混流式水轮机叶片裂纹声发射监测的若干关键技术研究
|
摘要
混流式水轮机是水电站普遍采用的机型。其转轮担负着把水能转化成机械能的重要任务,但极易产生裂纹。转轮出现裂纹将严重地危及电站的运行稳定性和运行安全,而频繁停机焊补裂纹又给电站造很大的经济损失。尽管从设计和制造方面进行了大量的研究,但仍不能完全解决混流式转轮的裂纹问题。若能监测裂纹的产生、掌握裂纹尤其贯穿裂纹的扩展趋势,将是预报转轮工作寿命的重要措施,它事关电站的高效、稳定和安全运行。
材料在裂纹产生和扩展过程中,伴随着局部源快速释放能量而产生瞬态弹性波的现象,即声发射(Acoustic Emission,AE)。通过检测此波而发展起来的AE技术目前已有很多的研究和应用。本文对AE技术监测转轮叶片裂纹的可行性进行探讨,主要对AE信号在转轮上的衰减特性、强大背景噪声下弱小裂纹AE信号的提取、AE信号特征参数的提取与叶片上裂纹源的定位,以及疲劳过程中叶片材料的裂纹AE特性进行研究。主要取得了以下成果:
1.研究了转轮上AE信号的衰减特性。根据AE信号在转轮上传输时的主要衰减因素,进行了两个方面的研究:传输距离对信号的衰减和结构界面对信号的衰减,并利用小波包技术对衰减结果进行了分析。采用信号能量和幅值这两个AE参数对信号的衰减性能进行描述,通过两组实验得出信号在转轮上传输6m后,仍然可检测到。距离为影响衰减的主要因素,界面也对信号衰减存在影响,但依赖于构成界面的构件相对尺寸的大小,因而实际应用中传感器的安装位置应选择在构件相对尺寸接近且结构较简单的部位。通过小波包分析得出特征小波包的信息与原信号的AE参数的最大偏差较小,特征小波包能有效反映转轮上AE信号的衰减特性。利用特征小波包代替原信号,可减少现场监测过程中数据保存和传输的压力。
2.强背景噪声下AE信号的提取。针对水轮机组运行环境背景噪声强的特点,为从接收到的信号中滤除噪声而提取出有用信号,分两种情况进行去噪:1)当噪声成分和独立成分总数不大于提取的混合信号数目时,把噪声成分同时看作独立源,利用独立成分分析(ICA)的盲源分离,将有用成分提取出来,再经过(伪)逆变换还原出无噪信号。通过对混合不同强度白高斯噪声的断铅信号和混合水轮机组背景噪声的断铅信号的提取,实验结果表明,此方法基本不受输入信噪比以及信号所处频率范围的影响,均能较好的分离出信号。2)当源信号总数大于混合信号数时,利用基于ICA的稀疏编码收缩(SCS)方法进行去噪。由最大后验估计对独立成分进行提取,采用泛化高斯模型捕获独立成分的概率密度函数,并利用非线性收缩函数进行去噪,实验结果与小波阈值去噪方法进行比较。结果表明,基于ICA的SCS去噪方法能够从水轮机组运行的背景噪声中提取出叶片裂纹信号和断铅信号。虽然去噪效果没第一种情况好,但比第一种方法更适用,且去噪效果高于小波阈值去噪效果。
3.提出了AE特征参数的提取方法以及转轮裂纹源的定位方法。针对转轮结构大而复杂且出现裂纹区域比较集中等特点,采用智能定位方法。为提高定位精度,首先必须对输入的大量相关AE参数进行特征提取。本文分别利用ICA、核主成分分析(KPCA)以及核独立成分分析(KICA)对AE参数进行特征提取。结果表明KICA提取的贡献率和超过90%的9维特征参数包含的信息量最大,且参数间的冗余最小。利用提取的特征参数分别通过小波神经网络和支持向量机这两种智能定位方法进行裂纹源位置的识别。仿真得出SVM对裂纹源的识别结果好于小波网络的结果。因而,在实际利用AE技术对水轮机转轮裂纹进行定位时,采用KICA特征提取降低了数据的维数,既减少在线监测过程中声发射参数存储和传输的压力,同时定位精度也得到了提高,KICA结合SVM是一种较好的定位方法。
4.研究了转轮叶片材料的疲劳AE特性。水轮机叶片绝大多数裂纹为交变载荷作用下的疲劳裂纹,而叶片疲劳裂纹扩展速率是评估水轮机转轮损伤程度的基本数据。因此在实验室环境下,使叶片材料试件在标准三点弯曲疲劳实验下产生裂纹,研究此材料的疲劳裂纹扩展速率及相应的AE特性,并与水轮机组现场背景噪声的AE特征进行比较。研究结果表明,AE参数与疲劳裂纹状态存在一定的对应关系,可用AE参数表征裂纹的状态。得到了疲劳裂纹扩展过程中的AE计数率、能量变化率、裂纹扩展率与应力强度因子的关系,通过这些关系式可预测叶片的疲劳寿命。同时,得出了AE计数率、能量变化率与裂纹扩展率的关系,即可由检测到的AE参数的变化率推算出裂纹扩展率的大小,从而大致评估叶片的安全性,避免了实际结构中应力强度因子测量困难的问题。而且,发现疲劳裂纹信号与水轮机组现场背景噪声的AE参数值相差较大。
Francis turbine is popularly used in power stations. The runner of the turbine bears the important task of transferring the water energy to mechanical energy, which makes cracks appeared easily. Initiation of blade cracks endangers the safety operation of power stations. And frequently shutting down to weld cracks brings a lot of economic loss for power stations. Although many studies have been done at the stage of design and manufacture, the problem of cracks can’t be resolved completely. It will be an important measure of predicting the working life of runners if we can monitor the crack initiation and master the developing trend of cracks, especially the throughout cracks, which is an enormous problem relevant to highly efficient, stationary and safety operation.
Acoustic emission (AE) is a phenomenon of generating transient elastic wave caused by the fast energy release of local source during the initiation and growth of cracks. There are many researches and applications using AE technique to collect the signals of cracks. This paper is a preliminary study of applying AE technique to detecting the large-scale turbine runner. The major investigative contents include: attenuation characteristics of AE signals across the runner, extraction of weak signals buried in big background noise, feature extraction of AE parameters and source location of cracks in blades and the fatigue AE characteristics of the blade material. The following achievements had been obtained:
1. The attenuation characteristics of AE signals propagated across the runner were researched. The attenuation characteristics due to the propagation distance and the existence of a structure interface were studied according to the major affecting factors on AE signals across the runner. At the same time, the attenuation results were analyzed by the wavelet packet technique. Two commonly used AE parameters, energy and maximum amplitude, were used to describe the attenuation performance. From the tests, it is concluded that AE signals are detectable after propagating at a distance of 6 m. The propagation distance is the major factor of attenuation and the interface composed by the same type of material has effects on attenuation, which depends on the relative size of structures. It is a better way to mount sensors on a simple structure that has a possibly equivalent size with the structure incurred AE sources when the sources propagate across the interface. Furthermore, through the wavelet packet analysis, it is concluded that the maximum deviations of the attenuation slopes of the energy and amplitude between the feature packet and raw signal are small, which shows that the attenuation characteristics of the packet and the corresponding raw signal are substantial agreement. The feature packet can reflect the attenuation characteristics of signals propagated across the runner. In light of this, the pressure on data transmission and storage can be decreased by extracting feature packet coefficients.
2. Extraction of AE signals mixed with the strong background noise was studied. The operating noise of turbine units is strong. In order to extract the wanted signal from the received noisy signal, there are two ways to denoise according to the different conditions: 1) When the total number of noise components and independent components is not more than that of the received mixed signals (i.e. the noise component is also regarded as an independent source), the blind source separation of the independent component analysis (ICA) was used to extract the wanted components. Then the free-noise signal was obtained by the (pseudo) inverse transformation of the wanted components. Through extraction for the pressing lead signal mixed with white Gaussian noise of different strengths and the operating noise of the turbine unit, it indicates that this method is not affected by the inputted signal-noise-ratio (SNR) and the frequency range of signals. The wanted signals can be extracted preferably.2) When the number of sources is more than the mixed signals, the denoising method of sparse coding shrinkage (SCS) based on ICA was used. The independent components were estimated by the maximum a posteriori (MAP) estimation and the probability density functions (PDFs) of the independent components were fitted by generalized Gaussian model. Then the nonlinear shrinkage functions were used to denoise. The results were compared with those obtained by the wavelet threshold value denoising method. It shows that the SCS method can extract the crack signal of the blade and the pressing lead signal buried in the operating noise of the turbine unit. Although the results of the SCS method are not better than those of the first method, it is more applicable. Furthermore, its denoising results are better than the wavelet method.
3. The feature extraction of the AE parameters and source location of the runner cracks were studied. Hydraulic turbine runner has a complex structure and the crack regions are centered, so the intelligent location methods were used. In order to improve the accuracy of location, it is necessary to extract the feature parameters of the AE signal. The ICA, kernel principle component analysis (KPCA) and kernel ICA (KICA) were used to extract features. The result shows that the information of the first nine feature parameters with above 90% contribution rate is the largest and that the redundancy between the feature parameters is the least. The wavelet neural network (WNN) and support vector machine (SVM) were used to recognize the crack regions according to the extracted feature parameters, respectively. The simulation shows that recognition rate of SVM is better than that of WNN. As a result, in real world applications, doing feature extraction by KICA can decrease the dimension of input signals, which reduces the pressure on AE parameters transmission and storage and improves the accuracy of location as well. In light of these, it is a good method for source location in complex big-size structures to combine KICA with SVM.
