基于多传感器融合的油管无损检测与缺陷量化技术研究
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摘要
油管缺陷的无损检测对保证采油作业的安全有着十分重要的意义,同时,油管也是昂贵物,通过对油管无损检测和量化分析可以为采油安全和油管的再使用提供依据。针对国内油管缺陷检测多数仍在定性研究,定量分析还处在探索阶段的实际情况,本论文系统研究基于多传感器融合的油管无损检测及其缺陷量化技术和检测系统,概括起来,本文主要研究成果及创造性研究如下:
对油管典型缺陷进行分析和归纳总结,在油管上人工制做系列化典型缺陷样本。通过实验对油管采用多传感器漏磁检测技术获取缺陷漏磁信号,用大量详实的实验数据详细地分析并建立了缺陷漏磁信号与缺陷大小的关系模型。
研究了缺陷漏磁检测的短时处理方法。油管漏磁信号是一种不平稳的随机过程,其特性是随时间变化的。基于此,就可以将漏磁信号分成一些短段进行处理。在分析和经过反复试验比较的基础上,提出并建立了一组基于多传感器融合、能够反映缺陷大小的特征量。
应用小波变换良好的时频特性对油管缺陷检测信号进行了多分辨分析,通过小波分解与重构有效地分离出偏磨产生的渐变信号和坑状缺陷产生的突变信号。通过对各个传感器检测信号采用高频部分重构实现了去噪目的,提高了缺陷信号的信噪比。对采集记录的漏磁信号进行增强处理的方法,提高缺陷信号与背景信号的信噪比,效果明显并给出了相应实例。
提出了基于多传感器融合的油管缺陷的定量分析理论与方法。对于不同的缺陷采用不同的特征和融合算法。根据偏磨和坑状缺陷信号的特点采用硬件实现偏磨和坑状缺陷的分离,采用插值方法定量分析偏磨大小,建立了基于特征量和最小距离分类的模式识别方法实现裂纹和孔的分类,以及基于特征量和神经网络数据融合算法实现裂纹和孔缺陷定量分析并给出分析结果。
开发油管微机在线检测与数据分析系统。依据以上建立的缺陷漏磁模型和漏磁信号的定量分析技术,借助计算机软硬件技术,开发的抽油管在线检测与数据分析系统。在软件上,采用多线程编程技术实现数据采集、存储与实时声光报警。在硬件上使用USB接口技术实现信号数据的高速采集与即插即用功能。提高探伤检测准确率和效率,同时完成缺陷的量化分析。
Nondestructive testing of oil-well tubing is of vital significance to the safety of oil extraction. At the same time, oil-well tubing is very expensive, while a guarantee could be provided to the safety of oil-extraction and the reusing of oil-well tubing by a method of nondestructive testing and quantificational analysis to the defects. Aiming at the fact that domestic researches of defects detection on oil-well tubing are mostly by far qualitative while the quantificational analysis is still on its early stage, this thesis develops a method, as well as an inspection system, of nondestructive testing and quantitative recognition of defects on oil-well tubing, which is based on a multi-sensor fusion. All in one, the specific works finished and main innovative contributions of this dissertation are as follows:
Typical defects of oil-well tubing are analyzed and classified, according to which a series of sample defects are designed and manufactured on oil-well tubing. By experiments magnetic flux leakage signals of defects are collected with an approach of multi-sensor detection, and the relation model of magnetic flux leakage signals and the size of defects is developed and analyzed according to a large amount of experimental data.
Method of short-time processing of magnetic flux leakage detection is applied. Magnetic flux leakage signals of defects of oil-well tubing are uneven and random processes. Thus, the signals could be divided into a certain amount of short parts to be processed. With the forward analysis and comparison, a group of characteristics reflecting the size of defects and based on multi-sensor fusion are proposed.
With the good time & frequency characteristic of wavelet transform, signals of defects are decomposed and reconstructed the signals .the signals with gradual change caused by partial abrasion are separated from signals with abrupt change caused by defects such as hole, etc. Noise is removed from signals got by each sensor by means of reconstructing the high frequency parts of the signals. As a result of which, the SNR(Signal-to-Noise Ratio) of signals of defects is increased. With a method of enhancing the magnetic flux leakage signals, the SNR of signals of defects and background is increased effectively, and relative examples are given in
the dissertation.
The method of quantitative recognition of oil-well tubing defects based on multi-sensor fusion is proposed in this dissertation. Different characteristics and algorithms are adopted in accordance with different types of defects. According to the deference between characteristics of signals of partial abrasion and hole-shaped defects, the two kinds of signals are separated with each other by hardware. The size of partial abrasion is worked out quantificationally with a method of interpolation. The crack-shaped and hole-shaped defects are identified respectively by a modal identification method based on classification of characteristic and minimum distance. This two kinds of defects are analyzed quantificationally based on neural network algorithms, and the analysis results are given.
