处理。通过对不同噪声强度下的实测数据进行滤波实验与分析,可知该项新技术较之于小波阈值滤波方法对动液面波的辨识能力更强,无论是对背景噪声的压制还是对有效波的提取都表现出更优越的性能。     

小波滤波方法应用效果浅析

为降低利用NaI(Tl)闪烁探测器得到的γ能谱数据因统计涨落而产生误差,又保留谱峰的全部重要特征,以便谱的分析,必须对实测伽马能谱数据进行光滑。文章研究了变换能谱光滑的各种方法:算术滑动平均法、重心法、多项式最小二乘拟合法、离散函数褶积滑动变换和小波滤波法等。采用理论计算,结合实测谱线,通过峰面积标准偏差,与H/FWHM的比较,对各种方法进行评价。结果表明采用小波滤波方法实现伽玛能谱的滤波处理,优于其它滤波效果。     

基于CMOR小波扩展特性的基础工程无损诊断

为了适应基础工程高精度无损诊断的要求 ,提出了中心频率参数Fc 超出小波允许条件取较小值 ,使其时域分析精度近似达到高斯实小波水平的CMOR小波扩展特性 ,从而完善了CMOR小波的反射波分析能力。在小波允许条件下 ,CMOR小波对反射波进行动力特性分析而实施介质损伤识别 ,在扩展特性下 ,CMOR小波对反射波进行突变检测而实施损伤定位 ,两者相结合构建了一条完备的基于反射波CMOR小波分析的基础工程无损诊断技术路线 ,并开发了具有强计算能力和可视化特性的相应软件系统。在地下连续墙无损诊断的实例分析中 ,CMOR小波扩展特性提高时域精度的有效性得到具体评价 ,所提供技术路线的可行性得以验证。     

小波多分辨方法在基桩检测中的应用

介绍了小波多分辨分析理论,利用小波多分辨分析方法,对桩基检测信号进行5层分解,利用分解的频带信息,指导信号滤波,消去高频干扰,利用细节信号提高了数据分析处理的质量以及桩基检测的分辨率和准确率。     

A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis

This paper presents a self-adaptive sensor fault detection and diagnosis (FDD) strategy for local system of air handing unit (AHU). This hybrid strategy consists of two stages. In the first stage, a fault detection model for the AHU control loop including two back-propagation neural network (BPNN) models is developed. BPNN models are trained by the normal operating data of system. Based on sensitive analysis for the first BPNN model, the second BPNN model is constructed in the same control loop. In the second stage, a fault diagnosis model is developed which combines wavelet analysis method with Elman neural network. The wavelet analysis is employed to process the measurement data by extracting the approximation coefficients of sensor measurement data. The Elman neural network is used to identify sensor faults. A new approach for increasing adaptability of sensor fault diagnosis is presented. This approach gains clustering information of the approximations coefficients by fuzzy c-means (FCM) algorithm. Based on cluster information of the approximation coefficients, the unknown sensor fault can be identified in the control loop. Simulation results in this paper show that this strategy can successfully detect and diagnose fixed biases and drifting fault of sensors for the local system of AHU.     

Combination of neural network and wavelet transform for islanding detection of distributed generation in a small-scale network

In this paper, an optimum and intelligent method is proposed for islanding detection using wavelet transform. The suggested relay is based on neural network (NN) in which different heuristic algorithms are used for training the NN. In the proposed method, the appropriate signals for detection procedure as well as mother wavelet are selected optimally, based on the mean square error (MSE) concept. Lately, the desired relay is trained by the optimally selected signals using four different algorithms and the optimum condition of the fault detector is identified. Simulation results approved that non detection zone (NDZ) has a significant reduction utilising the proposed intelligent technique. The contributions of the proposed method include presenting an appropriate signal selection method based on MSE, selecting optimum number of relay input signals using the proposed technique, fast training of intelligent relay by using least information, solving threshold selection problem and reduction of NDZ approximately to zero.     

多结构元素并行复合形态滤波方法在MOA在线监测信号处理中的应用研究

基于数学形态学基本原理,构造了一种多结构元素并行复合形态滤波器并应用于金属氧化物避雷器(MOA)在线监测信号处理中。该滤波方法可以在滤除随机、脉冲等多种干扰的同时,将强干扰背景下的MOA泄漏电流提取出来,较之小波阈值和传统形态滤波方法更为有效。MATLAB仿真和实测信号的分析表明,该方法通过选取合理的结构元素,可以准确提取泄漏电流信号,大幅度提高信噪比,有利于MOA故障诊断。     

基于软阈值小波降噪的天津CORS站点数据分析

以地心地固坐标与椭球面坐标转化关系的微分方程为基础,结合基线解算得到的坐标分量方差-协方差矩阵,推导了用于CORS站点坐标分量时间序列小波滤波的软阈值公式,并用于天津CORS站点的坐标时间序列滤波处理,滤波结果表明该软阈值的设定方式可较好的去坐标时间序列中的高频噪声。     

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

A method to generate a suite of artificial near‐fault ground motion time histories for specified earthquakes is presented. A wavelet‐based nonstationary (WB‐NS) model has been employed to effectively capture the time‐varying frequency content of a particular acceleration record and continuous wavelet transform has been used to simulate the largest velocity pulse. Furthermore, an iterative procedure using discrete wavelet transform is utilized to modify an earthquake ground motion and generate energy‐compatible ground motion. Eventually, the artificial near‐fault accelerogram is achieved via the superposition of a coherent extracted velocity pulse with a random acceleration record corresponding to a WB‐NS model and multiplied by a time‐modulating envelope function. The effectiveness of the method is demonstrated by comparing the spectral response and Arias intensity curves of the simulated accelerograms with those of the real records.     

基于BAS的空调系统过程监测与故障诊断

提出了基于BAS的空调系统过程监测与故障诊断模式，阐述用主成分分析法进行故障诊断的过程，并用其进行空调系统的传感器故障诊断。结果表明，主成分分析法具有很好的故障检测、故障识别和故障重构能力，还表明，故障诊断与BAS相结合是可行的。     

Fire Detection in Video Using LMS Based Active Learning

In this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [−1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage.     

Wavelet‐Based Detection of Beam Cracks Using Modal Shape and Frequency Measurements

Abstract: When a structure is subjected to dynamic or static loads, cracks may develop and the modal shapes and frequencies of the cracked structure may change accordingly. Based on this, a new method is proposed to locate beam cracks and to estimate their depths. The fault‐induced modal shape and frequency changes of cracked structures are taken into account to construct a new hybrid crack detection method. The method includes two steps: crack localization and depth estimation. The locations of the cracks are determined by applying the wavelet transform to the modal shape. Using the measured natural frequencies as inputs, the depths of the cracks are estimated from a database established by wavelet finite element method. The effectiveness of the proposed hybrid two‐step method is demonstrated by numerical simulation and experimental investigation of a cantilever beam with two cracks. Our analyses also indicate that the proposed method performed reasonably well at certain level of noise.     

低压供配电线路故障电弧检测方法

通过搭建试验平台模拟建筑物低压供配电线路中的故障电弧,分析故障电弧特征,提取出三种表征故障电弧的特征量,包括各周期采样点平均值、波形斜率和小波高频系数。应用MATLAB模糊工具箱,采用模糊逻辑推理的方法将三种判据融合,建立故障概率判定系统。