储罐罐底腐蚀声发射检测信号处理关键技术研究
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摘要
为保障我国石油供应安全,一批国家石油战略储备基地正在规划和建设中,进而我国大型立式金属储罐数量将随之激增。作为一种特种设备,储油罐一旦发生泄漏等事故,后果非常严重,因此加强储油罐的安全监控和维护管理是储备基地建成之后的重要工作。研究先进的储罐罐底在线检测技术,对于保障储油罐的安全稳定运行,减少和避免环境污染,减少因盲目检修所带来的经济损失具有重要意义。
本文对储罐罐底声发射在线检测技术中的信号处理环节进行了研究,主要包括以下几方面工作:
1、研究了罐底声发射信号的融合过程,提出了基于聚类分析的声发射信号融合方法,从而提高了对声发射事件提取的准确度,有效降低了噪声信号的干扰。
2、根据罐底声发射源的定位要求,分别采用三角定位算法和超定定位算法对罐底声发射源进行定位,并根据两种算法的特点分别选用了不同的数值解算方法,采用蒙特卡洛方法对比研究了两种定位算法在理想情况下和非理想情况下的定位性能,对罐底声发射源定位分析具有重要指导意义。
3、为实现对罐底腐蚀区域的自动化识别和评估,研究小波聚类算法,并将该算法应用到声发射源罐底分布区域的识别,提出采用声发射源分布信息熵对区域识别效果进行评价,通过对现场检测数据的分析,对小波聚类算法的参数设置范围进行了优化,进而提高识别效率。
4、对国内11座城市的输油站的25个储罐进行了现场检测实验,分析了储罐类型对检测结果的影响,总结了现场检测过程中避免噪声和干扰的方法,对进一步开展现场检测具有指导意义。
5、自主开发了声发射罐底检测数据分析评估软件,具有模块化,可扩展等特点,能够完成声发射信号波形分析、信号特征提取与分析、声发射源定位、罐底腐蚀评估等工作。
To protect the security of oil supply, a number of national strategic oil reserve base is constructing, and thus the number of large-scale vertical metal tanks is growthing rapidly. As special equipment, the safety of storage tanks is very important. Once the leakage accident of a tank happens, the consequences would be very serious. Therefore, enhancing monitoring the tanks’healthy is an important work after the national strategic oil reserve base being constructed. It’s is meaningful to study on advanced tanks online testing technology, for maintaining tanks’stable operation, reducing or avoiding environmental pollution, and reducing the economic loss due to blindness repairing.
In this paper, the acoustic emission (AE) testing for storage tank is studied, which is mainly focusing on the AE signals processing. The study includes the following aspects:
1. The fusion of the AE signals is studied, and a new AE signals fusion method, which is based on cluster analysis, is proposed. The new fusion method could raise the accuracy of extracting AE events and reduce signal noise effectively.
2. According to the requirements of tank bottom AE source positioning, the triangulate positioning algorithm and the overdetermined positioning algorithm are studied. Different solution methods are developed for the two different algorithms. In order to study the two algorithms’positioning performance under ideal and non-ideal conditions, Monte Carlo method is used. The result would be very meaningful for analyzing the AE sources’location of tank bottom.
3. In order to identificate and assess the corrosion region of tank bottom from AE testing data, wavelet cluster algorithm is developed. And the AE source distribution information entropy is proposed to assess the identification effect. Through analysis of the real test results, the range of parameter settings in the wavelet cluster algorithm is optimized to raise the identification efficiency.
4. 25 oil tanks located in 11 cities are tested by AE testing method. The impacts of tank type on test results are analyzed. The methods of avoiding noise and interference signals during the testing process are summarized, which would be meaningful for grading the further AE testing of tank bottom.
5. Based on the study of AE signal processing algorithms, a tank bottom AE data analysis software is developed. The software is modular, scalable and multifunctional, such as AE signal waveform analysis, AE signal characteristic extraction and analysis, AE source location and tank bottom corrosion assessment, etc.
本文对储罐罐底声发射在线检测技术中的信号处理环节进行了研究,主要包括以下几方面工作:
1、研究了罐底声发射信号的融合过程,提出了基于聚类分析的声发射信号融合方法,从而提高了对声发射事件提取的准确度,有效降低了噪声信号的干扰。
2、根据罐底声发射源的定位要求,分别采用三角定位算法和超定定位算法对罐底声发射源进行定位,并根据两种算法的特点分别选用了不同的数值解算方法,采用蒙特卡洛方法对比研究了两种定位算法在理想情况下和非理想情况下的定位性能,对罐底声发射源定位分析具有重要指导意义。
3、为实现对罐底腐蚀区域的自动化识别和评估,研究小波聚类算法,并将该算法应用到声发射源罐底分布区域的识别,提出采用声发射源分布信息熵对区域识别效果进行评价,通过对现场检测数据的分析,对小波聚类算法的参数设置范围进行了优化,进而提高识别效率。
4、对国内11座城市的输油站的25个储罐进行了现场检测实验,分析了储罐类型对检测结果的影响,总结了现场检测过程中避免噪声和干扰的方法,对进一步开展现场检测具有指导意义。
5、自主开发了声发射罐底检测数据分析评估软件,具有模块化,可扩展等特点,能够完成声发射信号波形分析、信号特征提取与分析、声发射源定位、罐底腐蚀评估等工作。
To protect the security of oil supply, a number of national strategic oil reserve base is constructing, and thus the number of large-scale vertical metal tanks is growthing rapidly. As special equipment, the safety of storage tanks is very important. Once the leakage accident of a tank happens, the consequences would be very serious. Therefore, enhancing monitoring the tanks’healthy is an important work after the national strategic oil reserve base being constructed. It’s is meaningful to study on advanced tanks online testing technology, for maintaining tanks’stable operation, reducing or avoiding environmental pollution, and reducing the economic loss due to blindness repairing.
In this paper, the acoustic emission (AE) testing for storage tank is studied, which is mainly focusing on the AE signals processing. The study includes the following aspects:
1. The fusion of the AE signals is studied, and a new AE signals fusion method, which is based on cluster analysis, is proposed. The new fusion method could raise the accuracy of extracting AE events and reduce signal noise effectively.
2. According to the requirements of tank bottom AE source positioning, the triangulate positioning algorithm and the overdetermined positioning algorithm are studied. Different solution methods are developed for the two different algorithms. In order to study the two algorithms’positioning performance under ideal and non-ideal conditions, Monte Carlo method is used. The result would be very meaningful for analyzing the AE sources’location of tank bottom.
3. In order to identificate and assess the corrosion region of tank bottom from AE testing data, wavelet cluster algorithm is developed. And the AE source distribution information entropy is proposed to assess the identification effect. Through analysis of the real test results, the range of parameter settings in the wavelet cluster algorithm is optimized to raise the identification efficiency.
4. 25 oil tanks located in 11 cities are tested by AE testing method. The impacts of tank type on test results are analyzed. The methods of avoiding noise and interference signals during the testing process are summarized, which would be meaningful for grading the further AE testing of tank bottom.
5. Based on the study of AE signal processing algorithms, a tank bottom AE data analysis software is developed. The software is modular, scalable and multifunctional, such as AE signal waveform analysis, AE signal characteristic extraction and analysis, AE source location and tank bottom corrosion assessment, etc.
引文
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