基于超声检测的轮箍缺陷模糊模式识别研究
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摘要
轮箍是铁路机车行走的重要部件，在制造和使用过程中容易出现各种危害性缺陷，严重威胁到列车的行驶安全。在轮箍缺陷的超声无损检测中，缺陷信号的识别会受到轮箍标记、闸瓦、轮轨接触点等位置的反射信号及表面波等多种因素的干扰。本文在超声横波探伤方法的基础上，将模糊模式识别方法应用到机车轮箍的无损检测中。
以铁路内燃机车轮箍为实验检测对象，并依据国内现行检测标准，通过多个标准人工伤模拟轮箍自然缺陷。在分析了轮箍的结构特征以及轮箍缺陷产生规律的基础上，选择了大角度横波检测方法进行检测。并根据声学原理，对探头声场分布、轮箍检测方法作了一定的理论分析及实验验证。
运用大角度横波检测方法对轮箍进行检测，并通过谱分析，提取缺陷回波信号的频域特征。利用模糊模式识别相关技术，以模糊聚类方法建立典型缺陷模糊子集，再对未知缺陷应用基于择近原则模糊识别方法进行归类识别，实验结果证明了该方法的有效性。在对同一缺陷重复检测中，正确识别率高达92.5％，为该方法进一步应用于轮箍缺陷的实际检测和分类提供了依据。
在利用信号频域信息基础上，进一步结合小波变换的多尺度分析优点，首先对信号进行连续小波变换，然后提取各尺度下的幅度谱作为信号特征，对未知缺陷使用模糊聚类方法进行识别。实验结果表明，基于连续小波变换的幅度谱特征能有效地区分回波信号的局部差异，提高了不同模式间的可分性。
Wheel is the most important part of running locomotive. During manufacturingand running, all kinds of flaws may appear in it, which highly affect the traffic safetyof train. During the ultrasonic non-destructive testing of wheel flaws, reflection fromthe wheel brake, sign, touch point, and the surface wave all may influence the flawechoes recognition result. This paper presents an application of fuzzy patternrecognition method in ultrasonic transverse wave detection of wheel flaws.
Using a gas engine wheel as the test experiment sample, in order to simulate thenatural flaws, several typical artificial flaws were made in it according to the nationalwheel testing standard. By analyzing the structure of wheels and the law of flawappearance, an ultrasonic transverse wave detection method is selected. Referring tocorrelative acoustics theory, the probe sound field and the wheel testing rule arestudied, and the experiments verify the computing result.
Using the frequency domain values as the pattern features, the fuzzy clusteringmethod is presented to construct the fuzzy sets of typical flaws. The concept ofsimilarity degree and the choosing near principle are employed in the fuzzy patternrecognition. Experiments show that the method is efficiency. The correctlyrecognition ratio is up to 92.5％.
Further more, considering the multi-scale analysis merit of CWT, the echosignals are transformed by continuous wavelet first, then the spectrum of which ischosen as the pattern's feature values. The fuzzy cluster analysis method is applied inrecognition. Experiment results show that the method can enhance thediscriminability of different flaws, and achieve more promising performance.
以铁路内燃机车轮箍为实验检测对象，并依据国内现行检测标准，通过多个标准人工伤模拟轮箍自然缺陷。在分析了轮箍的结构特征以及轮箍缺陷产生规律的基础上，选择了大角度横波检测方法进行检测。并根据声学原理，对探头声场分布、轮箍检测方法作了一定的理论分析及实验验证。
运用大角度横波检测方法对轮箍进行检测，并通过谱分析，提取缺陷回波信号的频域特征。利用模糊模式识别相关技术，以模糊聚类方法建立典型缺陷模糊子集，再对未知缺陷应用基于择近原则模糊识别方法进行归类识别，实验结果证明了该方法的有效性。在对同一缺陷重复检测中，正确识别率高达92.5％，为该方法进一步应用于轮箍缺陷的实际检测和分类提供了依据。
在利用信号频域信息基础上，进一步结合小波变换的多尺度分析优点，首先对信号进行连续小波变换，然后提取各尺度下的幅度谱作为信号特征，对未知缺陷使用模糊聚类方法进行识别。实验结果表明，基于连续小波变换的幅度谱特征能有效地区分回波信号的局部差异，提高了不同模式间的可分性。
Wheel is the most important part of running locomotive. During manufacturingand running, all kinds of flaws may appear in it, which highly affect the traffic safetyof train. During the ultrasonic non-destructive testing of wheel flaws, reflection fromthe wheel brake, sign, touch point, and the surface wave all may influence the flawechoes recognition result. This paper presents an application of fuzzy patternrecognition method in ultrasonic transverse wave detection of wheel flaws.
