钢轨裂纹伤损声发射源的建模仿真与特征分析

高速铁路的发展对钢轨的安全性提出了特殊要求,因此对钢轨内部裂纹的特征分析具有重要意义。为了研究钢轨内部裂纹声发射源特征,对钢轨中不同类型、深度和传导距离的裂纹声发射源进行了分析.采用有限元方法建立了钢轨模型并通过实验验证,然后结合小波方法和瑞利-兰姆方程分析了裂纹声发射源的特征。结果表明:钢轨中不同类型和不同频率特征的声发射源能够利用声发射模态之间的强度比率来识别;轨腰深度上的声发射源具有统一的模态特征,而轨头和轨底的声发射源则具有复杂的模态特征;同时,不同类型声发射源在钢轨中的传导特征差异明显。研究结果对利用声发射技术进行钢轨裂纹检测提供了指导,可作为实际应用的重要参考。      

Feature analysis of acoustic emission sources for rail crack detection by the finite element method

The safety of rail is very important for the development of high speed railway,and it is necessary to investigate the features of inner cracks in rail.In order to obtain the features of Acoustic Emission(AE)sources of inner cracks in rail,AE sources with different types,depths and propagation distances are examined for crack in rail.The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model.Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources.The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail.There are uniform AE mode features existing in the AE signals from AE sources in rail web,however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes.Different AE source types have the different propagation features in rail.It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.     

Defect detection and location in switch rails by acoustic emission and Lamb wave analysis: A feasibility study

An acoustic emission (AE) based approach is proposed in this study to identify and locate newly initiated defects or the propagation of existing defects in railroad switch rails. Defect-induced AE signals are identified through frequency analysis, as frequencies of these signals are much higher than those induced by structural vibration. Continuous wavelet transform (CWT) is employed to analyze the Lamb wave dispersion of the detected signal, so that two characteristic points can be selected on the CWT contour map to locate the defect. Using this approach, defects in a damaged switch rail can be located using a single sensor.     

Finite element modelling of ultrasound, with reference to transducers and AE waves

Finite element (FE) modelling has a role to play in simulating elastic wave propagation associated with structural vibrations, acoustic phenomena and ultrasound problems. In this work we have used the PAFEC software [PAFEC finite element software PACSYS, Strelley Hall, Nottingham, NG8 6PE, UK]. With the advent of increased computer power and greater availability of software these simulations have become more readily available and will provide improved insight into wave propagation problems. Simulations have been undertaken of transient wave propagation in steel plates with an attached simple resonant transducer. This simulates acoustic emission (AE) propagation in plate like structures relevant to many industrial applications. Simulations for short propagation distances suggest the resonant transducer voltage signal carries information on the plate-waves propagating in the structure, overlaid with the piezoelectric resonance and some information might be extracted from the transducer signal. Looking at the wave propagation information alone, a great deal of variability is seen in the displacement profile for different source types, orientations and locations. Although users have expressed a need for calibration of the AE detection process, this idea remains problematic since the complete generation and detection system has the features of a chaotic system. Using FE modelling a method of "point calibration" might be available, for some specific AE applications such as crack growth along known paths.     

Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

Harmonic wavelet-based data filtering for enhanced machine defect identification

A filter construction technique is presented for enhanced defect identification in rotary machine systems. Based on the generalized harmonic wavelet transform, a series of sub-frequency band wavelet coefficients are constructed by choosing different harmonic wavelet parameter pairs. The energy and entropy associated with each sub-frequency band are then calculated. The filtered signal is obtained by choosing the wavelet coefficients whose corresponding sub-frequency band has the maximum energy-to-entropy ratio. Experimental studies using rolling bearings that contain different types of structural defects have confirmed that the developed new technique enables high signal-to-noise ratio for effective machine defect identification.     

Measuring high-frequency wave propagation in railroad tracks by joint time-frequency analysis

The behavior of high-frequency elastic waves propagating in railroad tracks is relevant to the field of rail noise generation and long-range rail inspection. While a large amount of theoretical and numerical work exists to predict transient vibrations propagating in rails, obtaining experimental data has been particularly challenging due to the multimode and dispersive behavior of the waves.In this work a joint time-frequency analysis based on the Gabor wavelet transform is employed for characterizing longitudinal, lateral and vertical vibrational modes propagating in rails in the 1000- range. The Gabor transform optimizes the time-frequency resolution of the measurements and theoretically requires a single excitation point and a single measurement point. These features make the analysis well-suited for the study of wave propagation in rails.The theory of the wavelet transform is reviewed in the context of dispersive measurements. Accelerometer data were taken from a section of rail subject to impulse dynamic testing in the laboratory. The group (energy) velocity dispersion curves and the frequency-dependent attenuation of the waves were successfully extracted from the wavelet scalograms of the accelerometer signals.     

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.     

