| 陈旭,郑晓亮,薛生,庞靖煜,杨志强.惯性数据驱动的钻孔工序识别方法[J].电子测量与仪器学报,2026,40(1):279-288 |
| 惯性数据驱动的钻孔工序识别方法 |
| Method for recognizing drill tool actions using inertial response of the drill tail |
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| DOI： |
| 中文关键词: 智能钻探 钻孔工序识别 惯性感知 BiLSTM Transformer |
| 英文关键词:intelligent drilling drilling process identification inertial perception BiLSTM Transformer |
| 基金项目:煤炭安全精准开采国家地方联合工程研究中心开放基金（EC2021003）、中国中煤能源集团2021年重大专项（ZMYHT*AJ-W-WSZNCC-02-21-090）资助 |
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| Author | Institution |
| Chen Xu | 1.State Key Laboratory of Safe Mining of Deep Coal and Environmental Protection, Anhui University of Science
and Technology, Huainan 232001, China; 2.School of Safety Science and Engineering, Anhui University of Science
and Technology, Huainan 232001, China, China |
| Zheng Xiaoliang | 1.State Key Laboratory of Safe Mining of Deep Coal and Environmental Protection, Anhui University of Science
and Technology, Huainan 232001, China; 3.Joint National-Local Engineering Research Centre for
Safe and Precise Coal Mining, Anhui University of Science and Technology,Huainan 232001, China |
| Xue Sheng | 1.State Key Laboratory of Safe Mining of Deep Coal and Environmental Protection, Anhui University of Science
and Technology, Huainan 232001, China; 2.School of Safety Science and Engineering, Anhui University of Science
and Technology, Huainan 232001, China, China; 3.Joint National-Local Engineering Research Centre for
Safe and Precise Coal Mining, Anhui University of Science and Technology,Huainan 232001, China |
| Pang Jingyu | 1.State Key Laboratory of Safe Mining of Deep Coal and Environmental Protection, Anhui University of Science
and Technology, Huainan 232001, China; 2.School of Safety Science and Engineering, Anhui University of Science
and Technology, Huainan 232001, China, China |
| Yang Zhiqiang | Joint National-Local Engineering Research Centre for
Safe and Precise Coal Mining, Anhui University of Science and Technology,Huainan 232001, China |
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| 中文摘要: |
| 煤矿钻孔施工场景复杂,受强光水雾与粉尘的干扰,难以准确识别钻孔工序,为提升煤矿钻孔施工过程中的钻孔工序智能识别水平,提出一种基于惯性数据驱动的钻孔工序识别方法。首先,分析了钻孔过程中各工序的钻尾惯性响应特性,研制了惯性响应测量短节,给出了短节部署方案;其次,引入了双向长短时记忆（BiLSTM）网络提取惯性数据的时序特征,改进了经典Transformer Encoder网络,构造了针对惯性双模态的Transformer（DMT）特征提取网络,设计了惯性双模态数据的BiLSTM-DMT网络来提取惯性数据的特征,实现钻孔工序智能感知与识别;最后,开展了井下实测任务,得到了7类典型钻孔施工工序的惯性数据集,优选了BiLSTM网络的神经元数量,对比分析了多种融合模型的识别效果。实验结果表明,所提方法在训练实例中实现了98.87%的工序识别准确率,在工程实例中实现了96.26%的准确率,明显优于其他对比算法,说明方法能够有效地识别钻孔施工工序,且具备较高的识别精度和鲁棒性,为煤矿钻孔施工中的智能钻孔工序识别发展提供了新的技术路径。 |
| 英文摘要: |
| The complex working environment of coal mine drilling operations, characterized by intense illumination, water mist, and dust interference, poses significant challenges for accurate identification of drilling procedures. To enhance intelligent procedure recognition during coal mine drilling operations, this study proposes an inertial data-driven drilling procedure identification method. Firstly, the inertial response characteristics of the drill tail during various procedures were systematically analyzed. An inertial response measurement module was developed and its deployment scheme formulated. Secondly, a bidirectional long short-term memory (BiLSTM) network was employed to extract temporal features from inertial data. The classical Transformer Encoder network was improved to construct a dual-modal Transformer（DMT） feature extraction network specifically for inertial data. A BiLSTM-DMT network was designed to effectively capture features from dual-modal inertial data, enabling intelligent perception and identification of drilling procedures. Finally, field measurements were conducted underground, yielding an inertial dataset encompassing seven typical drilling procedures. The neuron configuration of the BiLSTM network was optimized through comparative analysis of multiple fusion models. Experimental results demonstrate that the proposed method achieves 98.87% recognition accuracy in training instances and 96.26% in engineering applications, significantly outperforming existing comparative algorithms. This confirms the method’s effectiveness in identifying drilling procedures with high accuracy and robustness, thereby establishing a novel technical pathway for intelligent procedure recognition in coal mine drilling operations. |
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