Using a gas engine wheel as the test experiment sample, in order to simulate thenatural flaws, several typical artificial flaws were made in it according to the nationalwheel testing standard. By analyzing the structure of wheels and the law of flawappearance, an ultrasonic transverse wave detection method is selected. Referring tocorrelative acoustics theory, the probe sound field and the wheel testing rule arestudied, and the experiments verify the computing result.
Using the frequency domain values as the pattern features, the fuzzy clusteringmethod is presented to construct the fuzzy sets of typical flaws. The concept ofsimilarity degree and the choosing near principle are employed in the fuzzy patternrecognition. Experiments show that the method is efficiency. The correctlyrecognition ratio is up to 92.5％.
Further more, considering the multi-scale analysis merit of CWT, the echosignals are transformed by continuous wavelet first, then the spectrum of which ischosen as the pattern's feature values. The fuzzy cluster analysis method is applied inrecognition. Experiment results show that the method can enhance thediscriminability of different flaws, and achieve more promising performance.
引文
[1] 孙国平，沈志坚．国外机车车辆走行部件无损检测的现状和发展．国外机车车辆工艺．1998(6)
[2] 潘健壮，原毅，刘继，陈建龙．铁道车辆轮箍裂纹检测系统．上海铁道科技，1998(03)：19-21
[3] 胥治成．SS6B型机车轮箍踏面剥离问题初探与防止措施．西铁科技，2003(2)：24-25
[4] 黄冰峰，游志宇，冯常．基于工控机控制的轮对数控车床检测装置．工业控制计算机．2001，19(1)：19-20．
[5] 王其琳，李建勇，郑立江，李庆祥．智能化测量在车轮轮径检测中的应用．铁道车辆．1994，4：12-14．
[6] 沈钢，黎冠中，李小江，曹志礼．轮轨踏面外形的实际测量及几何接触的进一步研究．铁道学报．1999，21(5)：24-28．
[7] 史海滨，蒋齐密，康宜华，张新访．随动式火车轮自动在线检测系统的传感器设计．仪表技术与传感器．2001，4：8-9．
[8] 徐扬 等著．模糊模式识别及其应用[M]，成都：西南交通大学出版社．1998．
[9] 彭祖赠，孙韫玉．模糊数学及其应用[M]，武汉：武汉大学出版社，2002
[10] 张海燕等．小波变换和模糊模式识别技术在金属超声检测缺陷分类中的应用．无损检测，2000，22(2)：51-54．
[11] 张海燕，刘镇清．模糊数学在超声检测中的应用．无损检测，2001，23(6)，260-263，
[12] 陆俭伟，吕干霖，等．管材超声检测中缺陷模糊模式识别方法研究．声学学报，1996，21(1)：20-28．
[13] 吕干霖，黎宗潼 等．超声检测中缺陷模糊聚类识别．声学学报，1996(增刊)，21(4)：592-600．
[14] 吕干霖，黎宗潼，蔡杰．超声检测缺陷模糊识别模型．无损检测，1996，18(5)：121-124
[15] 刘镇清等．强散射材料超声检测的模糊检测．声学学报，1997，22(5)：408-413．
[16] Baldev Raj, Hallai C, Kulcsar P, et al. Non-Destructive Testing[M]. Elsevier Science Publishers, 1992.
[17] Yao Min, On methodology of fuzzy pattern recognition, IEEE, 2001(3): 361-366.
[18] 黎连修．轮箍超声横波检测研究．无损检测，2000，22(4)，162-164．
[19] 张全惠，黎连修，等．不动车轮箍超声横波检测．理化检验．2001(2)：39-42
[20] 蒋危平，方京等．超声检测学[M]．武汉：武汉测绘科技大学出版社．1991．
[21] Albert Boggess, Francis J. Narcowich. A First Course in Wavelet with Fourier Analysis[M]. Beijing: Publish House of Electronics Industry. 2004.
