基于在线检测信息的储罐底板腐蚀状态智能评价方法研究
|
摘要
储罐底板腐蚀状态的严重程度是指导储罐维修的主要指标,然而储罐底板的特殊运行环境决定了它难以检测的特点。目前国际上主要采用声发射检测技术对储罐底板腐蚀状态进行在线检测。该技术在我国已有近十年的发展历史,广泛应用于石油化工行业的常压储罐底板腐蚀状态检测上。但是目前应用声发射在线检测技术对储罐底板腐蚀状态进行评价主要依赖于检测人员的经验,成为该技术推广与发展的瓶颈。
本文利用声发射在线检测信息,结合领域专家经验,确定了与储罐底板腐蚀相关的声发射因素和外观检查因素。应用多元统计理论中的相关分析计算各因素与储罐底板腐蚀状态之间的相关性,并采用双尾检验对相关性的显著性进行验证。针对传统启发式贝叶斯网络结构搜索算法的局限性,本文应用遗传算法改进贝叶斯网络结构搜索算法,并采用最优保存策略设计遗传适应性函数,应用贝叶斯参数学习算法对所建立的贝叶斯网络拓扑结构进行参数学习,利用联合树推理算法对实例进行评价分析,从而建立基于检测信息的储罐底板腐蚀状态智能评价模型。
利用外观检查信息,根据相关分析结果,分别建立基于外观检查因素和基于相关显著外观检查因素的储罐底板腐蚀状态评价模型。通过对测试储罐数据的评价,模型准确率分别为84%和86%;利用声发射检测信息,根据相关标准,分别建立基于标准用声发射因素和基于声发射因素储罐底板腐蚀状态评价模型。通过对测试储罐数据的评价,模型准确率分别为70%和84%。综合前述研究成果,将显著相关的外观检查因素与声发射因素结合,建立基于在线检测信息的储罐底板腐蚀状态智能评价模型。通过对测试储罐数据的评价,该模型的评价准确率为96%。评价结果表明,该评价模型能有效的利用在线检测信息,对储罐底板腐蚀状态进行智能评价,具有一定的工程应用价值。
The condition of tank bottom corrosion is the main indicators to guide the maintenance of tank, however, the special operating environment of tank bottom make it difficult to detect. At present, the acoustic emission testing is used as the major testing technology to detect the tank bottom corrosion international. The technology has a history of nearly a decade in China, it is widely used in the testing of tank bottom corrosion petrochemical industry. But the current application of acoustic emission online testing technique to evaluate the tank bottom corrosion mainly depends on the experience of test people, it has become the bottleneck in development and promotion of the technology.
This paper combines acoustic emission testing information with experience of experts of areas, the acoustic emission factors and appearance inspection factors correlated with tank bottom corrosion are determined. Using correlation analysis of multivariate statistical to calculate the correlation between factors and the tank bottom corrosion, and testing the correlation by two-tailed test. For the limitations of traditional heuristic Bayesian networks structure search algorithm, this paper improves Bayesian networks structure search by genetic algorithm, designs the genetic fitness function by optimal preservation strategy, learns the parameters of the established of Bayesian networks topological structure by Bayesian parameters learning algorithm, evaluates and analyses the examples by using joint tree algorithm. To establishing intelligent method in evaluation of the tank bottom corrosion based on-line information.
Using the appearance inspection information, according to the results of correlation analysis, establish two evaluation methods of the tank bottom corrosion based on appearance inspection factors and significant correlation appearance inspection factors. Comparing with the result of acoustic emission online testing, the accuracies of the evaluation models are 84% and 86%; Using acoustic emission testing information, according to the testing standard, establish two evaluation methods of the tank bottom corrosion based on standard acoustic emission testing factors and acoustic emission testing factors, Comparing with the result of acoustic emission online testing, the accuracies of the evaluation models are 70% and 84%. Synthesize the above research results, combining the significant correlation appearance inspection factors with the acoustic emission testing factors, establish intelligent method in evaluation of the tank bottom corrosion based on online testing information. Comparing with the result of acoustic emission online testing, the accuracy of the evaluation model is 96%. This result shows that the evaluation model can effectively use the online testing information, and evaluate tank bottom corrosion intelligently, and can be applied in engineering applications.
