Drill bit wear monitoring and failure prediction for mining automation
Hamed Rafezi, Ferri Hassani
Abstract
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 time-frequency 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.