4. The fatigue AE characteristics of the runner blade material were studied. Some researches indicated that most of the regular cracks were fatigue cracks when blades were subjected to vibratory alternate stress. The fatigue crack growth rate of blade is the basic data to evaluate the degree of damage of the turbine runner. The crack signals of the blade material from three-point bending fatigue tests were received, and the fatigue crack growth rate and the corresponding AE characteristics were studied in the laboratory. Furthermore, the characteristics of the cracks were compared with those of background noise received from the locale of a hydraulic turbine unit. The results show that the AE parameters and fatigue cracks have a coincidence relation and that the parameters can represent the state of crack. The correlations of the crack growth rates, AE count rates and AE energy changed rates versus the stress intensity factor (SIF) range were obtained. The fatigue life can be predicted by the correlations. At the same time, the correlations of the crack growth rates versus AE count rates and AE energy changed rates were obtained. As a result, the crack growth rates of can be deduced by the AE parameters changed rates, which avoids the problem of measuring the SIF in real applications. In addition, the ranges of the AE parameters of fatigue crack signals and the background noise onsite are different.
材料在裂纹产生和扩展过程中,伴随着局部源快速释放能量而产生瞬态弹性波的现象,即声发射(Acoustic Emission,AE)。通过检测此波而发展起来的AE技术目前已有很多的研究和应用。本文对AE技术监测转轮叶片裂纹的可行性进行探讨,主要对AE信号在转轮上的衰减特性、强大背景噪声下弱小裂纹AE信号的提取、AE信号特征参数的提取与叶片上裂纹源的定位,以及疲劳过程中叶片材料的裂纹AE特性进行研究。主要取得了以下成果:
1.研究了转轮上AE信号的衰减特性。根据AE信号在转轮上传输时的主要衰减因素,进行了两个方面的研究:传输距离对信号的衰减和结构界面对信号的衰减,并利用小波包技术对衰减结果进行了分析。采用信号能量和幅值这两个AE参数对信号的衰减性能进行描述,通过两组实验得出信号在转轮上传输6m后,仍然可检测到。距离为影响衰减的主要因素,界面也对信号衰减存在影响,但依赖于构成界面的构件相对尺寸的大小,因而实际应用中传感器的安装位置应选择在构件相对尺寸接近且结构较简单的部位。通过小波包分析得出特征小波包的信息与原信号的AE参数的最大偏差较小,特征小波包能有效反映转轮上AE信号的衰减特性。利用特征小波包代替原信号,可减少现场监测过程中数据保存和传输的压力。
2.强背景噪声下AE信号的提取。针对水轮机组运行环境背景噪声强的特点,为从接收到的信号中滤除噪声而提取出有用信号,分两种情况进行去噪:1)当噪声成分和独立成分总数不大于提取的混合信号数目时,把噪声成分同时看作独立源,利用独立成分分析(ICA)的盲源分离,将有用成分提取出来,再经过(伪)逆变换还原出无噪信号。通过对混合不同强度白高斯噪声的断铅信号和混合水轮机组背景噪声的断铅信号的提取,实验结果表明,此方法基本不受输入信噪比以及信号所处频率范围的影响,均能较好的分离出信号。2)当源信号总数大于混合信号数时,利用基于ICA的稀疏编码收缩(SCS)方法进行去噪。由最大后验估计对独立成分进行提取,采用泛化高斯模型捕获独立成分的概率密度函数,并利用非线性收缩函数进行去噪,实验结果与小波阈值去噪方法进行比较。结果表明,基于ICA的SCS去噪方法能够从水轮机组运行的背景噪声中提取出叶片裂纹信号和断铅信号。虽然去噪效果没第一种情况好,但比第一种方法更适用,且去噪效果高于小波阈值去噪效果。
3.提出了AE特征参数的提取方法以及转轮裂纹源的定位方法。针对转轮结构大而复杂且出现裂纹区域比较集中等特点,采用智能定位方法。为提高定位精度,首先必须对输入的大量相关AE参数进行特征提取。本文分别利用ICA、核主成分分析(KPCA)以及核独立成分分析(KICA)对AE参数进行特征提取。结果表明KICA提取的贡献率和超过90%的9维特征参数包含的信息量最大,且参数间的冗余最小。利用提取的特征参数分别通过小波神经网络和支持向量机这两种智能定位方法进行裂纹源位置的识别。仿真得出SVM对裂纹源的识别结果好于小波网络的结果。因而,在实际利用AE技术对水轮机转轮裂纹进行定位时,采用KICA特征提取降低了数据的维数,既减少在线监测过程中声发射参数存储和传输的压力,同时定位精度也得到了提高,KICA结合SVM是一种较好的定位方法。
4.研究了转轮叶片材料的疲劳AE特性。水轮机叶片绝大多数裂纹为交变载荷作用下的疲劳裂纹,而叶片疲劳裂纹扩展速率是评估水轮机转轮损伤程度的基本数据。因此在实验室环境下,使叶片材料试件在标准三点弯曲疲劳实验下产生裂纹,研究此材料的疲劳裂纹扩展速率及相应的AE特性,并与水轮机组现场背景噪声的AE特征进行比较。研究结果表明,AE参数与疲劳裂纹状态存在一定的对应关系,可用AE参数表征裂纹的状态。得到了疲劳裂纹扩展过程中的AE计数率、能量变化率、裂纹扩展率与应力强度因子的关系,通过这些关系式可预测叶片的疲劳寿命。同时,得出了AE计数率、能量变化率与裂纹扩展率的关系,即可由检测到的AE参数的变化率推算出裂纹扩展率的大小,从而大致评估叶片的安全性,避免了实际结构中应力强度因子测量困难的问题。而且,发现疲劳裂纹信号与水轮机组现场背景噪声的AE参数值相差较大。
Francis turbine is popularly used in power stations. The runner of the turbine bears the important task of transferring the water energy to mechanical energy, which makes cracks appeared easily. Initiation of blade cracks endangers the safety operation of power stations. And frequently shutting down to weld cracks brings a lot of economic loss for power stations. Although many studies have been done at the stage of design and manufacture, the problem of cracks can’t be resolved completely. It will be an important measure of predicting the working life of runners if we can monitor the crack initiation and master the developing trend of cracks, especially the throughout cracks, which is an enormous problem relevant to highly efficient, stationary and safety operation.
Acoustic emission (AE) is a phenomenon of generating transient elastic wave caused by the fast energy release of local source during the initiation and growth of cracks. There are many researches and applications using AE technique to collect the signals of cracks. This paper is a preliminary study of applying AE technique to detecting the large-scale turbine runner. The major investigative contents include: attenuation characteristics of AE signals across the runner, extraction of weak signals buried in big background noise, feature extraction of AE parameters and source location of cracks in blades and the fatigue AE characteristics of the blade material. The following achievements had been obtained:
1. The attenuation characteristics of AE signals propagated across the runner were researched. The attenuation characteristics due to the propagation distance and the existence of a structure interface were studied according to the major affecting factors on AE signals across the runner. At the same time, the attenuation results were analyzed by the wavelet packet technique. Two commonly used AE parameters, energy and maximum amplitude, were used to describe the attenuation performance. From the tests, it is concluded that AE signals are detectable after propagating at a distance of 6 m. The propagation distance is the major factor of attenuation and the interface composed by the same type of material has effects on attenuation, which depends on the relative size of structures. It is a better way to mount sensors on a simple structure that has a possibly equivalent size with the structure incurred AE sources when the sources propagate across the interface. Furthermore, through the wavelet packet analysis, it is concluded that the maximum deviations of the attenuation slopes of the energy and amplitude between the feature packet and raw signal are small, which shows that the attenuation characteristics of the packet and the corresponding raw signal are substantial agreement. The feature packet can reflect the attenuation characteristics of signals propagated across the runner. In light of this, the pressure on data transmission and storage can be decreased by extracting feature packet coefficients.
2. Extraction of AE signals mixed with the strong background noise was studied. The operating noise of turbine units is strong. In order to extract the wanted signal from the received noisy signal, there are two ways to denoise according to the different conditions: 1) When the total number of noise components and independent components is not more than that of the received mixed signals (i.e. the noise component is also regarded as an independent source), the blind source separation of the independent component analysis (ICA) was used to extract the wanted components. Then the free-noise signal was obtained by the (pseudo) inverse transformation of the wanted components. Through extraction for the pressing lead signal mixed with white Gaussian noise of different strengths and the operating noise of the turbine unit, it indicates that this method is not affected by the inputted signal-noise-ratio (SNR) and the frequency range of signals. The wanted signals can be extracted preferably.2) When the number of sources is more than the mixed signals, the denoising method of sparse coding shrinkage (SCS) based on ICA was used. The independent components were estimated by the maximum a posteriori (MAP) estimation and the probability density functions (PDFs) of the independent components were fitted by generalized Gaussian model. Then the nonlinear shrinkage functions were used to denoise. The results were compared with those obtained by the wavelet threshold value denoising method. It shows that the SCS method can extract the crack signal of the blade and the pressing lead signal buried in the operating noise of the turbine unit. Although the results of the SCS method are not better than those of the first method, it is more applicable. Furthermore, its denoising results are better than the wavelet method.
