基于振动信号的岩石单轴抗压强度钻进预测实验研究

doi:10.13722/j.cnki.jrme.2023.0960

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (7149 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了研究钻进振动信号与岩体地质力学参数之间的响应关系，准确、快速地感知、预测岩石单轴抗压强度，开展基于钻进振动信号的岩石单轴抗压强度预测研究。以室内钻取花岗岩、石灰岩、砂岩和煤四类原岩(煤)试件实验为基础，结合傅里叶变换和振动信号降噪方法构建GA-BP神经网络模型，并对比分析降噪前后以及不同降噪方法模型的预测性能。结果表明：钻进振动信号与岩石单轴抗压强度之间有响应关系，应用钻进振动信号可预测岩石单轴抗压强度；采用Adobe Audition软件对振动信号进行降噪处理的GA-BP神经网络预测模型决定系数R2为0.838，均方根误差为7.063 MPa，平均绝对误差为5.347 MPa，其结果优于原始预测模型和一般降噪方法预测模型；与原始预测模型相比，最优降噪模型在预测精度上提升了6.3%，均方根误差减小1.954 MPa，平均绝对误差减小1.621 MPa；同一预测模型中不同岩性的预测效果存在一定差异。降噪信号GA-BP神经网络预测模型对单轴抗压强度有较优秀的预测能力，所用方法可为在岩体地质力学参数随钻测量方面提供基础。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	郝建1
	刘河清1
	刘建康1
	吕家庆1
	郑义宁2
	刘建荣3

关键词 ：岩石力学, 钻进振动信号, 单轴抗压强度, 傅里叶变换, 信号降噪, 人工神经网络

Abstract：In order to study the response relationship between the vibration signal with drilling and the geomechanical parameters of the rock mass，and to perceive and predict the uniaxial compressive strength of the rock accurately and quickly，a research on the prediction of uniaxial compressive strength of the rock based on the vibration signal with drilling was carried out. Based on indoor drilling experiments of four types of raw rock(coal) specimens，namely granite，limestone，sandstone and coal，the GA-BP neural network model was constructed by combining Fourier transform and vibration signal noise reduction methods，and the prediction performance of the model before and after the noise reduction，as well as the models with different noise reduction methods，were compared and analyzed. The results show that there is a responsive relationship between the vibration signal with drilling and the uniaxial compressive strength of rock，and the uniaxial compressive strength of rock can be predicted by using the vibration signal while drilling. The GA-BP neural network prediction model using Adobe Audition software to denoise the vibration signal has a determination coefficient R2 of 0.838，a root mean square error of 7.063 MPa，and an average absolute error of 5.347 MPa. The results are better than the original prediction model and the general noise reduction method prediction model. Compared with the original prediction model，the prediction accuracy of the optimal noise reduction model is improved by 6.3 %，the root mean square error is reduced by 1.954 MPa，and the average absolute error is reduced by 1.621 MPa. There are some differences in the prediction effect of different lithology in the same prediction model. The GA-BP neural network prediction model of noise reduction signal has excellent prediction ability for uniaxial compressive strength. The method can provide a basis for the measurement of rock mass geomechanical parameters while drilling.

Key words： rock mechanics vibration signals with the drill uniaxial compressive strength Fourier transform signal noise reduction artificial neural networks

引用本文:

郝建1，刘河清1，刘建康1，吕家庆1，郑义宁2，刘建荣3. 基于振动信号的岩石单轴抗压强度钻进预测实验研究[J]. 岩石力学与工程学报, 2024, 43(6): 1406-1424.
HAO Jian1，LIU Heqing1，LIU Jiankang1，LYU Jiaqing1，ZHENG Yining2，LIU Jianrong3. Experimental study of rock uniaxial compressive strength prediction with drilling based on vibration signals. , 2024, 43(6): 1406-1424.

链接本文:

http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2023.0960 或 http://rockmech.whrsm.ac.cn/CN/Y2024/V43/I6/1406

