| 引用本文: | 乔世范,谭晶仁,王刚,李镐羽.盾构刀具整体磨损状态识别研究[J].哈尔滨工业大学学报,2023,55(5):39.DOI:10.11918/202203069 |
| QIAO Shifan,TAN Jingren,WANG Gang,LI Haoyu.Overall wear state recognition of shield cutters[J].Journal of Harbin Institute of Technology,2023,55(5):39.DOI:10.11918/202203069 |
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| 摘要: |
| 刀具磨损情况是影响盾构机掘进效率的重要因素,也是决定开仓换刀时间和频率的关键依据。针对盾构掘进过程中刀具整体磨损状态难以判断的问题,统计换刀点每把刀具磨损量与限定磨损量之间的关系,提出了3个磨损状态等级。在推导3种关键掘进参数（推力、扭矩和掘进速度）与单把滚刀切削分力理论关系的基础上,提出一种对掘进参数信号进行小波包分解以识别刀具整体磨损状态的方法。该方法以分解后的信号节点小波包系数标准差组成的特征向量作为磨损识别指标,通过敏感性分析找出对刀具磨损最敏感的节点特征向量,进而通过拟合分析确定磨损状态与磨损识别指标的函数关系。对深圳地铁14号线大运站至宝荷站区间工程实例的分析结果表明,该方法能准确识别盾构刀具的整体磨损状态,其中使用掘进速度信号进行识别的精度最高,推力次之,扭矩最低。该方法在使用中仅需对盾构机自动采集的掘进参数进行处理分析,不需要布置传感器,具有简便易行、成本低和精度高等优点,为及时开仓换刀提供了可靠依据。 |
| 关键词: 盾构 滚刀 掘进参数 小波包分析 磨损状态识别 |
| DOI：10.11918/202203069 |
| 分类号:U455.43 |
| 文献标识码:A |
| 基金项目:博士后联合资助引进项目(YJ20210409) |
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| Overall wear state recognition of shield cutters |
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QIAO Shifan,TAN Jingren,WANG Gang,LI Haoyu
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(School of Civil Engineering, Central South University, Changsha 410075, China)
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| Abstract: |
| The wear of cutter is an important factor affecting the efficiency of shield tunneling, which is also a basis for determining the time and frequency of cutter replacement. As it is difficult to evaluate the overall wear state of the cutters in the process of shield tunneling, three wear degrees (light, moderate, and severe) were proposed based on the relationship between the wear amount of each cutter and the limited wear at the cutters change site. The theoretical relationship between three main tunneling parameters (thrust, torque, and tunneling speed) and the cutting force component of a single cutter was derived, and a method for recognizing the overall wear state of cutters was proposed by using the wavelet packet algorithm to decompose the tunneling parameter signals. In this method, the eigenvectors composed by the standard deviation of the wavelet packet coefficient of decomposed signal nodes were used as the wear recognition index. Sensitivity analysis was performed to find out the most sensitive node eigenvector of the cutter wear. Then the functional relationship between the wear state and the wear recognition index was determined by fitting. The analysis of the section from Dayun station to Baohe station of Shenzhen Metro Line 14 showed that the method could accurately recognize the overall wear state of the shield cutters. Among the three tunneling parameters, the recognition accuracy was the highest when using the tunneling speed signal, followed by the thrust signal, and the torque signal was the lowest. The proposed method is easy to use and cost-effective, since it only needs to analyze the automatically collected tunneling signals without installing any sensors, which provides reference for cutter replacement. |
| Key words: shield cutter tunneling parameters wavelet packet analysis wear state recognition |
