基于稀疏采样数据的管道缺陷超声复合阵列检测方法研究

发布时间：2018-01-14 02:30

本文关键词：基于稀疏采样数据的管道缺陷超声复合阵列检测方法研究　出处：《江苏大学》2017年硕士论文　论文类型：学位论文

【摘要】：管道是能源输送的重要手段之一,在国民经济中占有重要的地位。然而,管道在长时间的服役过程中,受环境应力的影响会产生不同种类的缺陷,形成泄漏隐患,从而导致事故的发生。因此,定期对管道健康状态进行检测是十分必要的。超声阵列检测是管道缺陷常用的检测方法之一,但是现有设备大多采用单一声束入射方式,无法满足管道中对不同种类、不同走向缺陷的检测要求。另外,使用超声阵列对管道缺陷进行检测时,为了提高检测效率和对缺陷的检测和定位精度,超声传感器往往工作频率较高,按常规的数据采集方法会产生巨大的数据量,制约了该项技术的应用。针对上述问题,论文开展基于稀疏采样数据的管道缺陷超声复合阵列检测方法研究。设计了一种超声传感器复合阵列结构,声束以三种入射方式同时扫查管道,用以提高对不同种类缺陷的检出率,特别是裂纹类缺陷;设计了基于LabVIEW的复合阵列有限新息率(Finite Rate of Innovation,FRI)稀疏数据采集系统,并通过试验直接获取了FRI稀疏数据,减小了检测数据量;研究了从直接获取的稀疏数据中提取缺陷声学特征参数的方法,得到了超声回波信号的幅值和时延;建立了复合阵列结构的空间坐标模型和缺陷成像模型,得到了缺陷的可视化图像。论文的主要研究工作如下:(1)设计了集垂直入射、轴向斜入射和周向斜入射三种入射方式于一体的超声传感器复合阵列结构,分析了其在管道中的声场分布,建立了复合阵列空间坐标模型,并加工制作了复合阵列。(2)利用试验室自制的FRI采样硬件电路,设计了基于LabVIEW的复合阵列稀疏数据采集系统,搭建了管道缺陷超声复合阵列检测试验平台,并对六种不同类型缺陷进行了检测试验,直接获取了检测信号的稀疏数据。(3)研究了从稀疏采样数据中获取超声回波信号特征参数的方法,采用基于Cadzow去噪的零化滤波器算法对实测管道超声信号进行了特征参数提取,得到了回波信号时延与幅值参数,并对其误差进行了分析。(4)在分析传统超声成像方法特点的基础上,提出了一种基于稀疏采样数据的声学特征参数管道缺陷微元填充法,并建立了管道缺陷成像模型,实现了管道缺陷的可视化成像。并对比分析了常规采样数据和稀疏采样数据条件下不同缺陷的成像结果。试验表明,在以常规数据约5.5%的数据量下,仍然能够获得与常规采样数据同等分辨率的缺陷可视化图像。
[Abstract]:Pipeline is one of the important means of energy transmission, which plays an important role in the national economy. However, in the long service process, the pipeline will be affected by environmental stress to produce different kinds of defects. Therefore, it is necessary to detect the health status of pipelines periodically. Ultrasonic array detection is one of the commonly used detection methods for pipeline defects. However, most of the existing equipment adopt a single beam incident mode, which can not meet the detection requirements of different types and different strike defects in the pipeline. In addition, the ultrasonic array is used to detect the pipeline defects. In order to improve the efficiency of detection and the accuracy of defect detection and positioning, ultrasonic sensors often work high frequency, according to the conventional data acquisition method will produce a huge amount of data. The application of this technology is restricted. Aiming at the above problems, this paper studies the detection method of ultrasonic composite array of pipeline defects based on sparse sampling data, and designs a composite array structure of ultrasonic sensor. In order to improve the detection rate of different kinds of defects, especially crack type defects, the acoustic beam scans the pipeline simultaneously in three kinds of incident mode. The sparse data acquisition system of finite Rate of innovation based on LabVIEW is designed. The sparse data of FRI is obtained directly by experiment, and the amount of detection data is reduced. The method of extracting acoustic characteristic parameters of defects from directly acquired sparse data is studied, and the amplitude and delay of ultrasonic echo signal are obtained. The spatial coordinate model and defect imaging model of composite array structure are established, and the visual image of defects is obtained. The main research work of this paper is as follows: 1) the set of vertical incidence is designed. The ultrasonic sensor composite array structure with three incident modes of axial and circumferential incidence is presented. The sound field distribution in the pipeline is analyzed and the spatial coordinate model of the composite array is established. And the compound array. 2) the sparse data acquisition system of composite array based on LabVIEW is designed by using the self-made FRI sampling hardware circuit in the laboratory. The ultrasonic composite array testing platform for pipeline defects was built, and six different types of defects were tested. The method of obtaining the characteristic parameters of ultrasonic echo signal from sparse sampling data is studied. Based on the Cadzow denoising algorithm, the characteristic parameters of the measured ultrasonic signal are extracted, and the echo signal delay and amplitude parameters are obtained. On the basis of analyzing the characteristics of traditional ultrasonic imaging methods, a method of filling acoustic characteristic parameter pipeline defects based on sparse sampling data is proposed. A pipeline defect imaging model is established to realize the visual imaging of pipeline defects, and the imaging results of different defects under the condition of conventional sampling data and sparse sampling data are compared and analyzed. At the same time, the defect visualization image with the same resolution as the conventional sampling data can be obtained under the data volume of about 5.5%.
【学位授予单位】：江苏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TE973.6;TB559

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