本文利用声发射在线检测信息,结合领域专家经验,确定了与储罐底板腐蚀相关的声发射因素和外观检查因素。应用多元统计理论中的相关分析计算各因素与储罐底板腐蚀状态之间的相关性,并采用双尾检验对相关性的显著性进行验证。针对传统启发式贝叶斯网络结构搜索算法的局限性,本文应用遗传算法改进贝叶斯网络结构搜索算法,并采用最优保存策略设计遗传适应性函数,应用贝叶斯参数学习算法对所建立的贝叶斯网络拓扑结构进行参数学习,利用联合树推理算法对实例进行评价分析,从而建立基于检测信息的储罐底板腐蚀状态智能评价模型。
利用外观检查信息,根据相关分析结果,分别建立基于外观检查因素和基于相关显著外观检查因素的储罐底板腐蚀状态评价模型。通过对测试储罐数据的评价,模型准确率分别为84%和86%;利用声发射检测信息,根据相关标准,分别建立基于标准用声发射因素和基于声发射因素储罐底板腐蚀状态评价模型。通过对测试储罐数据的评价,模型准确率分别为70%和84%。综合前述研究成果,将显著相关的外观检查因素与声发射因素结合,建立基于在线检测信息的储罐底板腐蚀状态智能评价模型。通过对测试储罐数据的评价,该模型的评价准确率为96%。评价结果表明,该评价模型能有效的利用在线检测信息,对储罐底板腐蚀状态进行智能评价,具有一定的工程应用价值。
The condition of tank bottom corrosion is the main indicators to guide the maintenance of tank, however, the special operating environment of tank bottom make it difficult to detect. At present, the acoustic emission testing is used as the major testing technology to detect the tank bottom corrosion international. The technology has a history of nearly a decade in China, it is widely used in the testing of tank bottom corrosion petrochemical industry. But the current application of acoustic emission online testing technique to evaluate the tank bottom corrosion mainly depends on the experience of test people, it has become the bottleneck in development and promotion of the technology.
This paper combines acoustic emission testing information with experience of experts of areas, the acoustic emission factors and appearance inspection factors correlated with tank bottom corrosion are determined. Using correlation analysis of multivariate statistical to calculate the correlation between factors and the tank bottom corrosion, and testing the correlation by two-tailed test. For the limitations of traditional heuristic Bayesian networks structure search algorithm, this paper improves Bayesian networks structure search by genetic algorithm, designs the genetic fitness function by optimal preservation strategy, learns the parameters of the established of Bayesian networks topological structure by Bayesian parameters learning algorithm, evaluates and analyses the examples by using joint tree algorithm. To establishing intelligent method in evaluation of the tank bottom corrosion based on-line information.
Using the appearance inspection information, according to the results of correlation analysis, establish two evaluation methods of the tank bottom corrosion based on appearance inspection factors and significant correlation appearance inspection factors. Comparing with the result of acoustic emission online testing, the accuracies of the evaluation models are 84% and 86%; Using acoustic emission testing information, according to the testing standard, establish two evaluation methods of the tank bottom corrosion based on standard acoustic emission testing factors and acoustic emission testing factors, Comparing with the result of acoustic emission online testing, the accuracies of the evaluation models are 70% and 84%. Synthesize the above research results, combining the significant correlation appearance inspection factors with the acoustic emission testing factors, establish intelligent method in evaluation of the tank bottom corrosion based on online testing information. Comparing with the result of acoustic emission online testing, the accuracy of the evaluation model is 96%. This result shows that the evaluation model can effectively use the online testing information, and evaluate tank bottom corrosion intelligently, and can be applied in engineering applications.
引文
[1]王绍青.高含硫石脑油对常压储罐的影响及对策[J].化工设备与防腐蚀,2005,1:46～47.
[2]许振清,王观军.常压储罐腐蚀原因分析及防护[J].石油化工安全技术,2004, 19(3):40～41.
[3]李明福,唐卫军,孙正立.金属储罐罐底腐蚀及防治[J].石油化工设备,2007.36(3):100.
[4]李一博,孙立瑛,靳世久,等.大型常压储罐底板的声发射在线检测[J],天津大学学报.2008,41(1):11～16.
[5] Eric G Eckert. The Acoustic Noise Environment Associated with Leak Detection in Aboveground Storage Tanks[J]. Materials Evaluation, 1994: 954.
[6] Ming Huei Yang, Chang Bin Chou. An Investigation of Tank Bottom Leak Detection Using Acoustic Emission[J]. Materials Evaluation, 1998: 847.
[7]戴光,李善春,李伟.储罐的声发射在线检测技术与研究进展[J].压力容器,2005.22(3):33～35.
[8]关卫和,沈纯厚,陶元宏,等.大型立式储罐在线声发射检测与安全性评估[J].压力容器,2005.22(1):40～44.
[9]闫河.金属常压储罐底板声发射源特性研究[D].北京:北京工业大学硕士学位论文,2006.3～4.
[10] Phillip T. Cole, Stephen N. Gautrey. Development History of the Tankpac AE Tank Floor Corrosion Test [C].7th ECNDT, Copenhagen, 5 1998.
[11] P J van de Loo, B Herrmann. How Reliable is Acoustic Emission (AE) Tank Testing? The Quantified Results of an AE User group Correlation Study [C]. 7th European Conference on Nondestructive Testing. Copenhagen, 1998.
