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Development of a Tool Condition Monitoring System for Impregnated Diamond Bits in Rock Drilling Applications

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Abstract

The great success and widespread use of impregnated diamond (ID) bits are due to their self-sharpening mechanism, which consists of a constant renewal of diamonds acting at the cutting face as the bit wears out. It is therefore important to keep this mechanism acting throughout the lifespan of the bit. Nonetheless, such a mechanism can be altered by the blunting of the bit that ultimately leads to a less than optimal drilling performance. For this reason, this paper aims at investigating the applicability of artificial intelligence-based techniques in order to monitor tool condition of ID bits, i.e. sharp or blunt, under laboratory conditions. Accordingly, topologically invariant tests are carried out with sharp and blunt bits conditions while recording acoustic emissions (AE) and measuring-while-drilling variables. The combined output of acoustic emission root-mean-square value (AErms), depth of cut (d), torque (tob) and weight-on-bit (wob) is then utilized to create two approaches in order to predict the wear state condition of the bits. One approach is based on the combination of the aforementioned variables and another on the specific energy of drilling. The two different approaches are assessed for classification performance with various pattern recognition algorithms, such as simple trees, support vector machines, k-nearest neighbour, boosted trees and artificial neural networks. In general, Acceptable pattern recognition rates were obtained, although the subset composed by AErms and tob excels due to the high classification performances rates and fewer input variables.

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Abbreviations

AE:

Acoustic emissions

AErms :

Acoustic emission root-mean-square value

AI:

Artificial intelligence

ANN:

Artificial neural networks

b :

Bias parameters in SVM

BT:

Boosted trees

d :

Depth of cut

DAQ:

Data acquisition card

ID:

Impregnated diamond

K :

Number of points inside the selected region for KNN method

KNN:

K-nearest neighbour

MLP:

Multi-layered perceptron

MWD:

Measuring while drilling

n :

Number of drilling tests

P(X):

The probability density in the multi-dimensional spaces of KNN

SE:

Specific energy

ST:

Simple trees

SVM:

Support vector machines

tob:

Torque-on-bit

V :

Rate of penetration

V :

Volume of the selected region in KNN method

wob:

Weight-on-bit

X :

Multidimensional input vector

Z :

Number of points within an AE signal

Θ :

Weight coefficients vector

φ(X):

Fixed feature-space transformation

Ω :

Rotary speed

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Acknowledgements

The work has been supported by the Deep Exploration Technologies Cooperative research Centre whose activities are funded by the Australian Government’s Cooperative Research Centre Programme.

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Authors and Affiliations

  1. Deep Exploration Technologies Cooperative Research Centre, School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, 5005, Australia

    Santiago Perez & Murat Karakus

  2. Geosciences and Geoengineering Department, MINES ParisTech, Fontainebleau, France

    Frederic Pellet

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  1. Santiago Perez
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Correspondence to Santiago Perez.

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Perez, S., Karakus, M. & Pellet, F. Development of a Tool Condition Monitoring System for Impregnated Diamond Bits in Rock Drilling Applications. Rock Mech Rock Eng 50, 1289–1301 (2017). https://doi.org/10.1007/s00603-016-1150-6

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