A system for on-line oil-well tubing inspection and data analysis with computer is developed. According to the magnetic flux leakage model the technique of quantificational analysis of magnetic flux leakage signals developed forwardly, with the aid of computer technology, this inspection and analysis system is able to, by means of multi-thread programming technology, control data collection and storage and alerting a sound-light alarm. And by the application of USB interface technology, the system could collect data with a high speed and perform the function of plug and play very well. With this system, the veracity and efficiency of defects testing are increased and the defects are analyzed quantificationally.
对油管典型缺陷进行分析和归纳总结,在油管上人工制做系列化典型缺陷样本。通过实验对油管采用多传感器漏磁检测技术获取缺陷漏磁信号,用大量详实的实验数据详细地分析并建立了缺陷漏磁信号与缺陷大小的关系模型。
研究了缺陷漏磁检测的短时处理方法。油管漏磁信号是一种不平稳的随机过程,其特性是随时间变化的。基于此,就可以将漏磁信号分成一些短段进行处理。在分析和经过反复试验比较的基础上,提出并建立了一组基于多传感器融合、能够反映缺陷大小的特征量。
应用小波变换良好的时频特性对油管缺陷检测信号进行了多分辨分析,通过小波分解与重构有效地分离出偏磨产生的渐变信号和坑状缺陷产生的突变信号。通过对各个传感器检测信号采用高频部分重构实现了去噪目的,提高了缺陷信号的信噪比。对采集记录的漏磁信号进行增强处理的方法,提高缺陷信号与背景信号的信噪比,效果明显并给出了相应实例。
提出了基于多传感器融合的油管缺陷的定量分析理论与方法。对于不同的缺陷采用不同的特征和融合算法。根据偏磨和坑状缺陷信号的特点采用硬件实现偏磨和坑状缺陷的分离,采用插值方法定量分析偏磨大小,建立了基于特征量和最小距离分类的模式识别方法实现裂纹和孔的分类,以及基于特征量和神经网络数据融合算法实现裂纹和孔缺陷定量分析并给出分析结果。
开发油管微机在线检测与数据分析系统。依据以上建立的缺陷漏磁模型和漏磁信号的定量分析技术,借助计算机软硬件技术,开发的抽油管在线检测与数据分析系统。在软件上,采用多线程编程技术实现数据采集、存储与实时声光报警。在硬件上使用USB接口技术实现信号数据的高速采集与即插即用功能。提高探伤检测准确率和效率,同时完成缺陷的量化分析。
Nondestructive testing of oil-well tubing is of vital significance to the safety of oil extraction. At the same time, oil-well tubing is very expensive, while a guarantee could be provided to the safety of oil-extraction and the reusing of oil-well tubing by a method of nondestructive testing and quantificational analysis to the defects. Aiming at the fact that domestic researches of defects detection on oil-well tubing are mostly by far qualitative while the quantificational analysis is still on its early stage, this thesis develops a method, as well as an inspection system, of nondestructive testing and quantitative recognition of defects on oil-well tubing, which is based on a multi-sensor fusion. All in one, the specific works finished and main innovative contributions of this dissertation are as follows:
Typical defects of oil-well tubing are analyzed and classified, according to which a series of sample defects are designed and manufactured on oil-well tubing. By experiments magnetic flux leakage signals of defects are collected with an approach of multi-sensor detection, and the relation model of magnetic flux leakage signals and the size of defects is developed and analyzed according to a large amount of experimental data.
Method of short-time processing of magnetic flux leakage detection is applied. Magnetic flux leakage signals of defects of oil-well tubing are uneven and random processes. Thus, the signals could be divided into a certain amount of short parts to be processed. With the forward analysis and comparison, a group of characteristics reflecting the size of defects and based on multi-sensor fusion are proposed.
With the good time & frequency characteristic of wavelet transform, signals of defects are decomposed and reconstructed the signals .the signals with gradual change caused by partial abrasion are separated from signals with abrupt change caused by defects such as hole, etc. Noise is removed from signals got by each sensor by means of reconstructing the high frequency parts of the signals. As a result of which, the SNR(Signal-to-Noise Ratio) of signals of defects is increased. With a method of enhancing the magnetic flux leakage signals, the SNR of signals of defects and background is increased effectively, and relative examples are given in
the dissertation.
The method of quantitative recognition of oil-well tubing defects based on multi-sensor fusion is proposed in this dissertation. Different characteristics and algorithms are adopted in accordance with different types of defects. According to the deference between characteristics of signals of partial abrasion and hole-shaped defects, the two kinds of signals are separated with each other by hardware. The size of partial abrasion is worked out quantificationally with a method of interpolation. The crack-shaped and hole-shaped defects are identified respectively by a modal identification method based on classification of characteristic and minimum distance. This two kinds of defects are analyzed quantificationally based on neural network algorithms, and the analysis results are given.
A system for on-line oil-well tubing inspection and data analysis with computer is developed. According to the magnetic flux leakage model the technique of quantificational analysis of magnetic flux leakage signals developed forwardly, with the aid of computer technology, this inspection and analysis system is able to, by means of multi-thread programming technology, control data collection and storage and alerting a sound-light alarm. And by the application of USB interface technology, the system could collect data with a high speed and perform the function of plug and play very well. With this system, the veracity and efficiency of defects testing are increased and the defects are analyzed quantificationally.
引文
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