[12] Phil Cole, Jon Watson. Acoustic Emission for Corrosion Detection[C]. 3rd Middle East Nondestructive Testing Conference & Exhibition, Bahrain,2005.
[13]孙雷,沈建民,毛国均,等.压力容器声发射检测中常见非缺陷信号分析[J].无损检测,2008,30(8):550～552.
[14] Kwon Oh-Yang, Joo Yong-Chan. Source location in plate by u-sing wavelet transform of AE signals[C]. 14th International AE Symposium & 5th AE World Meeting, Hawaii, USA, 1998.
[15]邢菲菲.储罐罐底腐蚀声发射信号模式识别研究[D].天津:天津大学硕士学位论文,2008,30(8):41～55.
[16]王伟魁,曾周末,孙立瑛.基于相关分析的声发射储罐罐底检测降噪方法[J].振动与冲击,2010,29(8):178～180.
[17]王海涛,韩恩厚,柯伟.腐蚀领域中人工神经网络的应用进展[J].腐蚀科学与防护技术,2004,16(3):147～150.
[18] Shahab D. Mohaghegh, Shahab D. Mohaghegh, Carl Sisk. Building the foundation for Prudhoe Bay oil production optimisation using neural networks[J]. International Journal of Oil, Gas and Coal Technology. 2008, 1: 65～79.
[19]喻西崇,赵金洲,胡永全.人工神经网络预测注水腐蚀管道的剩余寿命[J].油气储运,2002,21(6)11～14.
[20]孔涛.用组合人工神经网络方法实现海水腐蚀的现场快速检测——建立海水腐蚀查询数据库[D].青岛:中国海洋大学硕士学位论文,2007.15.
[21]佟立强,张鹤群.油气集输管道腐蚀速率预测研究[J].管道技术与设备,2009,5:52～54.
[22]谭开忍,肖熙.基于灰色理论的海底管道腐蚀剩余寿命预测方法[J].上海交通大学学报,2007,2:178～180.
[23]龙学渊,袁宗.不等时距GM(1,1)模型在预测输气管道腐蚀中的应用[J].管道技术与设备,2006,1:35～37.
[24]刘武,季寿红,兰志林,等.灰色GM(1,1)模型在管道腐蚀预测中的应用[J].管道技术与设备,2008,5:52～53.
[25]李彬,秦燕,周劲.用模糊灰色理论评价管道腐蚀与防护态势[J].油气储运,2005,24(3):32~34.
[26]肖杰,刘丽川,欧益宏,等.储罐底板腐蚀的预测研究[J].腐蚀与防护,2004,25(12):526～528.
[27]刘刚,张赞牢.储罐腐蚀状况的灰色预测[J].石油规划设计,2005,16(4):26～27.
[28]肖杰,刘丽川,欧益宏,等.储罐腐蚀底板的可靠性数学模型研究[J].油气储运,2005,24(4):26～28.
[29]李春树,柳曾典,王印培.常压储罐底板腐蚀安全评估[J].压力容器,2009,26(2):35～38.
[30]李国义,王爱芳,杨淑香.储罐腐蚀剩余寿命计算[J].大庆石油学院学报,2005,29(1):67～68.
[31]栾丽华,吉根林.决策树分类技术研究[J].计算机工程,2004.30(9):94～96.
[32]陈励华.智能故障诊断技术的应用与研究[D].西安:西此工业大学硕士学位论文,2005.11～18.
[33]杨宇.基于MED和支持向量机的旋转机械故障诊断方法研究[D].长沙:湖南大学博士学位论文,2005.64～67.
[34]李旭升.贝叶斯网络分类模型研究及其在信用评估中的应用[D].成都:西南交通大学博士学位论文,2006.3～4.
[35]李俭川.贝叶斯网络故障诊断与维修决策方法及应用研究[D].长沙:国防科学技术大学博士学位论文,2002.1～4,9.
[36]王利民.贝叶斯学习理论中若干问题的研究[D].长春:吉林大学博士学位论文,2005.7.
[37]林春漪,尹俊勋,马丽红,等.基于模糊贝叶斯网络的星形细胞瘤智能分级模型[J].中国医学物理学杂志,2006,23(3):174～177.
[38]周波,杨国纬.基于贝叶斯算法的中国人名识别[J].计算机应用,2006,26(4):998～1000.
[39]左晓娜,刘冀伟,王志良.基于TAN贝叶斯网络分类器的测井岩性预测[J].微计算机信息,2006,22(9):284～286.
[40]李刚.知识发现的图模型方法[D].北京:中国科学院博士学位论文,2001.14.
[41] Ian H.Witten,Eibe Frank.数据挖掘实用及其学习技术[M].北京:机械工业出版社.2006,180～188,197～199.
[42] Akaike, H. New Look at the Statistical Model Identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716～723.
