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Related Concept Videos

Measurement of Fluid Pressure01:16

Measurement of Fluid Pressure

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Fluid pressure is commonly measured using devices called manometers, which rely on liquid columns to indicate pressure differences. The height of a liquid column in a manometer reflects the pressure exerted by the fluid, providing a simple yet effective means of measurement. Different types of manometers serve specific purposes based on their configurations and the type of fluids involved.
A basic form of manometer is the piezometer, a vertical tube open at the top and filled with the same...
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Design Example: Maintaining Level of an Embankment01:19

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Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
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Related Experiment Video

Updated: Sep 22, 2025

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Automatic Implementation Algorithm of Pressure Relief Drilling Depth Based on an Innovative Monitoring-While-Drilling

Zheng Wu1, Wen-Long Zhang1, Chen Li1

  • 1School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary

A new neural network model accurately identifies drilling depth using vibration signals. This method improves upon single-sensor approaches, enhancing accuracy for real-time drilling monitoring.

Keywords:
drilling depthdrilling state identification algorithmmonitoring-while-drilling methodneural networkvibration signals

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Area of Science:

  • Geotechnical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Monitoring-while-drilling (MWD) methods are crucial for efficient drilling operations.
  • Identifying the amplitude concentrated enlargement zone in vibration signals indicates drilling depth.
  • Existing methods lack high accuracy in automatically identifying this zone.

Purpose of the Study:

  • To develop a high-accuracy model for automatic identification of the amplitude concentrated enlargement zone.
  • To improve the accuracy of determining drilling depth from vibration signals.
  • To provide a foundation for automated drilling depth identification.

Main Methods:

  • Proposed a novel neural network model combining Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) networks.
  • Utilized both single-sensor and multi-sensor data for prediction.
  • Implemented an optimization method to mitigate data anomalies.

Main Results:

  • Single-sensor identification accuracy reached 92.72%.
  • The proposed multi-sensor neural network model achieved accuracy greater than 97.00%.
  • Optimization improved accuracy to the level of manual recognition.

Conclusions:

  • The developed neural network model effectively identifies the amplitude concentrated enlargement zone.
  • This research solves a key challenge in automated drilling depth identification.
  • The findings support the advancement of MWD technologies.