3. The feature extraction of the AE parameters and source location of the runner cracks were studied. Hydraulic turbine runner has a complex structure and the crack regions are centered, so the intelligent location methods were used. In order to improve the accuracy of location, it is necessary to extract the feature parameters of the AE signal. The ICA, kernel principle component analysis (KPCA) and kernel ICA (KICA) were used to extract features. The result shows that the information of the first nine feature parameters with above 90% contribution rate is the largest and that the redundancy between the feature parameters is the least. The wavelet neural network (WNN) and support vector machine (SVM) were used to recognize the crack regions according to the extracted feature parameters, respectively. The simulation shows that recognition rate of SVM is better than that of WNN. As a result, in real world applications, doing feature extraction by KICA can decrease the dimension of input signals, which reduces the pressure on AE parameters transmission and storage and improves the accuracy of location as well. In light of these, it is a good method for source location in complex big-size structures to combine KICA with SVM.
4. The fatigue AE characteristics of the runner blade material were studied. Some researches indicated that most of the regular cracks were fatigue cracks when blades were subjected to vibratory alternate stress. The fatigue crack growth rate of blade is the basic data to evaluate the degree of damage of the turbine runner. The crack signals of the blade material from three-point bending fatigue tests were received, and the fatigue crack growth rate and the corresponding AE characteristics were studied in the laboratory. Furthermore, the characteristics of the cracks were compared with those of background noise received from the locale of a hydraulic turbine unit. The results show that the AE parameters and fatigue cracks have a coincidence relation and that the parameters can represent the state of crack. The correlations of the crack growth rates, AE count rates and AE energy changed rates versus the stress intensity factor (SIF) range were obtained. The fatigue life can be predicted by the correlations. At the same time, the correlations of the crack growth rates versus AE count rates and AE energy changed rates were obtained. As a result, the crack growth rates of can be deduced by the AE parameters changed rates, which avoids the problem of measuring the SIF in real applications. In addition, the ranges of the AE parameters of fatigue crack signals and the background noise onsite are different.
引文
[1]顾四行,水轮机转轮裂纹及其处理,水电站机电技术,1992,(3):1-7.
[2] Grein, H.,混流式水轮机振动的成因及预防,东方电机,2001,(2):1-7+16.
[3]李光海,声发射源识别技术的研究,[博士学位论文],广州:华南理工大学,2002.
[4] Huth, H.J., Fatigue Design of Hydraulic Turbine Runners, [Dissertation], Trondheim:Norwegian University of Science and Technology,2005.
[5]曹剑绵,陈昌林,大型水轮机转轮异常振动及叶片裂纹分析,西南交通大学学报(增刊),2002,37(11):68-72.
[6]覃大清,刘光宁,陶星明,混流式水轮机转轮叶片裂纹问题,大电机技术,2005,(4):39-44.
[7]孙春方,混流式水轮机转轮裂纹扩展探讨,动力工程,2002,22(3):1827-1831.
[8]陈香林,混流式水轮机叶片流固耦合动力特性研究,[硕士学位论文],昆明:昆明理工大学,2004.12.
[9]邬海军,水轮机叶片三维有限元刚强度与振动特性研究,[硕士学位论文],西安:西安理工大学,2005.
[10]马世纪,基于小波分析的水轮机故障诊断研究,[硕士学位论文],武汉:武汉大学,2004.
[11] Betting, B.,Han, R.P.S., Predictive maintenance using the rotordynamic model of a hydraulic turbine-generator rotor, Journal of Vibration and Acoustics,1998,120(2):441-448.
[12] Mack, R.,Drtina, P.,Lang, E., Numerical prediction of erosion on guide vanes and in labyrinth seals in hydraulic turbines, Wear,1999,233-235:685-691.
[13] Liu, C.,Iung, B.,Ye, L.,et al.,An innovative hydraulic turbine governor for predictive maintenance of hydropower plant,in Proceedings of the 41st IEEEConference on Decision and Control, Las Vegas, Nevada, 2002:925-930.
[14]徐东海,水电机组状态监测及故障诊断研究,[硕士学位论文],北京:清华大学,2003.
[15]魏然,水轮发电机组状态监测与故障诊断系统研究,[硕士学位论文],西安:西安理工大学,2001.
[16]李成家,张双平,康振国等,水轮机转轮裂纹的检测分析与研究,西北电力技术,2001,(6):21-23+66.
[17]赵奇,李二伟,薛世钦等,进口水轮机转轮叶片的裂纹分析及处理,检验检疫科学,2003,13(4):4-6.
[18]陈凯,陆军,何万成,红石电站水轮机转轮叶片裂纹的分析及处理,水力发电,2003,29(1):54-56.
[19]罗永要,王正伟,梁权伟,混流式水轮机转轮动载荷作用下的应力特性,清华大学学报(自然科学版),2005,45(2):235-237+257.
[20]姬书得,方洪渊,刘雪松,混流式水轮机转轮简化模型焊接应力场的数值模拟,大电机技术,2004,(6):55-57+71.
[21]黄文,吴彤峰,毛汉领,水轮机转轮叶片的应力分布及其裂纹成因,实用测试技术,1999,(3):33-34.
[22] Carpinteri, A.,Brighenti, R.,Huth, H.-J.,et al., Fatigue growth of a surface crack in a welded T-joint, International Journal of Fatigue,2005,27(1):59-69.
[23] Masse, B.,Page, M.,Magnan, R.,et al., Numerical simulations: a tool to improve performance of hydraulic turbines, in ASCE Conference Proceeding on Waterpower. 1999, ASCE: Las Vegas. p. 1-10.
[24] Escaler, X.,Egusquiza, E.,Farhat, M.,et al., Detection of cavitation in hydraulic turbines, Mechanical Systems and Signal Processing,2006,20(4):983-1007.
[25]沈功田,戴光,刘时风,中国声发射检测技术进展,无损检测,2003,25(6):302-307.
[26] Miller, R.K.,Mclntire, P., Society for Nondestructive Testing. Acoustic Emission Testing, American Society for Nondestructive Testing.1987
[27] PAC PolyMODALTM Wave Toolbox for MISTRAS-2001, Physics Acoustic Corporation.
[28]耿荣生,声发射技术发展现状——学会成立20周年回顾,无损检测,1998,20(6):151-154.
[29]全国第八届声发射会议(论文集). 1999,中国无损检测学会声发射专业委员会:上海.
[30]全国第九届声发射会议(论文集). 2001,中国无损检测学会声发射专业委员会:成都.
[31] Gong, Z.,DuQuesnay, D.L.,McBride, S.L., Measurement and Interpretation of Fatigue Crack Growth in 7075 Aluminum Alloy Using Acoustic Emission Monitoring, Journal of Testing and Evaluation,1998,26(6):567-574.
[32] Berkovits, A.,Fang, D., Study of fatigue crack characteristics by acoustic emission, Engineering Fracture Mechanics,1995,51(3):401-409.
[33] Daniel, I. M., Luo, J. J., Sifniotopoulos, C. G., et al., Acoustic emission monitoring of fatigue damage in metals, Nondestructive Testing and Evaluation, 1998,14(1-2):71-87.
[34] Talebzadeh, M.,Roberts, T.M., Correlation of crack propagation and acoustic emission rates, Key Engineering Materials,2001,(204-205):341-350.
[35] Roberts, T.M.,Davies, A.W., Structural integrity of welded steel structures, Key engineering materials,1999,(167-168):142-151.
[36] Roberts, T.M.,Talebzadeh, M., Fatigue life prediction based on crack propagation and acoustic emission count rates, Journal of constructional steel research,2003,59(6):679-694.
[37] Roberts, T.M.,Talebzadeh, M., Acoustic emission monitoring of fatigue crackpropagation, Journal of Constructional Steel Research,2003,59(6):695-712.
[38]李耀东,黄成祥,侯力,模态声发射技术在构件疲劳裂纹检测中的应用,振动与冲击,2005,24(2):122-126.
[39]李光海,刘正义,基于声发射技术的金属高频疲劳监测,中国机械工程,2004,15(13):1205-1209.
[40] Biancolini, M.E.,Brutti, C.,Paparo, G.,et al., Fatigue cracks nucleation on steel, acoustic emission and fractal analysis, International Journal of Fatigue,2006,28(12):1820-1825.
[41]耿荣生,利用模态声发射技术监测飞机主结构的日历损伤,机械工程学报,2003,39(9):128-132.
[42] Shiwa, M.,Carpenter, S.,Kishi, T., Analysis of acoustic emission signals generated during the fatigue testing of GFRP, Journal of composite materials,1996,30(18):2019-2041.
[43] Tsamtsakis, D.,Wevers, M.,Meester, P.D., Acoustic emission from CFRP laminates during fatigue loading, Journal of reinforced plastics and composites,1998,17(13):1185-1201.
[44] Mizutani, Y. , Nagashima, K. , Takemoto, M. , et al., Fracture mechanism characterization of cross-ply carbon-fiber composites using acoustic emission analysis, NDT & E International,2000,33(2):101-110.
[45] Surgeon, M.,Wevers, M., Modal analysis of acoustic emission signals from CFRP laminates, NDT & E International,1999,32(6):311-322.
[46]郑金海,王彬,骆英,基于声发射技术的预应力钢筋混凝土梁破坏特性试验研究,西部探矿工程,2006,(9):224-226.
[47] Rozanov, A.O.,Savel, e.V.N.,Stanchits, S.A., Spectral analysis of pulse-induced acoustic field in rocks and its applications, CT theory and applications,1999,(8):47-51.
[48]杨明纬,声发射检测,北京:机械工业出版社.2005
[49] Ennaceur, C.,Laksimi, A.,Herve, C.,et al., Monitoring crack growth in pressure vessel steels by the acoustic emission technique and the method of potential difference, International Journal of Pressure Vessels and Piping,2006,83(3):197-204.
[50]沈功田,金属压力容器的声发射源特性及识别方法的研究,[博士学位论文],北京:清华大学,1998.
