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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

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Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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Updated: May 7, 2026

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
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Damage State Recognition and Quantification Method for Shield Machine Hob Based on Deep Forest.

Huawei Wang1,2, Qiang Gao3, Sijin Liu1,2

  • 1China Railway 14th Bureau Group Co., Ltd., Jinan 250011, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study introduces an intelligent method using Deep Forest (DF) models to accurately identify and quantify disc cutter damage in tunnel boring machines. The approach enhances safety and efficiency in tunnelling projects.

Keywords:
deep foresthob damagemachine learningshield machine hobsignal processingwear monitoring

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Area of Science:

  • Engineering
  • Machine Learning
  • Materials Science

Background:

  • Disc cutter damage significantly impacts tunnelling safety and efficiency.
  • Manual inspection is risky and inefficient; current detection methods lack accuracy and real-time capabilities.
  • Existing methods often fail to provide quantitative wear assessment.

Purpose of the Study:

  • To develop an intelligent method for identifying and quantitatively assessing disc cutter damage.
  • To improve the safety and efficiency of tunnelling operations through reliable cutter condition monitoring.
  • To overcome limitations of existing manual and automated inspection techniques.

Main Methods:

  • Established an eddy current sensor calibration platform for precise wear quantification.
  • Preprocessed sensor data using filtering and pulse edge detection to extract damage features.
  • Employed a Deep Forest (DF) classification model for damage state identification (normal, edge chipping, eccentric wear, cracks).
  • Utilized a DF regression model for continuous quantitative prediction of damage size.

Main Results:

  • The DF classification model achieved high accuracy (98% training, 96% validation/test) and F1-scores (>0.96).
  • The DF regression model demonstrated excellent performance with R² of 0.9940 and RMSE of 0.4051.
  • The integrated approach provides both qualitative identification and quantitative assessment of disc cutter damage.

Conclusions:

  • The proposed Deep Forest-based method offers accurate and reliable identification and quantitative assessment of disc cutter damage.
  • This intelligent system provides robust decision support for timely cutter maintenance and replacement in tunnelling projects.
  • The method demonstrates strong performance and generalization, addressing critical needs in tunnel engineering.