[26]	KLAIC M，MURAT Z，STAROVESKI T，et al. Tool wear monitoring in rock drilling applications using vibration signals[J]. Wear，2018，408：222-227.
[35]	REN H J，YIN A J，ZHANG Q. Vibration signal denoising using partial differential equations of arbitrary order[J]. Measurement，2019，148：106917.
[2]	谭卓英，李文，岳鹏君，等. 基于钻进参数的岩土地层结构识别技术与方法[J]. 岩土工程学报，2015，37(7)：1 328-1 333.(TAN Zhuoying，LI Wen，YUE Pengjun，et al. Techniques and approaches for identification of geo-formation structure based on diamond drilling parameters[J]. Chinese Journal of Geotechnical Engineering，2015，37(7)：1 328-1 333.(in Chinese))
[5]	吕祥锋，曹立厅，孟令峰，等. 旋压触探随钻摩阻表征围岩应力分布特征研究[J]. 岩石力学与工程学报，2023，42(10)：2 385-2 399. (LYU Xiangfeng，CAO Liting，MENG Lingfeng，et al. Surrounding rock stress distribution characterization via drilling friction resistance while rotary sounding[J]. Chinese Journal of Rock Mechanics and Engineering，2023，42(10)：2 385-2 399.(in Chinese))
[7]	SUN H，DU W S，LIU C. Uniaxial compressive strength determination of rocks using X-ray computed tomography and convolutional neural networks[J]. Rock Mechanics and Rock Engineering，2021，54(8)：4 225-4 237.
[9]	SAEDI B，MOHAMMADI S D. Prediction of uniaxial compressive strength and elastic modulus of migmatites by microstructural characteristics using artificial neural networks[J]. Rock Mechanics and Rock Engineering，2021，54(11)：5 617-5 637.
[10]	LI Y M，ZHAO G F. A numerical integrated approach for the estimation of the uniaxial compression strength of rock from point load tests[J]. International Journal of Rock Mechanics and Mining Sciences，2021，148：104939.
[12]	MENG W Z，WU W. Machine learning-aided prediction of the mechanical properties of frozen fractured rocks[J]. Rock Mechanics and Rock Engineering，2023，56(1)：261-273.
[15]	岳中琦，李焯芬，罗锦添，等. 香港大学钻孔过程数字监测仪在土钉加固斜坡工程中的应用[J]. 岩石力学与工程学报，2002，21(11)：1 685-1 690.(YUE Zhongqi，LEE C F，LAW K T，et al. Use of HKU drilling process monitor in slope stabilization[J]. Chinese Journal of Rock Mechanics and Engineering，2002，21(11)：1 685-1 690.(in Chinese))
[17]	QI W，HONGKE G，BEI J，et al. Relationship model for the drilling parameters from a digital drilling rig versus the rock mechanical parameters and its application[J]. Rock Mechanics and Rock Engineering，2018，11：357.
[3]	LIU J K，LUAN H J，ZHANG Y C，et al. Prediction of unconfined compressive strength ahead of tunnel face using measurement-while-drilling data based on hybrid genetic algorithm[J]. Geomechanics and Engineering，2020，22(1)：81-95.
[13]	王玉杰，佘磊，赵宇飞，等. 基于数字钻进技术的岩石强度参数测定试验研究[J]. 岩土工程学报，2020，42(9)：1 659-1 668. (WANG Yujie，SHE Lei，ZHAO Yufei，et al. Experimental study on measurement of rock strength parameters based on digital drilling technology[J]. Chinese Journal of Geotechnical Engineering，2020，42(9)：1 659-1 668.( in Chinese))
[20]	刘刚，张家林，刘闯，等. 钻头钻进不同介质时的振动信号特征识别研究[J]. 振动与冲击，2017，36(8)：71-78.(LIU Gang，ZHANG Jialin，LIU Chuang，et al. An identification method of vibration signal features when bit drills different mediums[J]. Journal of Vibration and Shock，2017，36(8)：71-78.(in Chinese))
[22]	CHEN L X，GUO W Y，JIANG Y J，et al. Experimental study on influence of lithology on directional propagation law of type-I cracks[J]. Journal of Central South University，2023，30：3 322-3 334.
[23]	CHEN L X，GUO W Y，ZHANG D X，et al. Experimental study on the influence of prefabricated fissure size on the directional propagation law of rock type-I crack[J]. International Journal of Rock Mechanics and Mining Sciences，2022，160：105274.
[25]	陈晓君，陈根龙，宋刚，等. 基于岩石性质的钻进振动响应分析[J]. 探矿工程：岩土钻掘工程，2019，46(10)：20-26.(CHEN Xiaojun，CHEN Genlong，SONG Gang，et al. Analysis of drilling vibration response based on rock properties[J]. Exploration Engineering：Rock and Soil Drilling and Tunneling，2019，46(10)：20-26.(in Chinese))
[27]	YANG Y，ZENG Q L，YIN G J，et al. Vibration test of single coal gangue particle directly impacting the metal plate and the study of coal gangue recognition based on vibration signal and stacking integration[J]. Ieee Access，2019，(7)：106 783-106 804.
[30]	LAWAL A I，KWON S，HAMMED O S，et al. Blast-induced ground vibration prediction in granite quarries：An application of gene expression programming，ANFIS，and sine cosine algorithm optimized ANN[J]. International Journal of Mining Science and Technology，2021，31(2)：265-277.