[51]刘孝敏,Holford, K.M.,钢板桁架疲劳裂纹生长模型和声发射特征的研究,中国科学技术大学学报,1998,28(3):326-330.
[52]耿荣生,景鹏,雷洪,飞机主梁疲劳裂纹萌生声发射信号的识别方法,航空学报,1996,17(3):368-372.
[53] Ravindra, H.V., Some aspects of acoustic emission signal processing, Journal of Materials Processing Technology,2001,109(3):242-247.
[54] Haber, R.E.,Jimenez, J.E.,Peres, C.R.,et al., An investigation of tool-wear monitoring in a high-speed machining process, Sensors and Actuators A: Physical,2004,116(3):539-545.
[55]王永涛,韩建,牟海维,基于声谱分析的阀门内泄漏检测系统,大庆石油学院学报,2006,30(3):72-73.
[56] Gupta, N.,Ramu, T.S., Estimation of partial discharge parameters in GIS using acoustic emission techniques, Journal of Sound and Vibration,2001,247(2):243-260.
[57] Johan Singh, P.,Mukhopadhyay, C.K.,Jayakumar, T.,et al., Understanding fatigue crack propagation in AISI 316 (N) weld using Elber's crack closure concept: Experimental results from GCMOD and acoustic emission techniques, International Journal of Fatigue,2007,29(12):2170-2179.
[58] Yue, Y.L.,Wei, P.Y.,Li, S.H.,et al., Acoustic emission signature of fatigue cracksat Titanium alloy welded joints, Journal of Ship Mechanics,2008,12(3):429-439.
[59] GB/T18182-2000金属压力容器声发射检测及结果评价方法,中国标准出版社出版第一版.北京,2000.
[60] Biancolini, M.E.,Brutti, C.,Paparo, G.,et al., Fatigue cracks nucleation on steel, acoustic emission and fractal analysis, International Journal of Fatigue,2006,28(12):1820.
[61]张平,集成化声发射信号处理平台的研究[博士学位论文],北京:清华大学, 2002.
[62]陈玉华,刘时风,耿荣生等,声发射信号的谱分析和相关分析,无损检测,2002,24(9):395-399.
[63] Merson, D.,Razuvaev, A.,Vinogradov, A., Application of the spectral analysis of acoustic emission signals to studies of vulnerability of TiN coatings on steel substrates, Russian journal of nondestructive testing,2002,38(7):508-516.
[64]杨占才,声发射事件谱分析技术在滚动轴承故障诊断中的应用,设备管理与维修,2000,(11):27-28.
[65] Surgeon, M.,Wevers, M., One sensor linear location of acoustic emission events using plate wave theories, Materials Science and Engineering A,1999,265(1-2):254-261.
[66]耿荣生,沈功田,刘时风,模态声发射基本理论,无损检测,2002,24(7):302-306.
[67] Jiao, J.,He, C.,Wu, B.,et al., Application of wavelet transform on modal acoustic emission source location in thin plates with one sensor, International Journal of Pressure Vessels and Piping,2004,81(5):427-431.
[68] Pullin, R.,Holford, K.M.,Baxter, M.G., Modal analysis of acoustic emission signals from artificial and fatigue crack sources in aerospace grade steel, key engineering materials,2005,293-294:217-224.
[69] Terchi, L., Time-frequency analysis of acoustic emission, Engineering Fracture Mechanics,2002,69:717-728.
[70] Suzuki, H.,Kinjo, T.,Hayash, Y., Wavelet tranform of acoustic emission signals, Journal of Acoustic Emission,1996,14(2):69-84.
[71]李录平,邹新元,唐月清,小波变换在声发射信号特征参数检测中的应用,振动与冲击,2001,20(2):67-68+81.
[72] Yang, L.,Zhou, Y.C., Wavelet analysis of acoustic emission signals from thermal barrier coatings, Transactions of Nonferrous Metals Society of China,2006,16:s270-s275.
[73]陈春朝,李孟源,王恒迪等,铁路货车轴承的声发射故障诊断及分析,轴承,2006,(1):36-38.
[74]王清明,董申,李小俚,基于小波分析的钻头破损检测,哈尔滨工业大学学报,2000,32(6):76-79.
[75] Li, X.,Dong, S.,Yuan, Z., Discrete wavelet transform for tool breakage monitoring, International Journal of Machine Tools and Manufacture,1999,39(12):1935-1944.
[76] Warren Liao, T.,Ting, C.-F.,Qu, J.,et al., A wavelet-based methodology for grinding wheel condition monitoring, International Journal of Machine Tools and Manufacture,2007,47(3-4):580-592.
[77]马建仓,吴启彬,罗磊,小波包分析在旋转机械冲击故障诊断中的应用研究,机械科学与技术,1998,17(2):310-312.
[78] Qi, G., Wavelet-based AE characterization of composite materials, NDT & E International,2000,33(3):133-144.
[79] Staszewski, W.J.,Holford, K.M., Wavelet signal processing of acoustic emission data, Key engineering materials,2001,204-205:351-358.
[80] Loutas, T.H.,Kostopoulos, V.,Ramirez-Jimenez, C.,et al., Damage evolution in center-holed glass/polyester composites under quasi-static loading using time/frequency analysis of acoustic emission monitored waveforms, Composites Science and Technology,2006,66(10):1366-1375.
[81]张承彪,罗运柏,文习山,主成分分析在变压器故障诊断中的应用研究,高电压技术,2005,31(8):9-11.
[82]苑宇,马孝江,基于主分量分析的柴油机振动信号特征提取,中国机械工程,2007,18(8):971-975.
[83]杨世锡,焦卫东,吴昭同,独立分量分析基网络应用于旋转机械故障特征提取与分类,机械工程学报,2004,40(3):45-49.
[84] Ozawa, S.,Sakaguchi, Y.,Kotani, M.,A study of feature extraction using supervised independent component analysis,in International Joint Conference on Neural Networks, Washington, DC, 2001:2958-2963.
[85] Martiriggiano, T.,Leo, M.,Spagnolo, P.,et al.,Facial feature extraction by kernel independent component analysis,in IEEE Conference on Advanced Video and Signal Based Surveillance, Como, Italy, 2005:270-275.
[86] Widodo, A.,Yang, B.-S., Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors, Expert Systems with Applications,2007,33(1):241-250.
[87] Jade, A.M.,Srikanth, B.,Jayaraman, V.K.,et al., Feature extraction and denoising using kernel PCA, Chemical Engineering Science,2003,58(19):4441-4448.
[88]刘爱伦,袁小艳,俞金寿,基于KPCA-SVC的复杂过程故障诊断,仪器仪表学报,2007,28(5):870-874.
[89]郭辉,刘贺平,王玲, KPCA-LSSVM建模方法及在钢材淬透性中的应用研究,控制与决策,2006,21(9):1073-1076.
[90] Wang, G.,Sun, Y.-a.,Ding, Q.,et al., Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression, Analytica Chimica Acta,2007,594(1):101-106.
[91]耿荣生,沈功田,刘时风,声发射信号处理和分析技术,无损检测,2002,24(1):23-28.
[92]顾江,张光新,刘国华等,基于独立分量分析的声发射信号去噪方法,江南大学学报(自然科学版),2008,7(1):55-59.
[93] Iyer, D.,Zouridakis, G., Single-trial evoked potential estimation: Comparison between independent component analysis and wavelet denoising, Clinical Neurophysiology,2007,118(3):495-504.
[94] Li, Z.,He, Z.,Zi, Y.,et al., Customized wavelet denoising using intra- and inter-scale dependency for bearing fault detection, Journal of Sound and Vibration,2008,313(1-2):342-359.
[95]张全明,刘会金,周新启等,电能质量信号的小波软阈值去噪方法,高电压技术,2006,32(1):99-101.
[96]王成江,舒乃秋,放电声发射波的小波提纯与消噪,高压电器,2003,39(6):8-10.
[97] Li, W.,Gu, F.,Ball, A.D.,et al., A study of the noise from diesel engines using the independent component analysis, Mechanical Systems and Signal Processing,2001,15(6):1165-1184.
[98]夏文静,傅行军,基于ICA在强背景噪声振动信号中的去噪研究,汽轮机技术,2006,48(2):121-123,155.
[99]季忠,金涛,杨炯明等,虚拟噪声通道在基于ICA消噪过程中的应用,中国机械工程,2005,16(4):350-352,364.
[100]Li, Z.,He, Y.,Chu, F., Application of the blind source separation in machine fault diagnosis: A review and prospect, Mechanical Systems and Signal Processing,In Press, Corrected Proof
[101]Hyvarinen, A., Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation, Neural Computation,1999,11(7):1739-1768.
[102]孔薇,杨杰,周越,基于独立成分分析的强背景噪声去噪方法,上海交通大学学报,2004,38(12):1957-1961.
[103]He, Q.B.,Feng, Z.H.,Kong, F.R., Detection of signal transients using independent component analysis and its application in gearbox condition monitoring, Mechanical Systems and Signal Processing,2007,21(5):2056-2071.
[104]Ju, L.,Caihua, Z.,Xin, Z.,et al.,An Approach of Speech Enhancement by Sparse Code Shrinkage,in International Conference on Neural Networks and Brain, Beijing, China, 2005:1952-1956.
[105]沈功田,耿荣生,刘时风,声发射源定位技术,无损检测,2002,24(3):114-117,125.
[106]金钟山,刘时风,耿荣生等,曲面和三维结构的声发射源定位方法,无损检测,2002,24(5):205-211.
[107]Lympertos, E.M.,Dermatas, E.S., Acoustic emission source location in dispersive media, Signal Processing,2007,87(12):3218-3225.