Defect identification in rods subject to forced vibrations using the spatial wavelet transform

Use of the wavelet transform (WT) to study the forced vibrations in a rod in order to detect the presence of a defect is proposed. The axial vibrations in an inhomogeneous rod, produced by the application of different forces, are simulated by the Network Simulation Method and the obtained response is analyzed with the WT. An analysis of the detection and location of defects for several applied forces and defects generated by locally changing density or stiffness of the rod is presented. A noise test was carried out to check the robustness of the technique in real situations.     

高速铁路轮轨滚动噪声的分形描述及分形维估计

提出基于分形分析的轮轨滚动噪声研究方法,研究了不同尺度下轮轨滚动噪声增量的统计特性,证明轮轨滚动噪声具有分形特性,可由分形布朗运动描述。在此基础上,利用分形布朗运动的小波系数方差与分形维的幂律关系估计了轮轨滚动噪声的分形维,并估计了以裂纹扩展为代表的钢轨伤损声发射信号的分形维,对比研究发现:轮轨滚动噪声分形维本身具有随机性,不同车速下的轮轨滚动噪声分形维估计以1.5666为均值随机分布在小于2的小区间内;分形维是轮轨滚动噪声的固有特征,与车速无关,不同车速的轮轨滚动噪声可由统一的分形布朗运动模型描述;裂纹扩展信号与轮轨滚动噪声不同,其分形维估计大于2,不满足分形布朗运动模型的要求,但分形维可作为二者的区别特征。研究提取了轮轨滚动噪声的分形维作为固有特征,为高速轮轨滚动噪声描述及轮轨伤损检测提供了有效的分析方法。      

小波变换软阈值去噪在粗晶材料超声检测中的应用

本研究多尺度小波分析方法在粗晶材料超声检测信号增强中的应用。在分析结构噪声，电噪声和缺陷信号的小波变换特性的基础上，提出一种用一个尺度间变化的门限闭值来抑制噪声的小波变换系数，并以各尺度缺陷信号的能量关系形成权值，加权重构信号来提高信噪比。实验结果表明，该方法具有很好的去噪效果，增强了粗晶材料缺陷的检出率。     

Acoustic emission source location in plates using wavelet analysis and cross time frequency spectrum

In this study, the theories of wavelet transform and cross-time frequency spectrum (CTFS) are used to locate AE source with frequency-varying wave velocity in plate-type structures. A rectangular array of four sensors is installed on the plate. When an impact is generated by an artificial AE source such as Hsu–Nielsen method of pencil lead breaking (PLB) at any position of the plate, the AE signals will be detected by four sensors at different times. By wavelet packet decomposition, a packet of signals with frequency range of 0.125–0.25 MHz is selected. The CTFS is calculated by the short-time Fourier transform of the cross-correlation between considered packets captured by AE sensors. The time delay is calculated when the CTFS reaches the maximum value and the corresponding frequency is extracted per this maximum value. The resulting frequency is used to calculate the group velocity of wave velocity in combination with dispersive curve. The resulted locating error shows the high precision of proposed algorithm.     

Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to Acoustic Emission signals

A new method to detect leakage in a water-filled plastic pipe is proposed. In this method, a leakage signal-signature in time domain is first captured by monitoring the Short Time Fourier Transforms (STFT) of AE (Acoustic Emission) signals over a relatively long time-interval. The captured signal is then used to find a mother wavelet (tuned wavelet) for the best signal localization in time and frequency domains. The technique for AE signal detection using tuned wavelet is then described. Practical application of the method proposed herein is then presented using a water-filled plastic pipe as a case study. Signals generated from this experimental setup are collected to identify leakage signal-signatures from other interfering signals (background, pipe natural frequency, splash and environmental noise). The results of the experiment prove that using tuned wavelet, AE events can be detected and identified precisely in time. In addition, sources of signals due to leakage and their respective energy levels can also be recognized.     

超高速撞击声发射信号在载人航天器加筋结构的传播特性实验研究

为研究超高速撞击声发射信号经过载人航天器加筋结构后的传播规律,分别在平板结构和加筋结构上模拟高速撞击实验,利用传感器阵列采集高速撞击产生的声发射信号。结合小波和傅里叶分析方法从板波模态、频域以及时域三方面分析加筋结构对声发射信号传播特性的影响,并研究成坑和穿孔损伤模式下声发射信号的传播规律。结果表明:加筋板中的信号高频部分比平板中高频部分能量少,筋体对信号高频部分有滤波效果。加筋结构受高速撞击产生穿孔损伤时,S0模态声波的能量增多。研究成果可为载人航天器结构的高速撞击感知与定位技术提供有利参考。      

Analysis on accuracy improvement of rotor–stator rubbing localization based on acoustic emission beamforming method

This paper attempts to introduce an improved acoustic emission (AE) beamforming method to localize rotor–stator rubbing fault in rotating machinery. To investigate the propagation characteristics of acoustic emission signals in casing shell plate of rotating machinery, the plate wave theory is used in a thin plate. A simulation is conducted and its result shows the localization accuracy of beamforming depends on multi-mode, dispersion, velocity and array dimension. In order to reduce the effect of propagation characteristics on the source localization, an AE signal pre-process method is introduced by combining plate wave theory and wavelet packet transform. And the revised localization velocity to reduce effect of array size is presented. The accuracy of rubbing localization based on beamforming and the improved method of present paper are compared by the rubbing test carried on a test table of rotating machinery. The results indicate that the improved method can localize rub fault effectively.     

