Li Qingwei, Du Lijie, Yang Yalei, Ni Zhihua, Zhao Xiangbo
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Collaborative Innovation Center for Performance and Safety of Large-Scale Infrastructure, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Sci Rep. 2024 Dec 28;14(1):30655. doi: 10.1038/s41598-024-76172-9.
Abnormal cutter wear has a serious impact on TBM construction. If not found in time, it may lead to the cutterhead overall failure. Aiming at this problem, a general model and method to identify and warn the abnormal cutter wear using Extreme Learning Machine (ELM) is proposed. Based on multiple projects data, taking the general characteristic parameters as model inputs, the tunneling parameters in normal cutter wear are used to establish an ELM prediction model of advance speed, which predicts the abnormal cutter wear condition through the difference between predicted and actual advance speed. Through the project case, the model is verified and the early-warning threshold of abnormal cutter wear is given. The results show that the model prediction results are consistent with the actual cutter replacement situation. The method is effective and universal, which can provide a useful guidance for the identification, warning and replacement of TBM disc-cutter abnormal wear.
刀具异常磨损对隧道掘进机（TBM）施工有严重影响。若不及时发现，可能导致刀盘整体失效。针对这一问题，提出了一种利用极限学习机（ELM）识别和预警刀具异常磨损的通用模型及方法。基于多个项目数据，以一般特征参数作为模型输入，利用正常刀具磨损情况下的掘进参数建立了掘进速度的ELM预测模型，通过预测掘进速度与实际掘进速度的差值来预测刀具异常磨损状况。通过工程实例验证了该模型，并给出了刀具异常磨损的预警阈值。结果表明，模型预测结果与实际刀具更换情况一致。该方法有效且通用，可为TBM盘形滚刀异常磨损的识别、预警及更换提供有益指导。
