Drill bit wear monitoring and failure prediction for mining automation

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 i...

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Bibliographic Details
Published inInternational journal of mining science and technology Vol. 33; no. 3; pp. 289 - 296
Main Authors Rafezi, Hamed, Hassani, Ferri
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2023
Elsevier
Subjects
Bit wear
Condition monitoring
Drilling vibration
Failure prediction
Mining automation
Wavelet energy
Condition monitoring
Wavelet energy
Failure prediction
Bit wear
Mining automation
Drilling vibration
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Summary: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.
ISSN:2095-2686
DOI:10.1016/j.ijmst.2022.10.006