Abstract
Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills.
Highlights
Drilling is of the utmost importance in underground mining and surface mining, since minerals are extracted from the earth’s surface by drilling blast holes in hard rock using rotary percussion drilling methods
Abnormalities often occur during drilling, such as wear and abrasion of the drill bit buttons due to excessive feed force and deviation of the drill hole trajectory. These abnormalities decrease drilling efficiency and increase drilling costs; for instance, cracks in the drill bit can cause thermal complications leading to loosening of the bit and rod, which often results in other parts such as the rod and shank failing [2]. These abnormalities are often detected by operators based on their sensory judgment and experience, which is an unreliable method of detecting drill bit failure
Due to the success of these studies, this study proposes a study, Ince et al [17] proposed a method based on a compact 1D CNN to detect a potential novel and reliable method to detect bit failure in rotary percussion drills anomalies using 1Din realmotor anomaly due drill to bearing faults
Summary
Accepted: 8 November 2021Drilling is of the utmost importance in underground mining and surface mining, since minerals are extracted from the earth’s surface by drilling blast holes in hard rock using rotary percussion drilling methods. The downhole blows to the rock are delivered by the bit while a rotational device ensures that the bit impacts a new rock surface with each blow This drilling method can be employed in both hard and soft rocks [1]. Abnormalities often occur during drilling, such as wear and abrasion of the drill bit buttons due to excessive feed force and deviation of the drill hole trajectory These abnormalities decrease drilling efficiency and increase drilling costs; for instance, cracks in the drill bit can cause thermal complications leading to loosening of the bit and rod, which often results in other parts such as the rod and shank failing [2]. The operating costs of this drilling method are likely to vary depending on Published: 12 November 2021
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