[108]Axinte, D.A.,Natarajan, D.R.,Gindy, N.N.Z., An approach to use an array of three acoustic emission sensors to locate uneven events in machining--Part 1: method and validation, International Journal of Machine Tools and Manufacture,2005,45(14):1605-1613.
[109]Ding, Y.,Reuben, R.L.,Steel, J.A., A new method for waveform analysis for estimating AE wave arrival times using wavelet decomposition, NDT & E International,2004,37(4):279-290.
[110]Cui, Y.,Wang, K.,Chen, W.,et al.,Application of wavelet packet transform to acoustic emission source location in thin plates,in 16th World Conference on NDT, Montreal, Canada,2004:233-248.
[111]Yang, M.,Manabe, K.,Hayashi, K.,et al., Data fusion of distributed AE sensors for the detection of friction sources during press forming, Journal of Materials Processing Technology,2003,139(1-3):368-372.
[112]Nivesrangsan, P.,Steel, J.A.,Reuben, R.L., Source location of acoustic emission indiesel engines, Mechanical Systems and Signal Processing,2007,21(2):1103-1114.
[113]龚斌,金志浩,包日东等,压力容器上声发射源的新型定位方法,压力容器,2005,22(6):13,14,54.
[114]Grabec, I.,Kosel, T.,Muzic, P., Location of continuous AE sources by sensory neural networks, Ultrasonics,1998,36(1-5):525-530.
[115]Kirikera, G.R.,Shinde, V.,Schulz, M.J.,et al., Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions, Mechanical Systems and Signal Processing,2007,21(1):280-297.
[116]Spall, J.C.,Maryak, J.L.,Asher, M.S., Neural network approach to locating acoustic emission sources in non-destructive evaluation, Journal of Sound and Vibration,1998,211(1):133-143.
[117]Tian, Y.,Lewin, P.L.,Davies, A.E.,et al.,PD pattern identification using acoustic emission measurement and neural networks,in Eleventh International Symposium on High Voltage Engineering, London, UK, 1999:41-44.
[118]来兴平,张海燕,刘叶玲等,支持向量机在岩石破裂失稳声发射定位实验中的应用,金属矿山,2006,(12):61-63,82.
[119]Du, B.X.,Lu, Y.H.,Wei, G.Z.,et al.,PD localization based on fuzzy theory using AE detection techniques,in 2005 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Nashville, TN, USA, 2005:449-452.
[120]Gaul, L.,Hurlebaus, S., Identification of the impact location on a plate using wavelets, Mechanical Systems and Signal Processing,1998,12(6):783-795.
[121]龚仁荣,顾建祖,骆英等,基于Lamb波频散特性的声发射源平面定位新方法,无损检测,2006,28(10):521-525.
[122]Hyunjo, J.,Young-Su, J., Fracture source location in thin plates using the wavelet transform of dispersive waves, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control,2000,47(3):612-619.
[123]龚仁荣,程志勤,顾建祖,模态声发射在结构材料缺陷定位中的研究,振动与冲击,2006,25(3):176-179+192.
[124]Kosel, T.,Grabec, I.,Kosel, F., Time-delay estimation of acoustic emission signals using ICA, Ultrasonics,2002,40(1-8):303-306.
[125]Intelligent location of two simultaneously active acoustic emission sources, Aerospace Science and Technology,2005,9(1):45-53.
[126]沈功田,耿荣生,刘时风,连续声发射信号的源定位技术,无损检测,2002,24(4):164-167.
[127]Nivesrangsan, P.,Steel, J.A.,Reuben, R.L., AE mapping of engines for spatially located time series, Mechanical Systems and Signal Processing,2005,19(5):1034-1054.
[128]Mba, D.,Hall, L.D., The transmission of acoustic emission across large-scale turbine rotors, NDT & E International,2002,35(8):529-539.
[129]Gang, Q., Attenuation of acoustic emission body waves in acrylic bone cement and synthetic bone using wavelet time-scale analysis, Journal of Biomedicine Material Research,2000,52(1):148-156.
[130]Jeong, H., Analysis of plate wave propagation in anisotropic laminates using a wavelet transform, NDT & E International,2001,34(3):185-190.
[131]Dalton, R.P.,Cawley, P.,Lowe, M.J., Propagation of acoustic emission signals in metallic fuselage structure, Iee Proceedings-Science Measurement and Technology,2001,148(4):169-177.
[132]ASTM E976-99: standard guild for determining the reproducibility of acoustic emission sensor response,1999:395-403.
[133]Wu, J.-D.,Chen, J.-C., Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT & E International,2006,39(4):304-311.
[134]Li, X.,Wu, J., Wavelet analysis of acoustic emission signals in boring, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture,2000,214(5):421-424.
[135]Velayudham, A.,Krishnamurthy, R.,Soundarapandian, T., Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform, Materials Science and Engineering: A,2005,412(1-2):141-145.
[136]Mallat, S., A wavelet tour of signal processing, Massachusetts:Academic Press.1999
[137]Wannucci, M.,Corradi, F., Covariance structure of wavelet coefficients: theory and models in Bayesian perspective, Journal of the Royal Stastistical Society Series B,1999,61(4):971-986.
[138]Giaouris, D.,Finch, J.W., Denoising using wavelets on electric drive applications, Electric Power Systems Research,2008,78(4):559-565.
[139]Hyvarinen, A. , Karhumen, J. , Oja, E., Independent component analysis,A Wiley-Interscience Publication.2001
[140]Hyvarinen, A., Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks,1999,10(3):626-634.
[141]孔薇,盲分离算法的研究及其在声信号处理中的应用,[博士学位论文],上海:上海交通大学,2005.
[142]孔薇,杨杰,周越, ICA特征提取技术在背景噪声建模与分析中的应用,上海交通大学学报,2006,40(3):387-390.
[143]Xin, Z.,Jancovic, P.,Ju, L.,et al., Speech Signal Enhancement Based on MAP Algorithm in the ICA Space, ,IEEE Transactions on Signal Processing,2008,56(5):1812-1820.
[144]Hyvarinen, A.,Hoyer, P.,Oja, E.,Denoising of non gaussian data by Independent Component Analysis and sparse coding,in IEEE workshop on independentcomponent analysis and blind source separation, Aussois, France, 1999:495-489.
[145]Lee, T.-W.,Lewicki, M.S.,The generalized gaussian mixture model using ICA,in Proceeding International workshop ICA, Helsinki, Finland, 2000:239-244.
[146]Kokkinakis, K.,Nandi, A.K., Exponent parameter estimation for generalized Gaussian probability density functions with application to speech modeling, Signal Processing,2005,85(9):1852-1858.
[147]Mallat, S.,Hwang, W.L., Singularity detection and processing with wavelets, IEEE Transactions on Information Theory,1992,38(2):617-643.
[148]Xu, Y.,Weaver, J.B.,Healy, D.M.,et al., Wavelet transform domain filters: a spatially selective noisefiltration technique, IEEE Transactions on Image Processing,1994,3(6):747-758.
[149]Donoho, D.L., De-noising by soft-thresholding, IEEE Transactions on Information Theory,1995,41(3):613-627.
[150]Donoho, D.L.,Johnstone, I.M.,Kerkyacharian, G.,et al., Wavelet shrinkage: asymptopia, Journal -Royal Statistical Society Series B,1995,57(2):301-369.
[151]张吉先,钟秋海,戴亚平,小波门限消噪法应用中分解层数及阈值的确定,中国电机工程学报,2004,24(2):118-122.
[152]Pasti, L.,Walczak, B.,Massart, D.L.,et al., Optimization of signal denoising in discrete wavelet transform, Chemometrics and Intelligent Laboratory Systems,1999,48(1):21-34.
[153]Hernández, L.,Juliá, J.E.,Chiva, S.,et al., Fast classification of two-phase flow regimes based on conductivity signals and artificial neural networks, Measurement Science and Technology,2006,17(6):1511-1521.
[154]Marashdeh, Q.,Warsito, W.,Fan, L.-S.,et al., A nonlinear image reconstruction technique for ECT using a combined neural network approach, Measurement Science and Technology,2006,17(8):2097-2103.
[155] Wang, Z.,Willett, P.,DeAguiar, P.R.,et al., Neural network detection of grinding burn from acoustic emission, International Journal of Machine Tools and Manufacture,2001,41(2):283-309.
[156]Moller, M.F., A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks,1993, 6:525-533.
[157]Sebald, D.J.,Sebald, D.J.,Bucklew, J.A., Support vector machines and the multiple hypothesis test problem, IEEE Transactions on Signal Processing,2001,49(11):2865-2872.
[158]Platt, J.C.,Cristianini, N.,Shawe-Taylor, J., Large margin DAGs for multiclass classification, Advances in Neural Information Processing Systems,2000,12:547-553.
[159]Vapnic, V., Statistical Learning Theory,New York:Wiley.1998
[160]攀世英,混流式水轮机转轮裂纹原因分析及预防措施,水力发电,2002,(5):38-41.
[161]Fisher, D.,The cause of runner crack and the solutions implement for the Xiaolangdi Hydroelectric project,in Proceedings of the XXIst IAHR symposium on Hydraulic machinery and systems, Lausanne, 2002
[162]ASTM E647-05: Standard Test Method for Measurement of Fatigue Crack Growth Rates, in Annual Book of ASTM Standard. 2005.
[163]GB/T 6398-2000,金属材料疲劳裂纹扩展速率实验方法,2000.
[164]Gong, Z.,Nyborg, E.O.,Oommen, G., Acoustic emission monitoring of steel railroad bridges, Materials Evaluation,1992,50(7):883-887.
