基于声发射和改进灰关联度分析的TBM滚刀磨损状态评估方法
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摘要
针对隧道掘进机TBM滚刀在施工过程中磨损快、磨损严重,且缺乏有效检测方法问题,提出基于声发射和改进灰关联度分析的TBM滚刀磨损评估方法。选取无磨损和不同磨损程度的滚刀,通过TBM岩机作用试验平台进行滚刀磨损声波检测,采用声发射数据采集系统采集滚刀的声波;采用最小均方根自适应滤波算法去除声波中的干扰噪声,利用改进灰关联度分析算法计算散点声波多特征参量的综合状态评估值,建立滚刀状态与综合状态评估值区间一一对应的原始数据库。在工程现场TBM滚刀刀座上搭载声发射传感器,对滚刀进行声波检测,然后采用上述方法计算其综合状态评估值;将其在原始数据库中进行比对,找到对应的滚刀状态,实现对TBM滚刀磨损的评估。该方法能有效消除声发射异常散点样本和单一特征参量的影响,准确反映TBM滚刀磨损状态。
Due to the rapid and severe wear of hob of tunnel boring machine(TBM) in construction process and lack of effective detection methods, a wear assessment method for TBM hobs based on acoustic emission and improved grey correlation analysis was proposed. Hobs without wear and with different wear degrees were chosen. The acoustic wave of hob wear was detected by TBM rock machine action test platform, and the acoustic wave of hob was collected by acoustic emission data acquisition system. The least mean square root adaptive filtering algorithm was adopted to remove the interference noise in acoustic wave. The improved grey correlation analysis algorithm was used to calculate the comprehensive state evaluation values of scatter acoustic wave multi-characteristic parameters, and the original database of the hob state corresponding to the comprehensive state evaluation value one by one was established. Acoustic emission sensors were mounted on the base of TBM hob in the engineering site to detect the acoustic wave of the hob, and then the comprehensive state evaluation values were calculated by the above method. The corresponding hob state was found by comparing them in the original database, and the wear evaluation of TBM hob was realized. This method can effectively eliminate the influence of anomalous scatter samples and single characteristic parameters of acoustic emission, and accurately reflect the wear state of TBM hob.
Due to the rapid and severe wear of hob of tunnel boring machine(TBM) in construction process and lack of effective detection methods, a wear assessment method for TBM hobs based on acoustic emission and improved grey correlation analysis was proposed. Hobs without wear and with different wear degrees were chosen. The acoustic wave of hob wear was detected by TBM rock machine action test platform, and the acoustic wave of hob was collected by acoustic emission data acquisition system. The least mean square root adaptive filtering algorithm was adopted to remove the interference noise in acoustic wave. The improved grey correlation analysis algorithm was used to calculate the comprehensive state evaluation values of scatter acoustic wave multi-characteristic parameters, and the original database of the hob state corresponding to the comprehensive state evaluation value one by one was established. Acoustic emission sensors were mounted on the base of TBM hob in the engineering site to detect the acoustic wave of the hob, and then the comprehensive state evaluation values were calculated by the above method. The corresponding hob state was found by comparing them in the original database, and the wear evaluation of TBM hob was realized. This method can effectively eliminate the influence of anomalous scatter samples and single characteristic parameters of acoustic emission, and accurately reflect the wear state of TBM hob.
引文
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[14] 罗本成,原魁,眭凌,等.基于灰关联度评价的投资决策模型及应用[J].系统工程理论与实践,2002,22(9):132-136.(LUO Bencheng,YUAG Kui,SUI Ling,et al.DGR-Based Investment Decision Model with Application[J].System Engineering—Theory & Practice,2002,22(9):132-136.in Chinese)
[15] SINCLAIR A C E,CONNORS D C,FORMBY C L.Acoustic Emission Analysis during Fatigue Crack Growth in Steel[J].Materials Science & Engineering,1977,28(2):263-273.
[16] BERSHAD N J,BERMUDEZ J C M,TOURNERET J Y.Stochastic Analysis of the LMS Algorithm for System Identification with Subspace Inputs[J].IEEE Transactions on Signal Processing,2008,56(3):1018-1027.
