Drill bit wear monitoring and failure prediction for mining automation  

作　　者：Hamed Rafezi Ferri Hassani 

机构地区：[1]Department of Mining and Materials Engineering,McGill University,Montreal H3A 0G4,Canada

出　　处：《International Journal of Mining Science and Technology》2023年第3期289-296,共8页矿业科学技术学报（英文版）

基　　金：The authors appreciate generous supports from Canada Natural Sciences and Engineering Research Council,McGill University Engine Centre as well as Faculty of Engineering.

摘　　要：This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.

关 键 词：Drilling vibration Condition monitoring Failure prediction Bit wear Wavelet energy Mining automation 

分 类 号：TD42[矿业工程—矿山机电]