[165]Roques, A.,Browne, M.,Thompson, J.,et al., Investigation of fatigue crack growth in acrylic bone cement using the acoustic emission technique, Biomaterials,2004,25(5):769-778.
[166]余圣甫,王铁琦,杨其良等, ZG20MnSi钢断裂韧度和疲劳裂纹,机械工程材料,2005,29(6):17-19.
[167]Moorthy, V.,Jayakumar, T.,Raj, B., Influence of micro structure on acoustic emission behavior during stage 2 fatigue crack growth in solution annealed, thermally aged and weld specimens of AISI type 316 stainless steel, Materials Science and Engineering A,1996,212(2):273-280.
[168]Lademo, O.G.,Hopperstad, O.S.,Langseth, M., An evaluation of yield criteria and flow rules for aluminium alloys, International Journal of Plasticity,1999,15(2):191-208.
[2] Grein, H.,混流式水轮机振动的成因及预防,东方电机,2001,(2):1-7+16.
[3]李光海,声发射源识别技术的研究,[博士学位论文],广州:华南理工大学,2002.
[4] Huth, H.J., Fatigue Design of Hydraulic Turbine Runners, [Dissertation], Trondheim:Norwegian University of Science and Technology,2005.
[5]曹剑绵,陈昌林,大型水轮机转轮异常振动及叶片裂纹分析,西南交通大学学报(增刊),2002,37(11):68-72.
[6]覃大清,刘光宁,陶星明,混流式水轮机转轮叶片裂纹问题,大电机技术,2005,(4):39-44.
[7]孙春方,混流式水轮机转轮裂纹扩展探讨,动力工程,2002,22(3):1827-1831.
[8]陈香林,混流式水轮机叶片流固耦合动力特性研究,[硕士学位论文],昆明:昆明理工大学,2004.12.
[9]邬海军,水轮机叶片三维有限元刚强度与振动特性研究,[硕士学位论文],西安:西安理工大学,2005.
[10]马世纪,基于小波分析的水轮机故障诊断研究,[硕士学位论文],武汉:武汉大学,2004.
[11] Betting, B.,Han, R.P.S., Predictive maintenance using the rotordynamic model of a hydraulic turbine-generator rotor, Journal of Vibration and Acoustics,1998,120(2):441-448.
[12] Mack, R.,Drtina, P.,Lang, E., Numerical prediction of erosion on guide vanes and in labyrinth seals in hydraulic turbines, Wear,1999,233-235:685-691.
[13] Liu, C.,Iung, B.,Ye, L.,et al.,An innovative hydraulic turbine governor for predictive maintenance of hydropower plant,in Proceedings of the 41st IEEEConference on Decision and Control, Las Vegas, Nevada, 2002:925-930.
[14]徐东海,水电机组状态监测及故障诊断研究,[硕士学位论文],北京:清华大学,2003.
[15]魏然,水轮发电机组状态监测与故障诊断系统研究,[硕士学位论文],西安:西安理工大学,2001.
[16]李成家,张双平,康振国等,水轮机转轮裂纹的检测分析与研究,西北电力技术,2001,(6):21-23+66.
[17]赵奇,李二伟,薛世钦等,进口水轮机转轮叶片的裂纹分析及处理,检验检疫科学,2003,13(4):4-6.
[18]陈凯,陆军,何万成,红石电站水轮机转轮叶片裂纹的分析及处理,水力发电,2003,29(1):54-56.
[19]罗永要,王正伟,梁权伟,混流式水轮机转轮动载荷作用下的应力特性,清华大学学报(自然科学版),2005,45(2):235-237+257.
[20]姬书得,方洪渊,刘雪松,混流式水轮机转轮简化模型焊接应力场的数值模拟,大电机技术,2004,(6):55-57+71.
[21]黄文,吴彤峰,毛汉领,水轮机转轮叶片的应力分布及其裂纹成因,实用测试技术,1999,(3):33-34.
[22] Carpinteri, A.,Brighenti, R.,Huth, H.-J.,et al., Fatigue growth of a surface crack in a welded T-joint, International Journal of Fatigue,2005,27(1):59-69.
[23] Masse, B.,Page, M.,Magnan, R.,et al., Numerical simulations: a tool to improve performance of hydraulic turbines, in ASCE Conference Proceeding on Waterpower. 1999, ASCE: Las Vegas. p. 1-10.
[24] Escaler, X.,Egusquiza, E.,Farhat, M.,et al., Detection of cavitation in hydraulic turbines, Mechanical Systems and Signal Processing,2006,20(4):983-1007.
[25]沈功田,戴光,刘时风,中国声发射检测技术进展,无损检测,2003,25(6):302-307.
[26] Miller, R.K.,Mclntire, P., Society for Nondestructive Testing. Acoustic Emission Testing, American Society for Nondestructive Testing.1987
[27] PAC PolyMODALTM Wave Toolbox for MISTRAS-2001, Physics Acoustic Corporation.
[28]耿荣生,声发射技术发展现状——学会成立20周年回顾,无损检测,1998,20(6):151-154.
[29]全国第八届声发射会议(论文集). 1999,中国无损检测学会声发射专业委员会:上海.
[30]全国第九届声发射会议(论文集). 2001,中国无损检测学会声发射专业委员会:成都.
[31] Gong, Z.,DuQuesnay, D.L.,McBride, S.L., Measurement and Interpretation of Fatigue Crack Growth in 7075 Aluminum Alloy Using Acoustic Emission Monitoring, Journal of Testing and Evaluation,1998,26(6):567-574.
[32] Berkovits, A.,Fang, D., Study of fatigue crack characteristics by acoustic emission, Engineering Fracture Mechanics,1995,51(3):401-409.
[33] Daniel, I. M., Luo, J. J., Sifniotopoulos, C. G., et al., Acoustic emission monitoring of fatigue damage in metals, Nondestructive Testing and Evaluation, 1998,14(1-2):71-87.
[34] Talebzadeh, M.,Roberts, T.M., Correlation of crack propagation and acoustic emission rates, Key Engineering Materials,2001,(204-205):341-350.
[35] Roberts, T.M.,Davies, A.W., Structural integrity of welded steel structures, Key engineering materials,1999,(167-168):142-151.
[36] Roberts, T.M.,Talebzadeh, M., Fatigue life prediction based on crack propagation and acoustic emission count rates, Journal of constructional steel research,2003,59(6):679-694.
[37] Roberts, T.M.,Talebzadeh, M., Acoustic emission monitoring of fatigue crackpropagation, Journal of Constructional Steel Research,2003,59(6):695-712.
[38]李耀东,黄成祥,侯力,模态声发射技术在构件疲劳裂纹检测中的应用,振动与冲击,2005,24(2):122-126.
[39]李光海,刘正义,基于声发射技术的金属高频疲劳监测,中国机械工程,2004,15(13):1205-1209.
[40] Biancolini, M.E.,Brutti, C.,Paparo, G.,et al., Fatigue cracks nucleation on steel, acoustic emission and fractal analysis, International Journal of Fatigue,2006,28(12):1820-1825.
[41]耿荣生,利用模态声发射技术监测飞机主结构的日历损伤,机械工程学报,2003,39(9):128-132.
[42] Shiwa, M.,Carpenter, S.,Kishi, T., Analysis of acoustic emission signals generated during the fatigue testing of GFRP, Journal of composite materials,1996,30(18):2019-2041.
[43] Tsamtsakis, D.,Wevers, M.,Meester, P.D., Acoustic emission from CFRP laminates during fatigue loading, Journal of reinforced plastics and composites,1998,17(13):1185-1201.
[44] Mizutani, Y. , Nagashima, K. , Takemoto, M. , et al., Fracture mechanism characterization of cross-ply carbon-fiber composites using acoustic emission analysis, NDT & E International,2000,33(2):101-110.
[45] Surgeon, M.,Wevers, M., Modal analysis of acoustic emission signals from CFRP laminates, NDT & E International,1999,32(6):311-322.
[46]郑金海,王彬,骆英,基于声发射技术的预应力钢筋混凝土梁破坏特性试验研究,西部探矿工程,2006,(9):224-226.
[47] Rozanov, A.O.,Savel, e.V.N.,Stanchits, S.A., Spectral analysis of pulse-induced acoustic field in rocks and its applications, CT theory and applications,1999,(8):47-51.
[48]杨明纬,声发射检测,北京:机械工业出版社.2005
[49] Ennaceur, C.,Laksimi, A.,Herve, C.,et al., Monitoring crack growth in pressure vessel steels by the acoustic emission technique and the method of potential difference, International Journal of Pressure Vessels and Piping,2006,83(3):197-204.
[50]沈功田,金属压力容器的声发射源特性及识别方法的研究,[博士学位论文],北京:清华大学,1998.
[51]刘孝敏,Holford, K.M.,钢板桁架疲劳裂纹生长模型和声发射特征的研究,中国科学技术大学学报,1998,28(3):326-330.
[52]耿荣生,景鹏,雷洪,飞机主梁疲劳裂纹萌生声发射信号的识别方法,航空学报,1996,17(3):368-372.
[53] Ravindra, H.V., Some aspects of acoustic emission signal processing, Journal of Materials Processing Technology,2001,109(3):242-247.
[54] Haber, R.E.,Jimenez, J.E.,Peres, C.R.,et al., An investigation of tool-wear monitoring in a high-speed machining process, Sensors and Actuators A: Physical,2004,116(3):539-545.
[55]王永涛,韩建,牟海维,基于声谱分析的阀门内泄漏检测系统,大庆石油学院学报,2006,30(3):72-73.