[43] Sehwarz, G. Estimating the Dimension of a Model[J]. Annals of Statistic.1978, 7(2): 461～464.
[44] Gregory F. Cooper, Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data[J]. Machine Learning, 1992, 9(4): 309～347.
[45]张少中.基于贝叶斯网络的知识发现与决策应用研究[D].大连:大连理工大学博士学位论文,2004.21.
[46]胡春玲.贝叶斯网络的结构学习算法研究[D].合肥:合肥工业大学硕士学位论文,2006.32.
[47] David Maxwell Chickering, David Heckerman, Christopher Meek. Large Sample Learning of Bayesian Networks is NP-Hard[J]. Journal of Machine Learning Research. 2004, 5: 1287～1330.
[48] Wray Buntine. Theory Refinement on Bayesian Networks[C]. Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. 1991: 52～60.
[49] David Maxwell Chickering. Learning Equivalence Classes of Bayesian Network Structures[J]. Journal of Machine Learning Research. 2002, 2: 445～498.
[50] Remco Ronaldus Bouckaert. Bayesian Belief Networks: From Construction to Inference[M]. University of Utrecht,1995. 65～69.
[51] David M. Chickering, Dan Geiger, David Heckerman. Learning Bayesian Networks: Search methods and Experimental Results[C]. Preliminary paper of the 5th International Workshop on Artificial Intelligence and Statistics, 1995, 112～128.
[52] Pedro Larranaga, Mikel Poza, Yosu Yurramendi. Structure Learning of Bayesian Networks by Genetic Algorithms: Performance Analysis of Control Parameters[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18: 912～926.
[53]蒋望东.基于遗传算法的贝叶斯分类器结构学习研究[D].南宁:广西师范大学硕士学位论文,2005.21.
[54] Raanan Yehezkel, Boaz Lerner. Bayesian Network Structure Learning by Recursive Autonomy Identification[J]. Journal of Machine Learning Research, 2009, 10: 1527-1570.
[55]黄清波,徐洁,罗小平.贝叶斯网络推理的联合树算法[C].中国仪器仪表与测控技术交流大会论文集(二),2007:894～896.
[56]程涤.改进型遗传算法在多位关联规则挖掘中的应用[M].武汉:武汉理工大学,2009.1～7.
[57]董立岩.贝叶斯网络应用基础研究[M].长春:吉林大学,2007.36～45.
[58]黎钧琪,石国桢.遗传算法交叉率与变异率关系的研究[J].武汉理工大学学报,2003,27(1):97～99.
[59]雷英杰,张善文,李续武,等.Matlab遗传算法工具箱及应用[M].西安电子科技大学出版社.2005:1～2.
[60]陈望宇,廖芹.基于遗传算法的贝叶斯网络模型研究[J].计算机工程与设,2009,30(11):2756～2759.
[61]廉捷.贝叶斯网络构造方法及应用研究[D].北京:北京交通大学硕士学位论文,2007.21～22.
[62] Friendman N, Geiger D, Goldszmidt M. Bayesian Network Classifiers [J]. Maehine Leaming, 1997, 29 (3): 131～163.
[63]曹华珍.原油贮罐底板的腐蚀机理研究与防护措施[D].杭州:浙江大学硕士学位论文,2002.1.
[64]阎永贵,吴建华,陈光章.原油储罐内底板的腐蚀防护现状与展望[J].腐蚀与防护,2002,23(5):199.
[65]张吉泉,王震宇,卢启春,等.立式原油罐底板腐蚀原因分析及解决方法[J].油气储运,2006.25(11):45～46.
[66]梁成浩,庄锁良,姜慧芬.石油化工设备材料腐蚀寿命预测系统[J].石油化工设备,2001.30(1): 21～22.
[67]徐自明.成品油储罐内的腐蚀及防护[J].储运技术,2003,12(5):41～43.
[68]李付绍,安茂忠,刘光洲,等.硫酸盐还原菌的含硫代谢产物在加速碳钢腐蚀中的作用[J].无机化学学报,2009,25(1):13～18.
[69]宋春才.浅谈金属储罐腐蚀与防护技术[J].今日科苑,2008,4:53.
[70]王德武.钢制储罐内部的腐蚀与防护[J].腐蚀与防护,1998,19(5): 221～222.
[71]赵淑稿,张绍举,刘钧泉.高温盐酸对碳钢腐蚀速率影响的探讨[J].石油化工设备安全技术,2008,29(4):25～27.
[72]高延敏,陈家坚,杨怀玉,等.A3钢在环烷酸中腐蚀的物理化学行为[J].材料保护,1999.32(6):14～15.
[73]谈平庆,谢水海,梅冬勇.大型原油储罐综合防腐蚀技术[J].石油化工腐蚀与防护,2005.22(2): 19～22.
