Journal of Powder Metallurgy & Mining
Open Access

Research Article

A Case Study of Assessing Button Bits Failure through Wavelet Transform Using Rock Drilling Induced Noise Signals

¹Graduate School of International Resource Sciences, Akita University, Japan

²Department of Mining Engineering & Metallurgical Engineering, Western Australia School of mines, Curtin University, Australia

³Mitsubishi Materials Corporation, Japan

⁴Graduate School of Engineering Science, Akita University, Japan

⁴Department of Intelligent Interaction Technologies, University of Tsukuba, Japan

*Corresponding Author:

Kawamura Y
Graduate School of International Resource Sciences
Akita University, Japan
Tel: +81 (18) 889 2258
E-mail: kawamura@iit.tsukuba.ac.jp

Received Date: March 20, 2017; Accepted Date: March 24, 2017; Published Date: March 28, 2017

Citation: Kawamura Y, Jang H, Hettiarachchi DS, Takarada Y, Okawa H, et al. (2017) A Case Study of Assessing Button Bits Failure through Wavelet Transform Using Rock Drilling Induced Noise Signals. J Powder Metall Min 6: 162. doi:10.4172/2168-9806.1000162

Copyright: © 2017 Kawamura Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Finding the precise moment of button breakage of bits during drilling, with the experience of drill rig operators is a serious concern for modern vibrant mining industry. This research proposed a new methodology to find the failure of button using the sound generated by rock-bit interactions. The experiment is conducted by the video and sound data recorded during a drilling process in an underground mine, that uses a Sandvik AXERA7 twin boom jumbo drill rig and Polycrystalline diamond (PCD) tapered button bits. Signal analysis techniques such as Fourier transform and Wavelet transform are utilised to analyse the hectic noise signal recorded. The analysed results are shown that Wavelet Transform is much more effective in finding singularity points such as chipping or breakage of a button in compared to the Fourier Transform. The outcome of this analysis, which is the peak intensity at the breakage point, was correlated to the average intensity of the sound wave using moving average method. The results suggest that the noise generated during the drilling process can be used to detect the condition of the drill bit.

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