[22] S. P. Das Gupta. Application of a fuzzy pattern recognition method in borehole geophysics. Computers & Geosciences. 27 (2001): 85-89.
[23] 余厚全等．一种超声石油固井质量检测回波信号识别的新方法．声学学报．1996，21(6)：922-927．
[24] 米露，张德俊．几种典型换能器声场特性的微机计算．声学技术，1986，(3)：7～14．
[25] G．韦德编，上海交通大学声全息研究室译．声成像-照相机、显微镜、相控阵和全息系统[M]．北京：国防工业出版社．1981．
[26] 范贤刚，李红建．SS7型电力机车轮箍崩裂的原因及防止措施．机车电传动，2003，(2)：47～49．
[27] 胡广书．数字信号处理—理论、算法与实现[M]．北京：清华大学出版社．2002．
[28] 彭玉华．小波变换与工程应用[M]．北京：科学出版社．2002．
[29] 边肇祺，边学工．模式识别[M]．北京：清华大学出版社．2003．
[30] 应崇福．超声学[M]．北京：科学出版社．1990．
[31] 冯若等．超声诊断设备原理与设计[M]．北京：中国医药科技出版社．1993．
[32] Miao Xiren, Zhang Peiming. Application of fuzzy pattern recognition to electrical apparatus evaluation. Proceedings of the 3" World Congress on Intelligent Control and Automation. Hefei, P.R. China, June 28-July 2,2000.
[33] 杜功焕，朱哲民，龚秀芬著．声学基础[M]．南京：南京大学出版社．2001．
[34] Boris Shukhat. Supervised fuzzy pattern recognition. Fuzzy Sets and Systems. 1998(100): 257-265.
[35] 姚天任，孙洪．现代数字信号处理[M]．武汉：华中理工大学出版社．1999
[36] C. Fritsch, A. Veca. Detecting small flaws near the interface in pulse-echo. Ultrasonics 2004(42): 797-801.
[37] 胡建恺，张谦琳．超声检测原理和方法[M]．合肥：中国科学技术大学出版社，1993．
[38] Xiaodong Liu. A new fuzzy model of pattern recognition and hitch diagnoses of complex systems. Fuzzy Sets and Systems. 1999(104): 289-296.
[39] 陈勤，张国煊，王小华等．基于模糊模式识别的文本自动分类法研究．浙江大学学报，2000，27(2)：292-295
[40] Brandt M, K haras Y. An error convergence simulation study of hard vs. fuzzy C-means clustering. In: IEEE International Conference on Fuzzy System. Orlando: The IEEE Neural Networks Council and the IEEE Orlando Section. 1994: 1835-1839
[41] Sam J, Chan Laiwan. Alternative membership function for sequential fuzzy clustering. In: IEEE International Conference on Fuzzy System. Orlando: The IEEE Neural Networks Council and the IEEE Orlando Section. 1994: 1846-1851
[2] 潘健壮，原毅，刘继，陈建龙．铁道车辆轮箍裂纹检测系统．上海铁道科技，1998(03)：19-21
[3] 胥治成．SS6B型机车轮箍踏面剥离问题初探与防止措施．西铁科技，2003(2)：24-25
[4] 黄冰峰，游志宇，冯常．基于工控机控制的轮对数控车床检测装置．工业控制计算机．2001，19(1)：19-20．
[5] 王其琳，李建勇，郑立江，李庆祥．智能化测量在车轮轮径检测中的应用．铁道车辆．1994，4：12-14．
[6] 沈钢，黎冠中，李小江，曹志礼．轮轨踏面外形的实际测量及几何接触的进一步研究．铁道学报．1999，21(5)：24-28．
[7] 史海滨，蒋齐密，康宜华，张新访．随动式火车轮自动在线检测系统的传感器设计．仪表技术与传感器．2001，4：8-9．
[8] 徐扬 等著．模糊模式识别及其应用[M]，成都：西南交通大学出版社．1998．
[9] 彭祖赠，孙韫玉．模糊数学及其应用[M]，武汉：武汉大学出版社，2002
[10] 张海燕等．小波变换和模糊模式识别技术在金属超声检测缺陷分类中的应用．无损检测，2000，22(2)：51-54．
[11] 张海燕，刘镇清．模糊数学在超声检测中的应用．无损检测，2001，23(6)，260-263，
[12] 陆俭伟，吕干霖，等．管材超声检测中缺陷模糊模式识别方法研究．声学学报，1996，21(1)：20-28．
[13] 吕干霖，黎宗潼 等．超声检测中缺陷模糊聚类识别．声学学报，1996(增刊)，21(4)：592-600．
[14] 吕干霖，黎宗潼，蔡杰．超声检测缺陷模糊识别模型．无损检测，1996，18(5)：121-124
[15] 刘镇清等．强散射材料超声检测的模糊检测．声学学报，1997，22(5)：408-413．
[16] Baldev Raj, Hallai C, Kulcsar P, et al. Non-Destructive Testing[M]. Elsevier Science Publishers, 1992.