[74]刘武超,王霁虹.原油储罐底板的腐蚀控制[J].全面腐蚀控制,2006,20(1):40.
[75]张斌.储罐基础沉降与变形的可靠度评价研究[D].大庆:大庆石油学院硕士学位论文,2007.17.
[76]张显亮,高洪波,石永军,等.保温储罐腐蚀分析及处理方法[J].油气储运,2003(1): 53~55.
[77]张耀,郑峥.储储罐腐蚀特征及失效分析[J].石油化工腐蚀与防护,2004,21(2):40～42.
[78]倪建乐.立式圆柱形储罐基础形状和罐底板寿命[J].油气储运,1994,13(5):48～56.
[79] HG 21502.1-92,钢制立式圆筒形固定顶储罐系列[S].
[80] HG 21502.2-92,钢制立式圆筒形内浮顶储罐系列[S].
[81]李君.保温层下防腐保护及冷喷铝技术[J].上海涂料,2008.46(6):19～21.
[82]张显亮,高洪波,石永军.保温储罐腐蚀分析及处理方法[J].油气储运,2003.22(1):53～55.
[83]徐至均,许朝铨,沈珠江.大型储罐基础设计与地基处理[M].北京:中国石化出版社,1999,4.
[84]谈平庆,谢水海,梅冬勇.大型原油储罐综合防腐蚀技术[J].石油化工腐蚀与防护.2005年,(2):19～24.
[85]朱吉新.原油储罐底板腐蚀的原因及其防护措施[J].材料保护,2009,42(5):57.
[86]张峰杰.大型原油储罐腐蚀的原因分析及防护对策[J].石油库与加油站,2008,17(2):42.
[87]季伟明.SPV490Q钢与16MnR钢横缝埋弧自动焊工艺评定[J].石油工程建设,2000,1:35～36.
[88]李丽菲,张万岭,沈功田,等.压力容器用钢氢致开裂的声发射试验研究[J].压力容器,2009,26(6):5～6.
[89]于国才,王振尧,韩薇,等.海南土壤化学成分对Q235钢腐蚀的影响[J].腐蚀与防护,2002,23(8):331～334.
[90]张文彤,闫洁.SPSS统计分析基础教程[M].北京:高等教育出版社,2004,329～330.
[91]秦川,黄欢,施化吉.基于区分矩阵的数据离散化算法[J].计算机工程与应用,2008.44(35):148～149.
[92]高原,耿国华,周明全.一种改进的快速数据离散化算法[J].小型微型计算机系统,2009.30(2):279～280.
[93]贺跃,郑建军,朱蕾.一种基于熵的连续属性离散化算法[J].计算机应用,2005.25(3): 637～638.
[94]刘国华.声发射信号处理关键技术研究[D].杭州:浙江大学博士学位论文.2008.120～121.
[95]王成双.贝叶斯网络学习、推理与应用[M].上海:立信会计出版社,2010:66～77.
[96]晏华.交易数据的聚类分析[D].成都:电子科技大学博士学位论文,2008.9～17.
[97]王晓飞,郭敏.结合模糊C均值聚类与图割的图像分割方法[J].计算机应用,2009,29(7):1918～1920.
[98]姜楠,周晓沧.基于非线性规划的数据离散化方法及其应用[J].清华大学学报(哲学社会科学版).2006.21(1): 54.
[99] X.L.Xie G.Beni.A Validity Measure for Fuzzy Clustering[J]. IEEE Transon Pattern Analysis and Machine Intelligence, 1991.13(8):841～847.
[100] JB/T 10764-2007,无损检测常压金属储罐声发射检测及评价方法[S].
[101]陈舵,李雪,崔杜武,等.一种基于模糊度的聚类有效性函数关[J].模式识别与人工智能,2008.21(1): 36～41.
[102] Gary Martin, John Dimopoulos. Acoustic Emission Monitoring As A Tool In Risk Based Assessments[C]. 12th A-PCNDT 2006.
[103]杨明维.声发射检测[M].北京:机械工业出版社,2005,41～44.
[2]许振清,王观军.常压储罐腐蚀原因分析及防护[J].石油化工安全技术,2004, 19(3):40～41.
[3]李明福,唐卫军,孙正立.金属储罐罐底腐蚀及防治[J].石油化工设备,2007.36(3):100.
[4]李一博,孙立瑛,靳世久,等.大型常压储罐底板的声发射在线检测[J],天津大学学报.2008,41(1):11～16.
[5] Eric G Eckert. The Acoustic Noise Environment Associated with Leak Detection in Aboveground Storage Tanks[J]. Materials Evaluation, 1994: 954.
[6] Ming Huei Yang, Chang Bin Chou. An Investigation of Tank Bottom Leak Detection Using Acoustic Emission[J]. Materials Evaluation, 1998: 847.
