起重机的声发射源特性及识别方法研究
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摘要
起重机是广泛使用的大型机电类特种设备,其安全运行与社会经济发展息息相关,我国在役起重设备数量多,增幅快,截至2007年底在用起重设备总量95.79万台,与2003年底相比增幅达72.3%,开展无损检测是保障起重机安全运行的重要手段。近年来,声发射技术在压力容器、航空航天等行业的无损检测中得到了广泛的应用,与超声、磁粉、射线、渗透等常规无损检测方法相比,它具有对活性缺陷敏感、一次试验中可对被检结构件或设备进行整体检测、检验周期短、效率高等优点,但对起重机的无损检测,目前还处于起步阶段,对起重机工作现场的声发射源缺乏认识。因此,研究起重机工作现场的声发射源特性、寻求有效的声发射源识别方法是目前检测中急需解决的问题,也是制定起重机声发射检测标准和开展现场检验的前提。本文结合国家“十一五”科技支撑计划项目,对起重机工作过程中的各种典型声发射源特性及识别方法开展研究,完成的主要工作有:
(1)通过Q235钢和Q345钢母材和焊缝试件拉伸过程的声发射监测,获取了四种试件拉伸过程的声发射特征,结果表明:拉伸过程的声发射行为与材料内部损伤是相吻合的;利用声发射有效值电压(RMS)曲线和能量率曲线能清晰的观察到屈服点的出现,也能观测到焊缝试件拉伸过程的多次屈服现象,Q345钢焊缝试件的双屈服现象尤为明显,而这些现象在应力应变曲线中是不能反映出来的。
(2)在Q235、Q345钢拉伸试验和箱形、槽形试件弯曲试验过程的声发射监测试验所获取的材料拉伸过程和结构件弯曲过程声发射特性基础上,对带有焊接表面裂纹的大型结构件——起重机箱形梁进行了破坏性试验,监测了试验全过程的声发射现象,对比各级载荷下的应力值和表面裂纹区域的磁记忆检测结果,得到了表面裂纹扩展和塑性变形声发射源的声发射特性,包括:声发射定位特征、参数分布特征和波形频谱特征。
(3)通过大量的起重机现场试验,系统地获取了桥式/门式起重机工作过程中六种典型声发射源的声发射特性,分别为大车/小车移动、起升/下降制动、结构摩擦、氧化皮剥落、雨滴噪声、电器设备噪音等;同时完成了起重机主梁上的衰减特性测量和线性定位试验,结果表明:线性定位方法可以对箱形梁、桁架主梁上的声发射源进行正确定位。
(4)研究了小波分析在起重机声发射信号处理中的应用问题,分析了声发射信号处理中的小波基选取方法,根据小波分析的Mallat算法,推导了小波分解的最大尺度公式,确定了各尺度下信号的频率范围,提出了基于小波能谱系数的起重机声发射源特征提取方法,实例证明,该方法能够对表面裂纹扩展、塑性变形、结构摩擦和小车移动四种声发射源进行正确的识别。
(5)研究了神经网络在起重机声发射波形信号模式识别中的应用问题,通过对BP网络的构造、初始权值的选取、算法的选择等问题的研究,提出了改进的RPROP算法;结合基于小波分析的声发射信号特征提取,设计并培训了能对起重机工作过程典型声发射源进行正确识别的神经网络,通过对起重机实际声发射检验获得的信号进行模式识别,验证了所构建网络的可靠性。
本文的研究成果有助于提高对起重机声发射源的全面认识,为起重机声发射检测及结果评价方法标准的制定奠定基础,对推动声发射技术在起重机行业的应用开展具有重要意义和实用价值。
As special and large mechanic-electrical equipment,crane is widely used.Its safe operation is closely related to social and economic development.There is a large number of lifting equipment in use in our country with a fast growing speed.By the end of 2007,there had been 957,900 cranes in use,which has increased by 72.3% compared with the same period in 2003.Conducting non-destructive testing(NDT) plays an important role in guaranteeing the safe operation of cranes.In the past few years,acoustic emission(AE) technology as a new NDT method has been widely used in such fields as pressure vessels,aerospace engineering,etc.Compared with such conventional NDT as ultrasonic testing(MT),magnetic particle testing(MT), radiographic testing(RT) and penetrant testing(PT),AE testing(AET) has many advantages such as being sensitive to active defects,whole monitoring the structures or equipment in a testing,having short testing period as well as high efficiency.But in the NDT of cranes,it is still at the initial stage.Moreover,it lacks cognition of AE sources in the crane operation field.Therefore,it is urgent to study AE sources characteristics in the crane operation field and to seek for an efficient AE sources recognition method.It is also the premise in drafting AET standard for crane and conducting field inspection.By referring to the studies of the 11th Five-yea Plan of the China Key Technologies of R&D Program(No.2006BAK02B04),the dissertation studies all kinds of classical AE sources characteristics and their recognition methods in the working process of crane.The tasks finished are as follows:
(1) Through the tensile testing of the Q235 and Q345 steel both material and welding;the AE characteristics of the four specimens were acquired.The results indicate that:the AE behavior corresponds with the internal damage of the material; the yield point of the specimen can be clearly observed from the AE RMS voltage curve and the energy rate curve,and the weld specimen yields many times especially for Q345 steel with two-yield phenomenon,which can not be observed in the stress-strain curves.