[32]	MIAO M Q，SUN Y H，YU J B. Deep sparse representation network for feature learning of vibration signals and its application in gearbox fault diagnosis[J]. Knowledge-Based Systems，2022，240：108116.
[33]	YU Z H，ZHU L，DAI B，et al. Noise reduction based on adaptive prediction fitting algorithm for a heterodyne Phi-OTDR system[J]. Ieee Photonics Technology Letters，2022，34(23)：1 311-1 314.
[1]	田昊，李术才，薛翊国，等. 基于钻进能量理论的隧道凝灰岩地层界面识别及围岩分级方法[J]. 岩土力学，2012，33(8)：2 457- 2 464.(TIAN Hao，LI Shucai，XUE Yiguo，et al. Identification of interface of tuff stratum and classfication of surrounding rock of tunnel using drilling energy theory[J]. Rock and Soil Mechanics，2012，33(8)：2 457-2 464.(in Chinese))
[4]	DAVOODI S，MEHRAD M，WOOD D A，et al. Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning[J]. International Journal of Rock Mechanics and Mining Sciences，2023，170：105546.
[6]	MOMENI E，ARMAGHANI D J，HAJIHASSANI M，et al. Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks[J]. Measurement，2015，60：50-63.
[8]	孙欢，刘晓丽，王恩志，等. 基于X射线断层扫描预测岩石单轴抗压强度[J]. 岩石力学与工程学报，2019，38(增2)：3 575-3 582. (SUN Huan，LIU Xiaoli，WANG Enzhi，et al. Prediction on uniaxial compression strength of rocks with X-ray computed tomography[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(Supp.2)：3 575-3 582.(in Chinese))
[11]	ZHAO T Y，SONG C，LU S F，et al. Prediction of uniaxial compressive strength using fully bayesian gaussian process regression(fB-GPR) with model class selection[J]. Rock Mechanics and Rock Engineering，2022，55(10)：6 301-6 319.
[14]	ABOUTALEB S，BEHNIA M，BAGHERPOUR R，et al. Using non-destructive tests for estimating uniaxial compressive strength and static Young?s modulus of carbonate rocks via some modeling techniques[J]. Bulletin of Engineering Geology and the Environment，2018，77(4)：1 717-1 728.
[16]	岳中琦. 钻孔过程监测(DPM)对工程岩体质量评价方法的完善与提升[J]. 岩石力学与工程学报，2014，33(10)：1 977-1 996.(YUE Zhongqi. Drilling process monitoring for refining and upgrading rock mass quality classification methods[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(10)：1 977-1 996.(in Chinese))
[18]	王琦，高红科，蒋振华，等. 地下工程围岩数字钻探测试系统研发与应用[J]. 岩石力学与工程学报，2020，39(2)：301-310.(WANG Qi，GAO Hongke，JIANG Zhenhua，et al. Development and application of a surrounding rock digital drilling test system of underground engineering[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(2)：301-310.(in Chinese))
[21]	LIU S，FU M，JIA H，et al. Numerical simulation and analysis of drill rods vibration during roof bolt hole drilling in underground mines[J]. International Journal of Mining Science and Technology，2018，28(6)：877-884.
[19]	郭书英，马念杰. 岩层破裂状态与钻削机构振动响应特性研究[J]. 采矿与安全工程学报，2016，33(5)：911-916.(GUO Shuying，MA Nianjie. Strata fracturing state and vibration response characteristics of drill[J]. Journal of Mining and Safety Engineering，2016，33(5)：911-916.(in Chinese))
[28]	李彦忠，胡坤，汪浅予，等. 基于随钻振动信号对钻进介质的识别研究[J]. 煤矿机电，2022，43(2)：1-5.(LI Yanzhong，HU Kun，WANG Qianyu，et al. Research on identification of drilling medium based on vibration signal while drilling[J]. Colliery Mechanical and Electrical Technology，2022，43(2)：1-5.(in Chinese))
[36]	YE Z，YU J B. Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis[J]. Mechanical Systems and Signal Processing，2021，161：107 984.
[24]	LAZAROVA E，KRULAKOVA M，KRUPA V，et al. Regime and rock identification in disintegration by drilling based on vibration signal differentiation[J]. International Journal of Rock Mechanics and Mining Sciences，2022，149：104984.
[29]	LIU M，LIAO S，YANG Y，et al. Tunnel boring machine vibration-based deep learning for the ground identification of working faces[J]. Journal of Rock Mechanics and Geotechnical Engineering，2021，13(6)：1 340-1 357.
[31]	LI C，HUANG R，YI Y，et al. Investigation of filtering algorithm for noise reduction in displacement sensing signal[J]. Ieee Sensors Journal，2021，21(6)：7 808-7 812.
[34]	QIN M，WANG K，PAN K，et al. Analysis of signal characteristics from rock drilling based on vibration and acoustic sensor approaches[J]. Applied Acoustics，2018，140：275-282.