[56] Gupta, N.,Ramu, T.S., Estimation of partial discharge parameters in GIS using acoustic emission techniques, Journal of Sound and Vibration,2001,247(2):243-260.
[57] Johan Singh, P.,Mukhopadhyay, C.K.,Jayakumar, T.,et al., Understanding fatigue crack propagation in AISI 316 (N) weld using Elber's crack closure concept: Experimental results from GCMOD and acoustic emission techniques, International Journal of Fatigue,2007,29(12):2170-2179.
[58] Yue, Y.L.,Wei, P.Y.,Li, S.H.,et al., Acoustic emission signature of fatigue cracksat Titanium alloy welded joints, Journal of Ship Mechanics,2008,12(3):429-439.
[59] GB/T18182-2000金属压力容器声发射检测及结果评价方法,中国标准出版社出版第一版.北京,2000.
[60] Biancolini, M.E.,Brutti, C.,Paparo, G.,et al., Fatigue cracks nucleation on steel, acoustic emission and fractal analysis, International Journal of Fatigue,2006,28(12):1820.
[61]张平,集成化声发射信号处理平台的研究[博士学位论文],北京:清华大学, 2002.
[62]陈玉华,刘时风,耿荣生等,声发射信号的谱分析和相关分析,无损检测,2002,24(9):395-399.
[63] Merson, D.,Razuvaev, A.,Vinogradov, A., Application of the spectral analysis of acoustic emission signals to studies of vulnerability of TiN coatings on steel substrates, Russian journal of nondestructive testing,2002,38(7):508-516.
[64]杨占才,声发射事件谱分析技术在滚动轴承故障诊断中的应用,设备管理与维修,2000,(11):27-28.
[65] Surgeon, M.,Wevers, M., One sensor linear location of acoustic emission events using plate wave theories, Materials Science and Engineering A,1999,265(1-2):254-261.
[66]耿荣生,沈功田,刘时风,模态声发射基本理论,无损检测,2002,24(7):302-306.
[67] Jiao, J.,He, C.,Wu, B.,et al., Application of wavelet transform on modal acoustic emission source location in thin plates with one sensor, International Journal of Pressure Vessels and Piping,2004,81(5):427-431.
[68] Pullin, R.,Holford, K.M.,Baxter, M.G., Modal analysis of acoustic emission signals from artificial and fatigue crack sources in aerospace grade steel, key engineering materials,2005,293-294:217-224.
[69] Terchi, L., Time-frequency analysis of acoustic emission, Engineering Fracture Mechanics,2002,69:717-728.
[70] Suzuki, H.,Kinjo, T.,Hayash, Y., Wavelet tranform of acoustic emission signals, Journal of Acoustic Emission,1996,14(2):69-84.
[71]李录平,邹新元,唐月清,小波变换在声发射信号特征参数检测中的应用,振动与冲击,2001,20(2):67-68+81.
[72] Yang, L.,Zhou, Y.C., Wavelet analysis of acoustic emission signals from thermal barrier coatings, Transactions of Nonferrous Metals Society of China,2006,16:s270-s275.
[73]陈春朝,李孟源,王恒迪等,铁路货车轴承的声发射故障诊断及分析,轴承,2006,(1):36-38.
[74]王清明,董申,李小俚,基于小波分析的钻头破损检测,哈尔滨工业大学学报,2000,32(6):76-79.
[75] Li, X.,Dong, S.,Yuan, Z., Discrete wavelet transform for tool breakage monitoring, International Journal of Machine Tools and Manufacture,1999,39(12):1935-1944.
[76] Warren Liao, T.,Ting, C.-F.,Qu, J.,et al., A wavelet-based methodology for grinding wheel condition monitoring, International Journal of Machine Tools and Manufacture,2007,47(3-4):580-592.
[77]马建仓,吴启彬,罗磊,小波包分析在旋转机械冲击故障诊断中的应用研究,机械科学与技术,1998,17(2):310-312.
[78] Qi, G., Wavelet-based AE characterization of composite materials, NDT & E International,2000,33(3):133-144.
[79] Staszewski, W.J.,Holford, K.M., Wavelet signal processing of acoustic emission data, Key engineering materials,2001,204-205:351-358.
[80] Loutas, T.H.,Kostopoulos, V.,Ramirez-Jimenez, C.,et al., Damage evolution in center-holed glass/polyester composites under quasi-static loading using time/frequency analysis of acoustic emission monitored waveforms, Composites Science and Technology,2006,66(10):1366-1375.
[81]张承彪,罗运柏,文习山,主成分分析在变压器故障诊断中的应用研究,高电压技术,2005,31(8):9-11.
[82]苑宇,马孝江,基于主分量分析的柴油机振动信号特征提取,中国机械工程,2007,18(8):971-975.
[83]杨世锡,焦卫东,吴昭同,独立分量分析基网络应用于旋转机械故障特征提取与分类,机械工程学报,2004,40(3):45-49.
[84] Ozawa, S.,Sakaguchi, Y.,Kotani, M.,A study of feature extraction using supervised independent component analysis,in International Joint Conference on Neural Networks, Washington, DC, 2001:2958-2963.
[85] Martiriggiano, T.,Leo, M.,Spagnolo, P.,et al.,Facial feature extraction by kernel independent component analysis,in IEEE Conference on Advanced Video and Signal Based Surveillance, Como, Italy, 2005:270-275.
[86] Widodo, A.,Yang, B.-S., Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors, Expert Systems with Applications,2007,33(1):241-250.
[87] Jade, A.M.,Srikanth, B.,Jayaraman, V.K.,et al., Feature extraction and denoising using kernel PCA, Chemical Engineering Science,2003,58(19):4441-4448.
[88]刘爱伦,袁小艳,俞金寿,基于KPCA-SVC的复杂过程故障诊断,仪器仪表学报,2007,28(5):870-874.
[89]郭辉,刘贺平,王玲, KPCA-LSSVM建模方法及在钢材淬透性中的应用研究,控制与决策,2006,21(9):1073-1076.
[90] Wang, G.,Sun, Y.-a.,Ding, Q.,et al., Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression, Analytica Chimica Acta,2007,594(1):101-106.
[91]耿荣生,沈功田,刘时风,声发射信号处理和分析技术,无损检测,2002,24(1):23-28.
[92]顾江,张光新,刘国华等,基于独立分量分析的声发射信号去噪方法,江南大学学报(自然科学版),2008,7(1):55-59.
[93] Iyer, D.,Zouridakis, G., Single-trial evoked potential estimation: Comparison between independent component analysis and wavelet denoising, Clinical Neurophysiology,2007,118(3):495-504.
[94] Li, Z.,He, Z.,Zi, Y.,et al., Customized wavelet denoising using intra- and inter-scale dependency for bearing fault detection, Journal of Sound and Vibration,2008,313(1-2):342-359.
[95]张全明,刘会金,周新启等,电能质量信号的小波软阈值去噪方法,高电压技术,2006,32(1):99-101.
[96]王成江,舒乃秋,放电声发射波的小波提纯与消噪,高压电器,2003,39(6):8-10.
[97] Li, W.,Gu, F.,Ball, A.D.,et al., A study of the noise from diesel engines using the independent component analysis, Mechanical Systems and Signal Processing,2001,15(6):1165-1184.
[98]夏文静,傅行军,基于ICA在强背景噪声振动信号中的去噪研究,汽轮机技术,2006,48(2):121-123,155.
[99]季忠,金涛,杨炯明等,虚拟噪声通道在基于ICA消噪过程中的应用,中国机械工程,2005,16(4):350-352,364.
[100]Li, Z.,He, Y.,Chu, F., Application of the blind source separation in machine fault diagnosis: A review and prospect, Mechanical Systems and Signal Processing,In Press, Corrected Proof
[101]Hyvarinen, A., Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation, Neural Computation,1999,11(7):1739-1768.
[102]孔薇,杨杰,周越,基于独立成分分析的强背景噪声去噪方法,上海交通大学学报,2004,38(12):1957-1961.
[103]He, Q.B.,Feng, Z.H.,Kong, F.R., Detection of signal transients using independent component analysis and its application in gearbox condition monitoring, Mechanical Systems and Signal Processing,2007,21(5):2056-2071.
[104]Ju, L.,Caihua, Z.,Xin, Z.,et al.,An Approach of Speech Enhancement by Sparse Code Shrinkage,in International Conference on Neural Networks and Brain, Beijing, China, 2005:1952-1956.
[105]沈功田,耿荣生,刘时风,声发射源定位技术,无损检测,2002,24(3):114-117,125.
[106]金钟山,刘时风,耿荣生等,曲面和三维结构的声发射源定位方法,无损检测,2002,24(5):205-211.
[107]Lympertos, E.M.,Dermatas, E.S., Acoustic emission source location in dispersive media, Signal Processing,2007,87(12):3218-3225.
[108]Axinte, D.A.,Natarajan, D.R.,Gindy, N.N.Z., An approach to use an array of three acoustic emission sensors to locate uneven events in machining--Part 1: method and validation, International Journal of Machine Tools and Manufacture,2005,45(14):1605-1613.
[109]Ding, Y.,Reuben, R.L.,Steel, J.A., A new method for waveform analysis for estimating AE wave arrival times using wavelet decomposition, NDT & E International,2004,37(4):279-290.
[110]Cui, Y.,Wang, K.,Chen, W.,et al.,Application of wavelet packet transform to acoustic emission source location in thin plates,in 16th World Conference on NDT, Montreal, Canada,2004:233-248.
[111]Yang, M.,Manabe, K.,Hayashi, K.,et al., Data fusion of distributed AE sensors for the detection of friction sources during press forming, Journal of Materials Processing Technology,2003,139(1-3):368-372.