[2] 张彪,张志强,孙飞,等.基于三维颗粒流模型的TBM滚刀顺次破岩的研究[J].中国铁道科学,2017,38(4):70-76.(ZHANG Biao,ZHANG Zhiqiang,SUN Fei,et al.Study on Rock Cutting Sequentially by TBM Disc Cutter Based on Three-Dimensional Particle Flow Model [J].China Railway Science,2017,38(4):70-76.in Chinese)
[3] 周佳媚,李志业,高波.TBM施工隧道仰拱预制块的受力分析[J].中国铁道科学,2004,25(3):33-35.(ZHOU Jiamei,LI Zhiye,GAO Bo.Mechanic Analysis of Invert Prefabricate in TBM Construction Tunnel[J].China Railway Science,2004,25(3):33-35.in Chinese)
[4] LEE K Y.Cyclic AE Count Rate and Crack Growth Rate under Low Cycle Fatigue Fracture Loading[J].Engineering Fracture Mechanics,1989,34(5):1069-1073.
[5] GAGAR D,FOOTE P,IRVING P.A New Approach for Fatigue Crack Length Estimation Using the Acoustic Emission Technique in Structural Health Monitoring Applications[J].Smart Materials & Structures,2014,23(10):64-75.
[6] ROGERS L M.The Application of Vibration Signature Analysis and Acoustic Emission Source Location to on-Line Condition Monitoring of Anti-Friction Bearings[J].Tribology International,1979,12(2):51-58.
[7] SKII V R,LYASOTA I M.Features of Acoustic-Emission Signals during the Initiation of a Fatigue Failure in a Welded Joint of an Aluminum Alloy of the Al-Cu-Mn System[J].Russian Journal of Nondestructive Testing,2014,50(2):120-126.
[8] 夏燕冰,赵剑锋,赵华,等.隧道全断面岩石挖掘机的铁谱光谱技术监测[J].中国铁道科学,2002,23(5):112-117.(XIA Yanbing,ZHAO Jianfeng,ZHAO Hua,et al.Condition Monitoring for Tunnel Boring Machine (TBM) Using Ferrography and SOAP [J].China Railway Science,2002,23(5):112-117.in Chinese)
[9] 郭玉华,黄华,谢亮.基于雷达测速曲线的车辆减速器单位制动能高自动计算方法[J].中国铁道科学,2017,38(3):116-123.(GUO Yuhua,HUANG Hua,XIE Liang.Automatic Calculation Method for Energy Taken-out per Unit Length of Car Retarder Based on Radar Velocity Curve[J].China Railway Science,2017,38(3):116-123.in Chinese)
[10] 王雪梅,倪文波,李芾,等.摆式列车线路检测信号的动态自适应滤波研究[J].中国铁道科学,2005,26(5):76-81.(WANG Xuemei,NI Wenbo,LI Fu,et al.Kalman Dynamic Adaptive Filtering to Railway Line Measurement Signals of Tilting Train[J].China Railway Science,2005,26(5):76-81.in Chinese)
[11] SCHOESS J N.Development and Application of Stress-Wave Acoustic Diagnostics for Roller Bearings[J].Proceedings of SPIE -the International Society for Optical Engineering,2000,3986(3):58-70.
[12] TOPOLAR L,PAZDERA L,CIKRLE P.Acoustic Emission Monitoring during Static Modulus Elasticity Test of Concrete Specimen[J].Applied Mechanics and Materials,2013,486:267-272.
[13] 赵彤,梁家碧,夏天翔,等.基于LMS自适应滤波算法的电力变压器有源降噪系统[J].高电压技术,2016,42(7):2299-2307.(ZHAO Tong,LIANG Jiabi,XIA Tianxiang,et al.Active Noise Control System Based on LMS Adaptive Filter Algorithm for Transformer Power Noise Reduction[J].High Voltage Engineering,2016,42(7):2299-2307.in Chinese)
[14] 罗本成,原魁,眭凌,等.基于灰关联度评价的投资决策模型及应用[J].系统工程理论与实践,2002,22(9):132-136.(LUO Bencheng,YUAG Kui,SUI Ling,et al.DGR-Based Investment Decision Model with Application[J].System Engineering—Theory & Practice,2002,22(9):132-136.in Chinese)
[15] SINCLAIR A C E,CONNORS D C,FORMBY C L.Acoustic Emission Analysis during Fatigue Crack Growth in Steel[J].Materials Science & Engineering,1977,28(2):263-273.
[16] BERSHAD N J,BERMUDEZ J C M,TOURNERET J Y.Stochastic Analysis of the LMS Algorithm for System Identification with Subspace Inputs[J].IEEE Transactions on Signal Processing,2008,56(3):1018-1027.