一种新型TBM刀具磨损检测技术

A new wear detection technique for TBM cutters

随着隧道及地下空间工程的发展,TBM工法被越来越多的使用。针对TBM施工中刀具磨损更换频繁且缺乏有效方法对刀具状态进行评估问题,以TBM模态掘进试验台搭载的不同刀具为研究对象,将声发射检测、自适应卡尔曼(Kalman)滤波和一种改进的信息熵值赋权模型应用于TBM刀具状态评估和检测,基于自适应卡尔曼滤波和改进信息熵值模型的声发射多散点多参量权重融合法可有效地反映出刀具的不同磨损状态,可为TBM刀具现场检修和保养提供参考。

TBM technology is applied more and more with the development of tunnel and underground engineering.Aiming at TBM cutters' frequent wear and renewal and lack of effective methods to evaluate their states in operation,different cutters carried on a TBM modal tunneling platform were taken study objects,acoustic emission detection,adaptive Kalman filtering and an improved information entropy model were applied to evaluate TBM cutters' conditions and detect them. It was shown that the acoustic emission multi-scatter multi-parameter weight fusion method based on the adaptive Kalman filtering and the improved information entropy model can effectively reflect different cutters' wear states and provide a guide for repairing and maintaining TBM cutting tools in site.