[17] Yao Min, On methodology of fuzzy pattern recognition, IEEE, 2001(3): 361-366.
[18] 黎连修．轮箍超声横波检测研究．无损检测，2000，22(4)，162-164．
[19] 张全惠，黎连修，等．不动车轮箍超声横波检测．理化检验．2001(2)：39-42
[20] 蒋危平，方京等．超声检测学[M]．武汉：武汉测绘科技大学出版社．1991．
[21] Albert Boggess, Francis J. Narcowich. A First Course in Wavelet with Fourier Analysis[M]. Beijing: Publish House of Electronics Industry. 2004.
[22] S. P. Das Gupta. Application of a fuzzy pattern recognition method in borehole geophysics. Computers & Geosciences. 27 (2001): 85-89.
[23] 余厚全等．一种超声石油固井质量检测回波信号识别的新方法．声学学报．1996，21(6)：922-927．
[24] 米露，张德俊．几种典型换能器声场特性的微机计算．声学技术，1986，(3)：7～14．
[25] G．韦德编，上海交通大学声全息研究室译．声成像-照相机、显微镜、相控阵和全息系统[M]．北京：国防工业出版社．1981．
[26] 范贤刚，李红建．SS7型电力机车轮箍崩裂的原因及防止措施．机车电传动，2003，(2)：47～49．
[27] 胡广书．数字信号处理—理论、算法与实现[M]．北京：清华大学出版社．2002．
[28] 彭玉华．小波变换与工程应用[M]．北京：科学出版社．2002．
[29] 边肇祺，边学工．模式识别[M]．北京：清华大学出版社．2003．
[30] 应崇福．超声学[M]．北京：科学出版社．1990．
[31] 冯若等．超声诊断设备原理与设计[M]．北京：中国医药科技出版社．1993．
[32] Miao Xiren, Zhang Peiming. Application of fuzzy pattern recognition to electrical apparatus evaluation. Proceedings of the 3" World Congress on Intelligent Control and Automation. Hefei, P.R. China, June 28-July 2,2000.
[33] 杜功焕，朱哲民，龚秀芬著．声学基础[M]．南京：南京大学出版社．2001．
[34] Boris Shukhat. Supervised fuzzy pattern recognition. Fuzzy Sets and Systems. 1998(100): 257-265.
[35] 姚天任，孙洪．现代数字信号处理[M]．武汉：华中理工大学出版社．1999
[36] C. Fritsch, A. Veca. Detecting small flaws near the interface in pulse-echo. Ultrasonics 2004(42): 797-801.
[37] 胡建恺，张谦琳．超声检测原理和方法[M]．合肥：中国科学技术大学出版社，1993．
[38] Xiaodong Liu. A new fuzzy model of pattern recognition and hitch diagnoses of complex systems. Fuzzy Sets and Systems. 1999(104): 289-296.
[39] 陈勤，张国煊，王小华等．基于模糊模式识别的文本自动分类法研究．浙江大学学报，2000，27(2)：292-295
[40] Brandt M, K haras Y. An error convergence simulation study of hard vs. fuzzy C-means clustering. In: IEEE International Conference on Fuzzy System. Orlando: The IEEE Neural Networks Council and the IEEE Orlando Section. 1994: 1835-1839
[41] Sam J, Chan Laiwan. Alternative membership function for sequential fuzzy clustering. In: IEEE International Conference on Fuzzy System. Orlando: The IEEE Neural Networks Council and the IEEE Orlando Section. 1994: 1846-1851