(2) On the basis of AE sources characteristics acquired from the AE inspection testing in the process of tensile testing of the Q235 and Q345 steel and bending testing of box beam and trough specimen,the author conducted a destructive testing on large structural—box girder of the crane with surface cracks in the weld.AE phenomena in the testing process are also inspected.Compared with the results of the stress and metal magnetic memory testing on the area of prefabricated surface cracks in each levels,the author acquired the AE sources characteristics of surface cracks propagation in the weld and plastic deformation,including AE location features,parameter distribution features and wave spectrum characteristics.
(3) By conducting many crane field AET,the author systematically obtained six kinds of classical AE characteristics of AE sources from the working process of overhead travelling cranes and portal bridge cranes.They are noises caused by moving vehicles and trolleys,brake in lifting and descending,structure friction, peeling of the oxide and paint,raindrops and electrical equipment noises respectively. Meanwhile,the author finished measuring the attenuation curve of the crane girder and linear location testing.The results show linear location testing can accurately locate the AE sources of box girder and truss girder.
(4) The author studied the application of wavelet analysis in processing crane AE signals.The rules of how to select the suitable wavelets for AE signal processing were analyzed,and Daubechies wavelet was selected for the crane AE.According to the Mallat arithmetic,the maximum decomposition level of wavelet analysis was also formulated.Moreover,the author fixed the frequency bands on each decomposition level of the signal and proposed the characteristic extraction method for crane acoustic emission resources based on the energy spectrum coefficients of wavelet analysis.It is proved that the method can accurately recognize the four AE sources including surface cracks propagation,plastic deformation,structure friction as well as moving vehicles and trolleys.
(5) The author studied the application of neural network in the pattern recognition of crane AE wave signal.Based on studying BP network structure and algorithmic selection,the improved BP algorithm was proposed.Combining the extraction of AE signal characteristics based on wavelet analysis,the author designed and trained a neural network which can accurately recognize classical AE sources of crane working process.By recognizing the signal acquired from actual crane AE testing,its reliability was validated.
The research results in this dissertation will contribute to the comprehensive understanding of AE sources and lay a foundation for the standard drafted of AE examination and evaluation of cranes.It is of vital importance and utility value in promoting the application of AE technology in the field of cranes.
(1)通过Q235钢和Q345钢母材和焊缝试件拉伸过程的声发射监测,获取了四种试件拉伸过程的声发射特征,结果表明:拉伸过程的声发射行为与材料内部损伤是相吻合的;利用声发射有效值电压(RMS)曲线和能量率曲线能清晰的观察到屈服点的出现,也能观测到焊缝试件拉伸过程的多次屈服现象,Q345钢焊缝试件的双屈服现象尤为明显,而这些现象在应力应变曲线中是不能反映出来的。
(2)在Q235、Q345钢拉伸试验和箱形、槽形试件弯曲试验过程的声发射监测试验所获取的材料拉伸过程和结构件弯曲过程声发射特性基础上,对带有焊接表面裂纹的大型结构件——起重机箱形梁进行了破坏性试验,监测了试验全过程的声发射现象,对比各级载荷下的应力值和表面裂纹区域的磁记忆检测结果,得到了表面裂纹扩展和塑性变形声发射源的声发射特性,包括:声发射定位特征、参数分布特征和波形频谱特征。
(3)通过大量的起重机现场试验,系统地获取了桥式/门式起重机工作过程中六种典型声发射源的声发射特性,分别为大车/小车移动、起升/下降制动、结构摩擦、氧化皮剥落、雨滴噪声、电器设备噪音等;同时完成了起重机主梁上的衰减特性测量和线性定位试验,结果表明:线性定位方法可以对箱形梁、桁架主梁上的声发射源进行正确定位。
(4)研究了小波分析在起重机声发射信号处理中的应用问题,分析了声发射信号处理中的小波基选取方法,根据小波分析的Mallat算法,推导了小波分解的最大尺度公式,确定了各尺度下信号的频率范围,提出了基于小波能谱系数的起重机声发射源特征提取方法,实例证明,该方法能够对表面裂纹扩展、塑性变形、结构摩擦和小车移动四种声发射源进行正确的识别。
(5)研究了神经网络在起重机声发射波形信号模式识别中的应用问题,通过对BP网络的构造、初始权值的选取、算法的选择等问题的研究,提出了改进的RPROP算法;结合基于小波分析的声发射信号特征提取,设计并培训了能对起重机工作过程典型声发射源进行正确识别的神经网络,通过对起重机实际声发射检验获得的信号进行模式识别,验证了所构建网络的可靠性。
本文的研究成果有助于提高对起重机声发射源的全面认识,为起重机声发射检测及结果评价方法标准的制定奠定基础,对推动声发射技术在起重机行业的应用开展具有重要意义和实用价值。