[7]戴光,李善春,李伟.储罐的声发射在线检测技术与研究进展[J].压力容器,2005.22(3):33～35.
[8]关卫和,沈纯厚,陶元宏,等.大型立式储罐在线声发射检测与安全性评估[J].压力容器,2005.22(1):40～44.
[9]闫河.金属常压储罐底板声发射源特性研究[D].北京:北京工业大学硕士学位论文,2006.3～4.
[10] Phillip T. Cole, Stephen N. Gautrey. Development History of the Tankpac AE Tank Floor Corrosion Test [C].7th ECNDT, Copenhagen, 5 1998.
[11] P J van de Loo, B Herrmann. How Reliable is Acoustic Emission (AE) Tank Testing? The Quantified Results of an AE User group Correlation Study [C]. 7th European Conference on Nondestructive Testing. Copenhagen, 1998.
[12] Phil Cole, Jon Watson. Acoustic Emission for Corrosion Detection[C]. 3rd Middle East Nondestructive Testing Conference & Exhibition, Bahrain,2005.
[13]孙雷,沈建民,毛国均,等.压力容器声发射检测中常见非缺陷信号分析[J].无损检测,2008,30(8):550～552.
[14] Kwon Oh-Yang, Joo Yong-Chan. Source location in plate by u-sing wavelet transform of AE signals[C]. 14th International AE Symposium & 5th AE World Meeting, Hawaii, USA, 1998.
[15]邢菲菲.储罐罐底腐蚀声发射信号模式识别研究[D].天津:天津大学硕士学位论文,2008,30(8):41～55.
[16]王伟魁,曾周末,孙立瑛.基于相关分析的声发射储罐罐底检测降噪方法[J].振动与冲击,2010,29(8):178～180.
[17]王海涛,韩恩厚,柯伟.腐蚀领域中人工神经网络的应用进展[J].腐蚀科学与防护技术,2004,16(3):147～150.
[18] Shahab D. Mohaghegh, Shahab D. Mohaghegh, Carl Sisk. Building the foundation for Prudhoe Bay oil production optimisation using neural networks[J]. International Journal of Oil, Gas and Coal Technology. 2008, 1: 65～79.
[19]喻西崇,赵金洲,胡永全.人工神经网络预测注水腐蚀管道的剩余寿命[J].油气储运,2002,21(6)11～14.
[20]孔涛.用组合人工神经网络方法实现海水腐蚀的现场快速检测——建立海水腐蚀查询数据库[D].青岛:中国海洋大学硕士学位论文,2007.15.
[21]佟立强,张鹤群.油气集输管道腐蚀速率预测研究[J].管道技术与设备,2009,5:52～54.
[22]谭开忍,肖熙.基于灰色理论的海底管道腐蚀剩余寿命预测方法[J].上海交通大学学报,2007,2:178～180.
[23]龙学渊,袁宗.不等时距GM(1,1)模型在预测输气管道腐蚀中的应用[J].管道技术与设备,2006,1:35～37.
[24]刘武,季寿红,兰志林,等.灰色GM(1,1)模型在管道腐蚀预测中的应用[J].管道技术与设备,2008,5:52～53.
[25]李彬,秦燕,周劲.用模糊灰色理论评价管道腐蚀与防护态势[J].油气储运,2005,24(3):32~34.
[26]肖杰,刘丽川,欧益宏,等.储罐底板腐蚀的预测研究[J].腐蚀与防护,2004,25(12):526～528.
[27]刘刚,张赞牢.储罐腐蚀状况的灰色预测[J].石油规划设计,2005,16(4):26～27.
[28]肖杰,刘丽川,欧益宏,等.储罐腐蚀底板的可靠性数学模型研究[J].油气储运,2005,24(4):26～28.
[29]李春树,柳曾典,王印培.常压储罐底板腐蚀安全评估[J].压力容器,2009,26(2):35～38.
[30]李国义,王爱芳,杨淑香.储罐腐蚀剩余寿命计算[J].大庆石油学院学报,2005,29(1):67～68.
[31]栾丽华,吉根林.决策树分类技术研究[J].计算机工程,2004.30(9):94～96.
[32]陈励华.智能故障诊断技术的应用与研究[D].西安:西此工业大学硕士学位论文,2005.11～18.
[33]杨宇.基于MED和支持向量机的旋转机械故障诊断方法研究[D].长沙:湖南大学博士学位论文,2005.64～67.
[34]李旭升.贝叶斯网络分类模型研究及其在信用评估中的应用[D].成都:西南交通大学博士学位论文,2006.3～4.
[35]李俭川.贝叶斯网络故障诊断与维修决策方法及应用研究[D].长沙:国防科学技术大学博士学位论文,2002.1～4,9.
[36]王利民.贝叶斯学习理论中若干问题的研究[D].长春:吉林大学博士学位论文,2005.7.
