针对地下施工中TBM(Tunnel Boring Machine)刀具磨损更换频繁且缺乏有效方法对其状态进行评估问题,将声发射技术用于TBM刀具检测,以TBM模态掘进试验台为对象,采集不同磨损程度的滚刀声发射信号研究声发射单特征参量及多特征参量对滚刀磨损状态趋势评估影响,提出基于改进CRITIC声发射多特征融合刀具状态评估新方法。滚刀磨损量测试表明,改进CRITIC声发射多特征融合后所得评估值对刀具磨损信息更敏感,能有效评估及预测刀具磨损状态,可为TBM刀具现场检修、保养提供指导。
Abstract
The TBM tool changes frequently and lacks effective method to evaluate the tool condition in underground construction.The acoustic emission technology is applicated to detect TBM tool.Based on TBM mode,and by acquiring acoustic emission signals of different tool wear.Researching single parameter and multi parameter acoustic emission influence hob wear trend and put a kind of improved CRITIC Method to evaluate hob wear. The disc cutter wear test showed that the improved CRITIC Method is more sensitive to the tool wear information.The new method is more efficient to evaluate and predicte tool wear,and can provide guidance for repairing and maintenance the cutting tool.
关键词
声发射 /
CRITIC /
特征融合 /
TBM /
状态评估
{{custom_keyword}} /
Key words
acoustic emission /
CRITIC /
feature fusion /
TBM /
condition assessment
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Gong Q M,Zhao J,Hefny A M.Numerical simulation of rock fragmentation process induced by two TBM cutters and cutter spacing optimization[J]. Tunnelling and Underground Space Technology,2006(64):2509- 2516.
[2] 孙金山,陈明,陈保国.TBM滚刀破岩过程影响因素 数值模拟研究[J].岩土力学,2011,32(6): 1892-1897.
SUN Jin-shan,CHEN Ming,CHEN Bao-guo.Numerical simulaition of influence factors for rock fragmentation by TBM cutters[J]. Rock and Soil Mechanics,2011, 32(6): 1892-1897.
[3] Jamaludin N, Mba D. Monitoring extremely slow rolling element bearings part 1[J].NDT & E International, 2002(35): 349-358.
[4] Rogers L M. The application of vibration analysis and acoustic emission source location to on-line condition monitoring of antifriction bearings[J]. Tribology International,2004, 12(2) :51-59.
[5] Ghamdi A M,Cole P,Such R,et al.Estimation of bearing defect size with acoustic emission[J].Insight, 2004, 46(12):758-761.
[6] NiShimoto C R,Citti P. Comparison of accelerometer and acoustic emission signals as diagnostic tools in assessing bearing[C]. Proceedings of 2nd International Conference on Condition Monitoring, London, UK, 1988: 117-125.
[7] Korenaga A,Yoshioka T. Development and application of stress-wave acoustic diagnostics for roller bearings [J]. Proceedings of SPIE, The International society for Optical Engineering, 2000 ,28:58-70.
[8] Toshiharub T, Katsuhiko M. Development of a new composite structure segment for large diameter shield tunnel[J]. Tunnelling and Underground Space Technology , 2004,12(2):58-70.
[9] Jahan A, Mustapha F, Sapuan S M,et al. A framework for weighting of criteria in ranking stage of material selection process[J]. The International Journal of Advanced Manufacturing Technology,2012,58 (1): 411-420.
[10] Anis C. Nonlinear channel estimation for of dm system by complex LS-SVM under high mobility condittons[J]. International Journal of Wireless & Mobile Networks,2011 6(12):758-761.
[11] 邵忍平,李永龙. 基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J]. 振动与冲击,2012,31(8):96-106.
SHAO Ren-ping, LI Yong-long. Gear fault pattern identification and diagnosis using Time-Frquency Analysis and wavelet threshold de-noising based on EMD [J]. Journal of Vibration and Shock, 2012,31(8):96-106.
[12] Segata N, Blanzieri E. Operators for transforming kernels into quasi-local kernels that improve SVM accuracy[J]. Journal of Intelligent Information Systems, 2011, 37(2): 155-186.
[13] Diakoulaki D, Mavrotas G, Papayannakis L. Determining objective weights in multiple criteria problems: the critic method[J]. Computers and Operations Research,1995(7): 178-182.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}