[112]Nivesrangsan, P.,Steel, J.A.,Reuben, R.L., Source location of acoustic emission indiesel engines, Mechanical Systems and Signal Processing,2007,21(2):1103-1114.
[113]龚斌,金志浩,包日东等,压力容器上声发射源的新型定位方法,压力容器,2005,22(6):13,14,54.
[114]Grabec, I.,Kosel, T.,Muzic, P., Location of continuous AE sources by sensory neural networks, Ultrasonics,1998,36(1-5):525-530.
[115]Kirikera, G.R.,Shinde, V.,Schulz, M.J.,et al., Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions, Mechanical Systems and Signal Processing,2007,21(1):280-297.
[116]Spall, J.C.,Maryak, J.L.,Asher, M.S., Neural network approach to locating acoustic emission sources in non-destructive evaluation, Journal of Sound and Vibration,1998,211(1):133-143.
[117]Tian, Y.,Lewin, P.L.,Davies, A.E.,et al.,PD pattern identification using acoustic emission measurement and neural networks,in Eleventh International Symposium on High Voltage Engineering, London, UK, 1999:41-44.
[118]来兴平,张海燕,刘叶玲等,支持向量机在岩石破裂失稳声发射定位实验中的应用,金属矿山,2006,(12):61-63,82.
[119]Du, B.X.,Lu, Y.H.,Wei, G.Z.,et al.,PD localization based on fuzzy theory using AE detection techniques,in 2005 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Nashville, TN, USA, 2005:449-452.
[120]Gaul, L.,Hurlebaus, S., Identification of the impact location on a plate using wavelets, Mechanical Systems and Signal Processing,1998,12(6):783-795.
[121]龚仁荣,顾建祖,骆英等,基于Lamb波频散特性的声发射源平面定位新方法,无损检测,2006,28(10):521-525.
[122]Hyunjo, J.,Young-Su, J., Fracture source location in thin plates using the wavelet transform of dispersive waves, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control,2000,47(3):612-619.
[123]龚仁荣,程志勤,顾建祖,模态声发射在结构材料缺陷定位中的研究,振动与冲击,2006,25(3):176-179+192.
[124]Kosel, T.,Grabec, I.,Kosel, F., Time-delay estimation of acoustic emission signals using ICA, Ultrasonics,2002,40(1-8):303-306.
[125]Intelligent location of two simultaneously active acoustic emission sources, Aerospace Science and Technology,2005,9(1):45-53.
[126]沈功田,耿荣生,刘时风,连续声发射信号的源定位技术,无损检测,2002,24(4):164-167.
[127]Nivesrangsan, P.,Steel, J.A.,Reuben, R.L., AE mapping of engines for spatially located time series, Mechanical Systems and Signal Processing,2005,19(5):1034-1054.
[128]Mba, D.,Hall, L.D., The transmission of acoustic emission across large-scale turbine rotors, NDT & E International,2002,35(8):529-539.
[129]Gang, Q., Attenuation of acoustic emission body waves in acrylic bone cement and synthetic bone using wavelet time-scale analysis, Journal of Biomedicine Material Research,2000,52(1):148-156.
[130]Jeong, H., Analysis of plate wave propagation in anisotropic laminates using a wavelet transform, NDT & E International,2001,34(3):185-190.
[131]Dalton, R.P.,Cawley, P.,Lowe, M.J., Propagation of acoustic emission signals in metallic fuselage structure, Iee Proceedings-Science Measurement and Technology,2001,148(4):169-177.
[132]ASTM E976-99: standard guild for determining the reproducibility of acoustic emission sensor response,1999:395-403.
[133]Wu, J.-D.,Chen, J.-C., Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT & E International,2006,39(4):304-311.
[134]Li, X.,Wu, J., Wavelet analysis of acoustic emission signals in boring, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture,2000,214(5):421-424.
[135]Velayudham, A.,Krishnamurthy, R.,Soundarapandian, T., Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform, Materials Science and Engineering: A,2005,412(1-2):141-145.
[136]Mallat, S., A wavelet tour of signal processing, Massachusetts:Academic Press.1999
[137]Wannucci, M.,Corradi, F., Covariance structure of wavelet coefficients: theory and models in Bayesian perspective, Journal of the Royal Stastistical Society Series B,1999,61(4):971-986.
[138]Giaouris, D.,Finch, J.W., Denoising using wavelets on electric drive applications, Electric Power Systems Research,2008,78(4):559-565.
[139]Hyvarinen, A. , Karhumen, J. , Oja, E., Independent component analysis,A Wiley-Interscience Publication.2001
[140]Hyvarinen, A., Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks,1999,10(3):626-634.
[141]孔薇,盲分离算法的研究及其在声信号处理中的应用,[博士学位论文],上海:上海交通大学,2005.
[142]孔薇,杨杰,周越, ICA特征提取技术在背景噪声建模与分析中的应用,上海交通大学学报,2006,40(3):387-390.
[143]Xin, Z.,Jancovic, P.,Ju, L.,et al., Speech Signal Enhancement Based on MAP Algorithm in the ICA Space, ,IEEE Transactions on Signal Processing,2008,56(5):1812-1820.
[144]Hyvarinen, A.,Hoyer, P.,Oja, E.,Denoising of non gaussian data by Independent Component Analysis and sparse coding,in IEEE workshop on independentcomponent analysis and blind source separation, Aussois, France, 1999:495-489.
[145]Lee, T.-W.,Lewicki, M.S.,The generalized gaussian mixture model using ICA,in Proceeding International workshop ICA, Helsinki, Finland, 2000:239-244.
[146]Kokkinakis, K.,Nandi, A.K., Exponent parameter estimation for generalized Gaussian probability density functions with application to speech modeling, Signal Processing,2005,85(9):1852-1858.
[147]Mallat, S.,Hwang, W.L., Singularity detection and processing with wavelets, IEEE Transactions on Information Theory,1992,38(2):617-643.
[148]Xu, Y.,Weaver, J.B.,Healy, D.M.,et al., Wavelet transform domain filters: a spatially selective noisefiltration technique, IEEE Transactions on Image Processing,1994,3(6):747-758.
[149]Donoho, D.L., De-noising by soft-thresholding, IEEE Transactions on Information Theory,1995,41(3):613-627.
[150]Donoho, D.L.,Johnstone, I.M.,Kerkyacharian, G.,et al., Wavelet shrinkage: asymptopia, Journal -Royal Statistical Society Series B,1995,57(2):301-369.
[151]张吉先,钟秋海,戴亚平,小波门限消噪法应用中分解层数及阈值的确定,中国电机工程学报,2004,24(2):118-122.
[152]Pasti, L.,Walczak, B.,Massart, D.L.,et al., Optimization of signal denoising in discrete wavelet transform, Chemometrics and Intelligent Laboratory Systems,1999,48(1):21-34.
[153]Hernández, L.,Juliá, J.E.,Chiva, S.,et al., Fast classification of two-phase flow regimes based on conductivity signals and artificial neural networks, Measurement Science and Technology,2006,17(6):1511-1521.
[154]Marashdeh, Q.,Warsito, W.,Fan, L.-S.,et al., A nonlinear image reconstruction technique for ECT using a combined neural network approach, Measurement Science and Technology,2006,17(8):2097-2103.
[155] Wang, Z.,Willett, P.,DeAguiar, P.R.,et al., Neural network detection of grinding burn from acoustic emission, International Journal of Machine Tools and Manufacture,2001,41(2):283-309.
[156]Moller, M.F., A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks,1993, 6:525-533.
[157]Sebald, D.J.,Sebald, D.J.,Bucklew, J.A., Support vector machines and the multiple hypothesis test problem, IEEE Transactions on Signal Processing,2001,49(11):2865-2872.
[158]Platt, J.C.,Cristianini, N.,Shawe-Taylor, J., Large margin DAGs for multiclass classification, Advances in Neural Information Processing Systems,2000,12:547-553.
[159]Vapnic, V., Statistical Learning Theory,New York:Wiley.1998
[160]攀世英,混流式水轮机转轮裂纹原因分析及预防措施,水力发电,2002,(5):38-41.
[161]Fisher, D.,The cause of runner crack and the solutions implement for the Xiaolangdi Hydroelectric project,in Proceedings of the XXIst IAHR symposium on Hydraulic machinery and systems, Lausanne, 2002
[162]ASTM E647-05: Standard Test Method for Measurement of Fatigue Crack Growth Rates, in Annual Book of ASTM Standard. 2005.
[163]GB/T 6398-2000,金属材料疲劳裂纹扩展速率实验方法,2000.
[164]Gong, Z.,Nyborg, E.O.,Oommen, G., Acoustic emission monitoring of steel railroad bridges, Materials Evaluation,1992,50(7):883-887.
[165]Roques, A.,Browne, M.,Thompson, J.,et al., Investigation of fatigue crack growth in acrylic bone cement using the acoustic emission technique, Biomaterials,2004,25(5):769-778.
[166]余圣甫,王铁琦,杨其良等, ZG20MnSi钢断裂韧度和疲劳裂纹,机械工程材料,2005,29(6):17-19.
[167]Moorthy, V.,Jayakumar, T.,Raj, B., Influence of micro structure on acoustic emission behavior during stage 2 fatigue crack growth in solution annealed, thermally aged and weld specimens of AISI type 316 stainless steel, Materials Science and Engineering A,1996,212(2):273-280.
[168]Lademo, O.G.,Hopperstad, O.S.,Langseth, M., An evaluation of yield criteria and flow rules for aluminium alloys, International Journal of Plasticity,1999,15(2):191-208.