[37]林春漪,尹俊勋,马丽红,等.基于模糊贝叶斯网络的星形细胞瘤智能分级模型[J].中国医学物理学杂志,2006,23(3):174～177.
[38]周波,杨国纬.基于贝叶斯算法的中国人名识别[J].计算机应用,2006,26(4):998～1000.
[39]左晓娜,刘冀伟,王志良.基于TAN贝叶斯网络分类器的测井岩性预测[J].微计算机信息,2006,22(9):284～286.
[40]李刚.知识发现的图模型方法[D].北京:中国科学院博士学位论文,2001.14.
[41] Ian H.Witten,Eibe Frank.数据挖掘实用及其学习技术[M].北京:机械工业出版社.2006,180～188,197～199.
[42] Akaike, H. New Look at the Statistical Model Identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716～723.
[43] Sehwarz, G. Estimating the Dimension of a Model[J]. Annals of Statistic.1978, 7(2): 461～464.
[44] Gregory F. Cooper, Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data[J]. Machine Learning, 1992, 9(4): 309～347.
[45]张少中.基于贝叶斯网络的知识发现与决策应用研究[D].大连:大连理工大学博士学位论文,2004.21.
[46]胡春玲.贝叶斯网络的结构学习算法研究[D].合肥:合肥工业大学硕士学位论文,2006.32.
[47] David Maxwell Chickering, David Heckerman, Christopher Meek. Large Sample Learning of Bayesian Networks is NP-Hard[J]. Journal of Machine Learning Research. 2004, 5: 1287～1330.
[48] Wray Buntine. Theory Refinement on Bayesian Networks[C]. Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. 1991: 52～60.
[49] David Maxwell Chickering. Learning Equivalence Classes of Bayesian Network Structures[J]. Journal of Machine Learning Research. 2002, 2: 445～498.
[50] Remco Ronaldus Bouckaert. Bayesian Belief Networks: From Construction to Inference[M]. University of Utrecht,1995. 65～69.
[51] David M. Chickering, Dan Geiger, David Heckerman. Learning Bayesian Networks: Search methods and Experimental Results[C]. Preliminary paper of the 5th International Workshop on Artificial Intelligence and Statistics, 1995, 112～128.
[52] Pedro Larranaga, Mikel Poza, Yosu Yurramendi. Structure Learning of Bayesian Networks by Genetic Algorithms: Performance Analysis of Control Parameters[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18: 912～926.
[53]蒋望东.基于遗传算法的贝叶斯分类器结构学习研究[D].南宁:广西师范大学硕士学位论文,2005.21.
[54] Raanan Yehezkel, Boaz Lerner. Bayesian Network Structure Learning by Recursive Autonomy Identification[J]. Journal of Machine Learning Research, 2009, 10: 1527-1570.
[55]黄清波,徐洁,罗小平.贝叶斯网络推理的联合树算法[C].中国仪器仪表与测控技术交流大会论文集(二),2007:894～896.
[56]程涤.改进型遗传算法在多位关联规则挖掘中的应用[M].武汉:武汉理工大学,2009.1～7.
[57]董立岩.贝叶斯网络应用基础研究[M].长春:吉林大学,2007.36～45.
[58]黎钧琪,石国桢.遗传算法交叉率与变异率关系的研究[J].武汉理工大学学报,2003,27(1):97～99.
[59]雷英杰,张善文,李续武,等.Matlab遗传算法工具箱及应用[M].西安电子科技大学出版社.2005:1～2.
[60]陈望宇,廖芹.基于遗传算法的贝叶斯网络模型研究[J].计算机工程与设,2009,30(11):2756～2759.
[61]廉捷.贝叶斯网络构造方法及应用研究[D].北京:北京交通大学硕士学位论文,2007.21～22.
[62] Friendman N, Geiger D, Goldszmidt M. Bayesian Network Classifiers [J]. Maehine Leaming, 1997, 29 (3): 131～163.
[63]曹华珍.原油贮罐底板的腐蚀机理研究与防护措施[D].杭州:浙江大学硕士学位论文,2002.1.
[64]阎永贵,吴建华,陈光章.原油储罐内底板的腐蚀防护现状与展望[J].腐蚀与防护,2002,23(5):199.
[65]张吉泉,王震宇,卢启春,等.立式原油罐底板腐蚀原因分析及解决方法[J].油气储运,2006.25(11):45～46.
[66]梁成浩,庄锁良,姜慧芬.石油化工设备材料腐蚀寿命预测系统[J].石油化工设备,2001.30(1): 21～22.
[67]徐自明.成品油储罐内的腐蚀及防护[J].储运技术,2003,12(5):41～43.
[68]李付绍,安茂忠,刘光洲,等.硫酸盐还原菌的含硫代谢产物在加速碳钢腐蚀中的作用[J].无机化学学报,2009,25(1):13～18.
