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压力容器无损检测--声发射检测技术 总被引:18,自引:0,他引:18
声发射技术是20世纪60年代开始,目前逐步成熟的一种无损检测方法,已被广泛应用在压力容器检测和结构的完整性评价方面。文中简要介绍了国内外压力容器声发射检测的发展史和现状。给出了压力容器用钢的声发射特性和压力容器声发射检测方法,综合分析了国内外压力容器声发射检测的标准、仪器和应用进展。最后指出了压力容器声发射检测的发展方向,即在线监测、声发射信号的模式识别和人工神经网络模式识别分析。 相似文献
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现场压力容器检验的声发射源 总被引:6,自引:5,他引:1
自1963年Dunegan首次进行压力容器的声发射检验以来.到目前声发射技术在压力容器检验中的应用已超过了30多a(年)。在这30a中.声发射仪从全模拟式到全数字式已经更新了五代以上.声发射技术在北美、欧洲、中国、日本、澳大利亚等许多国家的压力容器检验中得到广泛应用.并制订了许多声发射检验标准.据文献报道.全世界采用声发射技术检验的大型压力容器有数千台.有关压力容器声发射检验的文章也很多。然而.许多文章只报道了在试验室内进行一台压力容器的试验结果.另一些文章报道了进行许多压力容器声发射检验的统计结果.没有给出现场压力容器检验遇到大量声发射源的特性。本文根据笔者对500多台压力容器现场在役和在线检验的数据,给出了现场压力容器检验可能遇到的多种声发射源。这些声发射源包括裂纹、夹渣、未熔合、未焊透等焊接缺陷的开裂和增长、残余应力释放、氧化皮的剥落、结构摩擦、泄漏、风吹、雨滴撞击和电子噪声等。文中时这些声发射源的定位、分布和关联待性分别进行了分析.并列举了大量的实例。1 定位特性1.1 裂纹1.1.1 焊接表面裂纹和体内深埋裂纹的定位控制焊接工艺.在几何尺寸为内径1800mm、筒体长8000mm、壁厚14mm、材质为16MnR的 相似文献
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采场冒顶灾害的声发射预报技术 总被引:10,自引:0,他引:10
简要介绍了用于监测岩体稳定性的声发射源定位系统SDL-I和便携式声射智能监测仪DYF-1,这些仪器能获取一个声发射事件所包含的尽量多的信息,基于这些信息开发了一种有效可靠的预测冒顶技术。该技术利用多个声发射参数(AE事件率、AE能量和m值)评价声发射活动,在这些参数的监测数据基础上应用灰色系统理论预测将来的声发射,预测值通过训练好的冒顶模式识别,由人工神经网络模型输出对应的顶模式(较大规模的顶板塌 相似文献
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《Science & Technology of Welding & Joining》2013,18(2):117-124
AbstractThe present paper describes the application of neural networks to obtain a model for estimating the stability of gas metal arc welding (GMAW) process. A neural network has been developed to obtain and model the relationships between the acoustic emission (AE) signal parameters and the stability of GMAW process. Statistical and temporal parameters of AE signals have been used as input of the neural networks; a multilayer feedforward neural network has been used, trained with back propagation method, and using Levenberg Marquardt's algorithm for different network architectures. Different welding conditions have been studied to analyse the incidence of the parameters of the process in acoustic signals. The AE signals have been processed by using the wavelet transform, and have been characterised statistically. Experimental results are provided to illustrate the proposed approach. Finally a statistical analysis for the validation of the experimental results obtained is presented. As a main result of the study, the effectiveness of the application of the artificial neural networks for modelling stability analysis in welding processes has been demonstrated. The regression analysis demonstrates the validity of neural networks to predict the stability of welding process using the statistical characterisation of the signal parameters of AE that have been calculated. 相似文献
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厚壁压力容器声发射技术声源定位误差分析 总被引:1,自引:0,他引:1
声发射技术(AE)已经被广泛应用到压力容器、压力管道等检验中。声源定位在整个声发射检验与评定结果过程中起重要作用,目前这方面的研究热点是如何提高定位精度。声发射技术通常采用时差定位法来检测压力容器和压力管道的缺陷,通过检测声波到达不同传感器的时间来确定声源位置。对于厚壁压力容器来说,若声源位于容器的内表面或内部,显然容器壁厚会对声源的精确定位产生一定的影响。针对此问题,详细推导并得出厚壁压力容器中声发射检测的定位误差的解析解,分析和讨论了声源定位误差的变化规律。分析结果表明,定位误差的试验值和理论分析符合良好,计算数据与试验值之间的最大误差为7.12%。当容器壁厚小于600mm的情况下,建议实际声发射检测中对声源位置200nm以内区域采用其他常规无损检测方法进行复验以确定实际声源位置。 相似文献
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AE testing of a low-alloy steel pressure vessel 总被引:2,自引:0,他引:2
Testing pressure vessels in the field is generally conducted within a given range of test pressures, determined beforehand according to specified criteria. Thus monitoring of flaw growth using AE is carried under restricted conditions. To study the AE activity of flaws in welded seams of a pressure vessel (made of low-alloy steel) at different stages of growth and at different strains, tests were conducted on both spherical and cylindrical pressure vessels containing different artificial flaws. For given test conditions, the relationships between AE activity of artificial flaws and measured strains were studied. These tests form an important basis for the testing and assessment of engineering pressure vessels in the field. 相似文献
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Seounghwan Lee Suneung Ahn Changsoon Park 《Journal of Materials Engineering and Performance》2014,23(3):700-707
In this article, an in-process monitoring scheme for a pulsed Nd:YAG laser spot welding (LSW) is presented. Acoustic emission (AE) was selected for the feedback signal, and the AE data during LSW were sampled and analyzed for varying process conditions such as laser power and pulse duration. In the analysis, possible AE generation sources such as melting and solidification mechanism during welding were investigated using both the time- and frequency-domain signal processings. The results, which show close relationships between LSW and AE signals, were adopted in the feature (input) selection of a back-propagation artificial neural network, to predict the weldability of stainless steel sheets. Processed outputs agree well with LSW experimental data, which confirms the usefulness of the proposed scheme. 相似文献
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Zhen Wang Peter Willett Paulo R. DeAguiar John Webster 《International Journal of Machine Tools and Manufacture》2001,41(2)
An artificial neural network (ANN) approach is proposed for the detection of workpiece “burn”, the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful. 相似文献
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提出了一种基于小波神经网络的磨削质量在线智能监测及模糊综合评判方法,即通过提取反映磨削质量的多种AE信号特征参数,利用小波神经网络的非线性模型和学习机制,实现磨削质量的在线监测,同时,根据监测结果,利用模糊综合评判方法,对磨削加工质量进行在线综合评判,定出磨削质量等级,为进一步调整加工参数提供信息。 相似文献