This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations. This research addresses the increasing demand for automation in mining to increase the efficiency, safety, and ability to work in harsh environments. A crucial issue in fully autonomous unmanned drilling is to have a system to detect the bit wear condition through the drilling signals analysis in real time. In this work, based on extensive field studies, a novel qualitative method for tricone bit wear state classification is developed and introduced. The relations between drilling vibration as well as electric motor current signals and bit wear are investigated and bit failure vibration frequencies, regardless of the geological conditions, are introduced. Bit failure frequencies are experimentally investigated and analytically calculated. Finally, the effect of bit design parameters on the failure frequencies is presented for the application of bit wear condition monitoring and bit failure prediction.

本文提出了一种新颖的方法来研究现实世界中大规模采矿作业中的钻井信号与钻头磨损状况之间的关系。这项研究解决了对采矿自动化的不断增长的需求，以提高效率，安全性和在恶劣环境下工作的能力。全自动无人驾驶钻探的关键问题是拥有一个通过钻探信号分析实时检测钻头磨损状况的系统。在这项工作中，在广泛的现场研究的基础上，开发并介绍了一种新的定锥孔磨损状态分类的定性方法。研究了钻探振动以及电动机电流信号与钻头磨损之间的关系，并介绍了与地质条件无关的钻头故障振动频率。对位故障频率进行了实验研究和分析计算。最后，介绍了钻头设计参数对失效频率的影响，以用于钻头磨损状态监测和钻头失效预测。