Application and comparison of feature-based classification models for multistable impact motions of percussive drilling
Application and comparison of feature-based classification models for multistable impact motions of percussive drilling
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DOI：
10.1016/j.jsv.2021.116205
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发表时间：
2021-09
影响因子：
4.7
通讯作者：
K. O. Afebu;Yang Liu;E. Papatheou
中科院分区：
文献类型：
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作者：
K. O. Afebu;Yang Liu;E. Papatheou
冲击钻进过程中钻头-岩石相互作用的动力学过程常常会遇到多稳定性问题，这种多稳定性会在各种钻进条件下产生共存的冲击运动。其中一些可能对它的性能有害，因为它穿过不均匀的岩层。一个必要的缓解措施是能够区分共存的冲击运动，以保持高性能的钻井。为此，在这项研究中，通过使用机器学习技术，探讨了振动冲击系统的动态响应模拟钻头-岩石相互作用的冲击钻进。作为改进机器学习的基本方法，进行了手工和自动特征提取。从模拟数据的结果表明，提取适当的功能和使用合适的网络是必不可少的特征的振动碰撞运动。提取统计，梯度直方图，连续小波变换和预训练的卷积网络特征是有效的，计算量较小。由于其高精度，它们成为设计冲击钻井多稳态振动-冲击运动分类模型时的首要考虑因素。
Dynamics of the bit-rock interaction under percussive drilling often encounter multistability that produces coexisting impact motions for a wide range of drilling conditions. Some of them may be detrimental to its performance as it cuts through the inhomogeneous rock layers. A necessary mitigation is the ability to distinguish between coexisting impact motions in order to maintain a high-performance drilling. For this purpose, dynamical responses of a vibro-impact system mimicking the bit-rock interaction of percussive drilling were explored in this study by using machine learning techniques. As a fundamental approach of improving machine learning, hand-crafted and automatic feature extractions were carried out. Results from the simulated data show that extracting appropriate features and using a suitable network are essential for characterising the vibro-impact motions. Extracting statistical, histogram of gradients, continuous wavelet transform and pre-trained convolutional network features are effective and less computationally intensive. With their high accuracies, they become the first point of consideration when designing the classification model for multistable vibro-impact motions of percussive drilling.