[69]宋春才.浅谈金属储罐腐蚀与防护技术[J].今日科苑,2008,4:53.
[70]王德武.钢制储罐内部的腐蚀与防护[J].腐蚀与防护,1998,19(5): 221～222.
[71]赵淑稿,张绍举,刘钧泉.高温盐酸对碳钢腐蚀速率影响的探讨[J].石油化工设备安全技术,2008,29(4):25～27.
[72]高延敏,陈家坚,杨怀玉,等.A3钢在环烷酸中腐蚀的物理化学行为[J].材料保护,1999.32(6):14～15.
[73]谈平庆,谢水海,梅冬勇.大型原油储罐综合防腐蚀技术[J].石油化工腐蚀与防护,2005.22(2): 19～22.
[74]刘武超,王霁虹.原油储罐底板的腐蚀控制[J].全面腐蚀控制,2006,20(1):40.
[75]张斌.储罐基础沉降与变形的可靠度评价研究[D].大庆:大庆石油学院硕士学位论文,2007.17.
[76]张显亮,高洪波,石永军,等.保温储罐腐蚀分析及处理方法[J].油气储运,2003(1): 53~55.
[77]张耀,郑峥.储储罐腐蚀特征及失效分析[J].石油化工腐蚀与防护,2004,21(2):40～42.
[78]倪建乐.立式圆柱形储罐基础形状和罐底板寿命[J].油气储运,1994,13(5):48～56.
[79] HG 21502.1-92,钢制立式圆筒形固定顶储罐系列[S].
[80] HG 21502.2-92,钢制立式圆筒形内浮顶储罐系列[S].
[81]李君.保温层下防腐保护及冷喷铝技术[J].上海涂料,2008.46(6):19～21.
[82]张显亮,高洪波,石永军.保温储罐腐蚀分析及处理方法[J].油气储运,2003.22(1):53～55.
[83]徐至均,许朝铨,沈珠江.大型储罐基础设计与地基处理[M].北京:中国石化出版社,1999,4.
[84]谈平庆,谢水海,梅冬勇.大型原油储罐综合防腐蚀技术[J].石油化工腐蚀与防护.2005年,(2):19～24.
[85]朱吉新.原油储罐底板腐蚀的原因及其防护措施[J].材料保护,2009,42(5):57.
[86]张峰杰.大型原油储罐腐蚀的原因分析及防护对策[J].石油库与加油站,2008,17(2):42.
[87]季伟明.SPV490Q钢与16MnR钢横缝埋弧自动焊工艺评定[J].石油工程建设,2000,1:35～36.
[88]李丽菲,张万岭,沈功田,等.压力容器用钢氢致开裂的声发射试验研究[J].压力容器,2009,26(6):5～6.
[89]于国才,王振尧,韩薇,等.海南土壤化学成分对Q235钢腐蚀的影响[J].腐蚀与防护,2002,23(8):331～334.
[90]张文彤,闫洁.SPSS统计分析基础教程[M].北京:高等教育出版社,2004,329～330.
[91]秦川,黄欢,施化吉.基于区分矩阵的数据离散化算法[J].计算机工程与应用,2008.44(35):148～149.
[92]高原,耿国华,周明全.一种改进的快速数据离散化算法[J].小型微型计算机系统,2009.30(2):279～280.
[93]贺跃,郑建军,朱蕾.一种基于熵的连续属性离散化算法[J].计算机应用,2005.25(3): 637～638.
[94]刘国华.声发射信号处理关键技术研究[D].杭州:浙江大学博士学位论文.2008.120～121.
[95]王成双.贝叶斯网络学习、推理与应用[M].上海:立信会计出版社,2010:66～77.
[96]晏华.交易数据的聚类分析[D].成都:电子科技大学博士学位论文,2008.9～17.
[97]王晓飞,郭敏.结合模糊C均值聚类与图割的图像分割方法[J].计算机应用,2009,29(7):1918～1920.
[98]姜楠,周晓沧.基于非线性规划的数据离散化方法及其应用[J].清华大学学报(哲学社会科学版).2006.21(1): 54.
[99] X.L.Xie G.Beni.A Validity Measure for Fuzzy Clustering[J]. IEEE Transon Pattern Analysis and Machine Intelligence, 1991.13(8):841～847.
[100] JB/T 10764-2007,无损检测常压金属储罐声发射检测及评价方法[S].
[101]陈舵,李雪,崔杜武,等.一种基于模糊度的聚类有效性函数关[J].模式识别与人工智能,2008.21(1): 36～41.
[102] Gary Martin, John Dimopoulos. Acoustic Emission Monitoring As A Tool In Risk Based Assessments[C]. 12th A-PCNDT 2006.
[103]杨明维.声发射检测[M].北京:机械工业出版社,2005,41～44.