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Percussion is a fundamental technique used to assess the liver, spleen, and abdominal organs by tapping the abdomen and interpreting the resulting sounds. This method helps identify fluid, distention, and masses through variations in sound, such as the high-pitched tympany of air-filled areas and the dullness of solid masses. Understanding how to percuss these organs provides valuable information for healthcare professionals in diagnosing conditions early.
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The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
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Percussion and PSO-SVM-Based Damage Detection for Refractory Materials.

Dan Yang1,2, Yi Peng1,3, Ti Zhou4

  • 1Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.

Micromachines
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

A new method uses percussion sound analysis and a Particle Swarm Optimization-Support Vector Machine (PSO-SVM) to detect damage in refractory materials. This approach achieves over 97% accuracy in identifying five damage levels, ensuring material safety.

Keywords:
damage detectionhistogram of oriented gradient (HOG)local binary patterns (LBP)mel spectrogramparticle swarm optimization (PSO)percussionrefractory materialssupport vector machine (SVM)

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Signal Processing

Background:

  • Refractory materials are crucial for industrial furnaces and share microstructural similarities with micro/nanodevices.
  • Damage in refractories compromises mechanical properties, shortens service life, and poses safety risks.

Purpose of the Study:

  • To propose a novel method for detecting damage in refractory materials using percussion-induced sound analysis.
  • To evaluate the effectiveness of a Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm for damage classification.

Main Methods:

  • Percussion was applied to refractory materials, and the resulting sound signals were recorded.
  • Mel spectrograms were generated from sound signals, and texture features were extracted using Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG).
  • Fused LBP and HOG features were input into a PSO-SVM classifier for damage detection.

Main Results:

  • The proposed method successfully identified five distinct degrees of damage in refractory materials.
  • The damage detection accuracy rate exceeded 97%.

Conclusions:

  • The percussion and PSO-SVM-based method demonstrates high potential for practical applications in refractory material damage detection.
  • The methodology is adaptable for damage detection in other materials relevant to micro/nanodevices.