全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于声发射波形参数的判别算法在模型试验中的应用研究
Application of Discrimination Algorithms Based on AE Waveform Parameters in Model Test

DOI: 10.12677/HJCE.2019.86130, PP. 1114-1124

Keywords: 模型试验,声发射,判别算法,波形参数,快速傅里叶变换(FFT)
Model Test
, Acoustic Emission, Discriminant Algorithm, Waveform Parameters, Fast Fourier Transform (FFT)

Full-Text   Cite this paper   Add to My Lib

Abstract:

本文以三次试验中单个孔隙类岩石单轴压缩全过程试验中获取的242万个声发射撞击事件为样本,基于持续时间、幅值和上升时间三个参数,应用判别算法获得八类声发射的特征波形,进而开展快速傅里叶变换(FFT)和matlab统计分析,重点研究岩石声发射信号的特征分类与应力的对应关系。研究表明:在整个试验阶段,八类类型波的频域主要集中在0~50 kHz和250~300 kHz二个频段,并随应力水平的变化其分布占比具有规律性;自始至终持续时间短、幅值较低、上升时间较长的波占据着大多数,接近峰值强度前,高幅值、上升时间较短的波形比例上升,持续时间长的波比例下降,进入破坏阶段后与之前相反。总体上波形参数中幅值与上升时间存在明显相关性对试件的破坏具有预警作用。该研究方法以整体大数据为研究基础,是对原有声发射参数评价方法的补充,后续进行不同类型岩石试验,将更加完善该方法的应用。
Based on the duration, amplitude and rising time of 2.42 million acoustic emission impact events obtained in the whole process of uniaxial compression test of single porous rock in three tests, eight types of acoustic emission characteristic waveforms are obtained by discriminant algorithm, and then fast Fourier transform (FFT) and Matlab statistical analysis are carried out. This paper focuses on the relationship between the classification of rock acoustic emission signals and stress. The results show that the frequency domains of the eight types of waves are mainly concentrated in the two frequency bands of 0~50 kHz and 250~300 kHz, and their distribution proportion is regular with the change of stress level. Waves with short duration, low amplitude and long rise time occupy the majority. Before approaching the peak intensity, the proportion of waveforms with high amplitude and short rise time increases, while the proportion of waveforms with long duration decreases. After entering the destruction stage, the proportion of waveforms with high amplitude and short rise time decreases. In general, there is a significant correlation between the amplitude of the waveform parameters and the rising time, which has an early warning effect on the failure of the specimens. The research method is based on large data and is a supplement to the original acoustic emission parameter evaluation method. The subsequent rock tests of different types will improve the application of this method.

References

[1]  耿荣生, 沈功田, 刘时风. 声发射信号处理和分析技术[J]. 无损检测, 2002, 24(1): 23-28.
[2]  贾雪娜. 应变岩爆试验的声发射本征频谱特征[D]: [博士学位论文]. 北京: 中国矿业大学, 2013.
[3]  姜耀东, 吕玉凯, 赵毅鑫, 宋义敏, 陶磊. 煤样失稳破坏的多参量监测试验[J]. 岩石力学与工程学报, 2012, 31(4): 667-674.
[4]  左建平, 裴建良, 刘建锋, 彭瑞东, 李岳春. 煤岩体破裂过程中声发射行为及时空演化机制[J]. 岩石力学与工程学报, 2011, 30(8): 1564-1570.
[5]  Yuyama, S., Yokoyama, K., Niitani, K., Ohtsu, M. and Uomoto, T. (2007) Detection and Evaluation of Failures in High-Strength Tendon of Pre-Stressed Concrete Bridges by Acoustic Emission. Construction and Building Materials, 21, 491-500.
https://doi.org/10.1016/j.conbuildmat.2006.04.010
[6]  姜德义, 陈结, 任松, 王维忠, 白月明. 盐岩单轴应变率效应与声发射特征试验研究[J]. 岩石力学与工程学报, 2012, 31(2): 326-336.
[7]  纪洪广, 王宏伟, 曹善忠, 等. 花岗岩单轴受压条件下声发射信号频率特征试验研究[J]. 岩石力学与工程学报, 2012, 31(S1): 2900-2905.
[8]  李楠, 王恩元, 赵恩来, 马衍坤, 许福乐, 钱伟华. 岩石循环加载和分级加载损伤破坏声发射实验研究[J]. 煤炭学报, 2010, 35(7): 1099-1103.
[9]  逄焕东, 张兴民, 姜福兴. 岩石类材料声发射事件的波谱分析[J]. 煤炭学报, 2004, 29(5): 540-544.
[10]  王伟魁, 曾周末, 杜刚, 魏永佳, 宋诗哲. 304控氮不锈钢应力腐蚀过程中声发射信号聚类分析[J]. 化工学报, 2011, 62(4): 1027-1033.
[11]  沈功田, 段庆儒, 周裕峰, 李帮宪, 刘其志, 李春树, 蒋仕良. 压力容器声发射信号人工神经网络模式识别方法的研究[J]. 无损检测, 2001, 23(4): 144-146+149.
[12]  Arakawa, K. and Matsuo, T. (2017) Acoustic Emission Pattern Recognition Method Utilizing Elastic Wave Simulation. Materials Transactions, 58, 1411-1417.
https://doi.org/10.2320/matertrans.M2017104
[13]  张艳博, 梁鹏, 田宝柱, 姚旭龙, 孙林, 刘祥鑫. 花岗岩灾变声发射信号多参量耦合分析及主破裂前兆特征试验研究[J]. 岩石力学与工程学报, 2016, 35(11): 2248-2258.
[14]  张艳博, 杨震, 姚旭龙, 梁鹏, 田宝柱, 孙林. 基于声发射信号聚类分析和神经网络识别的岩爆预警方法实验研究[J]. 岩土力学, 2017, 38(S2): 89-98.
[15]  王宗炼, 任会兰, 宁建国. 基于小波变换的混凝土压缩损伤模式识别[J]. 兵工学报, 2017, 38(9): 1745-1753.
[16]  罗津辉, 蔡忠理, 刘克, 李小春. 用声波参数确定岩石加载破坏过程的不同阶段[J]. 岩土力学, 1992(1): 51-56.
[17]  李春林, 陈旭红. 应用多元统计分析[M]. 北京: 清华大学出版社, 2013.
[18]  魏建新, 狄帮让. 裂隙密度对纵波传播特性影响的实验观测[J]. 石油地球物理勘探, 2007, 42(5): 554-559.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133