School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel). 2022 Apr 22;22(9):3234. doi: 10.3390/s22093234.
An innovative monitoring-while-drilling method of pressure relief drilling was proposed in a previous study, and the periodic appearance of amplitude concentrated enlargement zone in vibration signals can represent the drilling depth. However, there is a lack of a high accuracy model to automatically identify the amplitude concentrated enlargement zone. So, in this study, a neural network model is put forward based on single-sensor and multi-sensor prediction results. The neural network model consists of one Deep Neural Network (DNN) and four Long Short-Term Memory (LSTM) networks. The accuracy is only 92.72% when only using single-sensor data for identification, while the proposed multiple neural network model could improve the accuracy to being greater than 97.00%. In addition, an optimization method was supplemented to eliminate some misjudgment due to data anomalies, which improved the final accuracy to the level of manual recognition. Finally, the research results solved the difficult problem of identifying the amplitude concentrated enlargement zone and provided the foundation for automatically identifying the drilling depth.
先前的一项研究提出了一种创新的随钻监测减压钻井方法，振动信号中振幅集中放大区的周期性出现可以代表钻进深度。然而，缺乏一种高精度的模型来自动识别振幅集中放大区。因此，本研究提出了一种基于单传感器和多传感器预测结果的神经网络模型。该神经网络模型由一个深度神经网络（DNN）和四个长短时记忆（LSTM）网络组成。仅使用单传感器数据进行识别时，准确率仅为 92.72%，而提出的多神经网络模型可以将准确率提高到 97.00%以上。此外，还补充了一种优化方法，以消除由于数据异常导致的一些误判，最终准确率达到了人工识别的水平。最后，研究结果解决了识别振幅集中放大区的难题，为自动识别钻进深度提供了基础。
