Abstract
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.