One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Senjoba, Lesego; Sasaki, Jo; Kosugi, Yoshino; Toriya, Hisatoshi; Hisada, Masaya; Kawamura, Y.

doi:10.3390/mining1030019

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…A stationary drifter (YH-70 Yamamoto Rock Machine Ltd., Tokyo, Japan) was used for horizontally drilling through 18 m 3 of marble rock at a sampling frequency of 50 kHz. The operational parameters are shown in Table 1, including impact pressure, striking frequency, rotation pressure, and hits per minute, and were consistent with those reported in Senjoba et al [26] Different drill bits, each with distinct conditions, were used for data collection. These included a standard bit (normal) and a bit with chipped buttons (abnormal) as shown in Figure 3.…”

Section: Data Collection and Preprocessingsupporting

confidence: 57%

“…In this study, we employed the data collection and preprocessing methodology described by Senjoba et al [26], where the temporal acceleration of drill vibrations was recorded using acceleration sensors (600 series; TEAC Corporation, Tokyo, Japan) fixed to the guide cell of a rock drill. A stationary drifter (YH-70 Yamamoto Rock Machine Ltd., Tokyo, Japan) was used for horizontally drilling through 18 m 3 of marble rock at a sampling frequency of 50 kHz.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…The ANN model was efficient in determining the physicomechanical rock properties. Senjoba et al [26] proposed a method to detect drill bit failure in rotary percussion drills using a one-dimensional convolutional neural network (1D CNN) with drill vibration as the input data. The 1D CNN model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

Senjoba,

Ikeda,

Toriya

et al. 2024

Applied Sciences

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This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

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Section: Data Collection and Preprocessingsupporting

confidence: 57%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

Senjoba,

Ikeda,

Toriya

et al. 2024

Applied Sciences

Self Cite

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“…Furthermore, deep learning based on lightweight convolutional neural networks allowed for the timely detection of the damage of machines and equipment by analyzing a large number of images (up to 100) every second with a test accuracy of 93.22% when integrating MobileNet and Yolov4 networks [79]. The use of a convolutional neural network (1D CNN) to analyze the causes of breakdowns in drilling equipment made it possible to use artificial intelligence to eliminate the human factor and search for technical and mining sources of breakdowns with an accuracy of 88.7% [80]. To eliminate the human factor and the subjectivity and bias in the planning of mining operations, as well as limit the soil, water, and air pollution by quarries and find the most effective solutions for reclamation, Kohonen's neural network has proven itself well.…”

Section: Review Of End-to-end Technologies Of Industry 40 In Surface ...mentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

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“…Therefore, an important core technology of smart machinery is how to improve equipment activation through intelligent sensing and fault diagnosis prediction, and how to reduce the risk of downtime due to equipment failure. There are many types of mechanical equipment failures, such as abrasion and damage of the processing machine's spindle cutter [1][2][3][4][5][6][7], the abnormal damage of the bearing [8][9][10][11][12] or gearbox of the rotary machinery due to the harsh environment, rotational instability caused by mechanical failures of the power generator, etc. Therefore, accurate prediction of mechanical failures will reduce production losses, a key factor, and condition for the efficient production of smart machinery.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Chen

Lin

et al. 2023

Applied Sciences

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Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

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One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Cited by 13 publications

References 27 publications

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

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