As special and large mechanic-electrical equipment,crane is widely used.Its safe operation is closely related to social and economic development.There is a large number of lifting equipment in use in our country with a fast growing speed.By the end of 2007,there had been 957,900 cranes in use,which has increased by 72.3% compared with the same period in 2003.Conducting non-destructive testing(NDT) plays an important role in guaranteeing the safe operation of cranes.In the past few years,acoustic emission(AE) technology as a new NDT method has been widely used in such fields as pressure vessels,aerospace engineering,etc.Compared with such conventional NDT as ultrasonic testing(MT),magnetic particle testing(MT), radiographic testing(RT) and penetrant testing(PT),AE testing(AET) has many advantages such as being sensitive to active defects,whole monitoring the structures or equipment in a testing,having short testing period as well as high efficiency.But in the NDT of cranes,it is still at the initial stage.Moreover,it lacks cognition of AE sources in the crane operation field.Therefore,it is urgent to study AE sources characteristics in the crane operation field and to seek for an efficient AE sources recognition method.It is also the premise in drafting AET standard for crane and conducting field inspection.By referring to the studies of the 11th Five-yea Plan of the China Key Technologies of R&D Program(No.2006BAK02B04),the dissertation studies all kinds of classical AE sources characteristics and their recognition methods in the working process of crane.The tasks finished are as follows:
(1) Through the tensile testing of the Q235 and Q345 steel both material and welding;the AE characteristics of the four specimens were acquired.The results indicate that:the AE behavior corresponds with the internal damage of the material; the yield point of the specimen can be clearly observed from the AE RMS voltage curve and the energy rate curve,and the weld specimen yields many times especially for Q345 steel with two-yield phenomenon,which can not be observed in the stress-strain curves.
(2) On the basis of AE sources characteristics acquired from the AE inspection testing in the process of tensile testing of the Q235 and Q345 steel and bending testing of box beam and trough specimen,the author conducted a destructive testing on large structural—box girder of the crane with surface cracks in the weld.AE phenomena in the testing process are also inspected.Compared with the results of the stress and metal magnetic memory testing on the area of prefabricated surface cracks in each levels,the author acquired the AE sources characteristics of surface cracks propagation in the weld and plastic deformation,including AE location features,parameter distribution features and wave spectrum characteristics.
(3) By conducting many crane field AET,the author systematically obtained six kinds of classical AE characteristics of AE sources from the working process of overhead travelling cranes and portal bridge cranes.They are noises caused by moving vehicles and trolleys,brake in lifting and descending,structure friction, peeling of the oxide and paint,raindrops and electrical equipment noises respectively. Meanwhile,the author finished measuring the attenuation curve of the crane girder and linear location testing.The results show linear location testing can accurately locate the AE sources of box girder and truss girder.
(4) The author studied the application of wavelet analysis in processing crane AE signals.The rules of how to select the suitable wavelets for AE signal processing were analyzed,and Daubechies wavelet was selected for the crane AE.According to the Mallat arithmetic,the maximum decomposition level of wavelet analysis was also formulated.Moreover,the author fixed the frequency bands on each decomposition level of the signal and proposed the characteristic extraction method for crane acoustic emission resources based on the energy spectrum coefficients of wavelet analysis.It is proved that the method can accurately recognize the four AE sources including surface cracks propagation,plastic deformation,structure friction as well as moving vehicles and trolleys.
(5) The author studied the application of neural network in the pattern recognition of crane AE wave signal.Based on studying BP network structure and algorithmic selection,the improved BP algorithm was proposed.Combining the extraction of AE signal characteristics based on wavelet analysis,the author designed and trained a neural network which can accurately recognize classical AE sources of crane working process.By recognizing the signal acquired from actual crane AE testing,its reliability was validated.
The research results in this dissertation will contribute to the comprehensive understanding of AE sources and lay a foundation for the standard drafted of AE examination and evaluation of cranes.It is of vital importance and utility value in promoting the application of AE technology in the field of cranes.
引文
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