Feature extraction for sound source material recognition with impact sounds

Sound source recognition is a part of environmental sound recognition,which is one of the most important research areas in pattern recognition.Impact sounds carry much physical information associated with the sound sources,which makes impact sound based sound source recognition an important approach to improve recognition performance.In this study,the impact sound continuum synthesized with a ball-plate collision model is used for material recognition of the impacted plates.The basis function learning method and time-frequency representation methods,including the short time Fourier transform and the wavelet packet transform,are applied into classification and the recognition results are compared.The result shows that the features obtained by using the basis function learning perform better for material classification of the impacted plates than that by using the short time Fourier transform and the wavelet packet transform.This demonstrates the high efficiency and superiority of this method in material recognition of sound sources.     

Q235钢板局部腐蚀声发射信号特性研究

为解决腐蚀声发射源特征提取和识别的难题,直接从理论上证明了腐蚀声发射监测的有效性并推导出声发射信号特征。气泡破裂声发射信号幅值近似与气泡半径平方和液位高度成正比,频率与气泡半径成反比;钢板及其腐蚀产物开裂声发射信号幅值与开裂位置局部应力强度成正比,频率与裂纹扩展速度成正比,与裂纹扩展距离成反比。用低频和高频两套声发射系统,同时长时间监测Q235钢板在10%FeCl₃·6H₂O、10%FeCl₃·6H₂O加0.01 mol/L HCl混合液、5%CuSO₄·5H₂O溶液中的腐蚀情况,辅以监测钢板及钢板腐蚀产物开裂作为验证实验。通过对声发射信号的参数及谱分析,得出不同声发射源可以通过撞击数及功率谱在频域的分布来有效区分。实验结果与理论分析相吻合,研究结果对腐蚀声发射监测技术具有重要指导意义。      

Identification of two simultaneous partial discharge sources in an oil-pressboard insulation system using acoustic emission techniques

Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharge (PD) causes insulation degradation and premature failure of insulation. In power apparatus, more than one PD source may be active simultaneously. The nature of insulation degradation for multiple PD sources is different from that due to single PD source. Therefore, it will be helpful for severity assessment of insulation degradation, if the number of active PD sources are identified and classified. This paper presents a method for identification and classification of two simultaneously active PD sources using acoustic emission techniques. The acoustic emission (AE) signals are measured for laboratory simulated PD in an oil-pressboard insulation system for three different electrode systems. The measurements of partial discharge acoustic emission (PDAE) signals are carried out for single PD source and for two simultaneous PD sources. The measured signals are analyzed using discrete wavelet transform (DWT), box counting fractal dimension and lacunarity. Box counting fractal dimension and lacunarity are calculated for DWT decomposed signal of major frequency band. Energy distribution in different frequency bands of DWT decomposed signal along with box counting fractal dimension and lacunarity is used for classification of two simultaneous PD sources.     

声发射技术在铁路系统检测中的研究和应用

高速铁路的快速发展,给我国经济和社会发展带来了巨大的推动作用,同时对铁路系统的安全性提出了更高的要求。针对声发射技术在铁路系统安全检测中的研究和应用,介绍了声发射技术及其在检测应用中的优点,综述了目前国内外声发射技术在铁路系统检测研究中的情况,包括钢轨伤损检测、车轮伤损检测、轮轴轴承伤损检测、铁路结构安全监测以及车体材料安全监测等,特别针对钢轨裂纹伤损引起的钢轨断裂这一铁路安全中的主要事故,着重分析了声发射技术在钢轨裂纹伤损检测研究中的情况,并介绍了在高速铁路方面的应用探索。最后在上述分析总结的基础上,指出了声发射技术在铁路系统特别是高速铁路检测研究中的前景及研究难点。     

抛物方程的多重非均匀网格模型及其应用

提出了抛物方程的多重非均匀网格模型,以准确求解三维空间存在多辐射源的电波传播问题。通过对不同辐射源建立不同的坐标系,并对其仿真空间采用不同的非均匀网格划分,构建了抛物方程的多重非均匀网格模型。在此基础上,实现了三维多辐射源问题的并行计算。实例仿真了空间存在四个辐射源的电波传播特性。结果表明,抛物方程的多重非均匀网格模型能够准确求解多源的空间电磁场分布特性,且在该算例中,并行技术使得抛物方程的计算速度提升了2.41倍,极大地提高了抛物方程对三维多源问题的求解效率。