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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Non destructive defect detection by spectral density analysis.

Ondrej Krejcar1, Robert Frischer

  • 1Department of Measurement and Control, CAK, FEECS, VSB Technical University of Ostrava, Ostrava, Czech Republic. ondrej.krejcar@remoteworld.net

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nondestructive diagnostic method using vibration analysis and artificial pulses. The technique achieves an 85.7% success rate in identifying object conditions, offering significant financial savings.

Keywords:
FFTMatLabStatisticadefectpower spectrum

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Signal Processing

Background:

  • Nondestructive diagnostics are crucial for assessing the integrity of solid objects.
  • Current visual comparison methods for diagnostics are often subjective and lack precision.
  • The need for automated, data-driven diagnostic techniques is increasing.

Purpose of the Study:

  • To develop and evaluate a new method for nondestructive diagnostics of solid objects.
  • To improve the accuracy and efficiency of object state assessment.
  • To demonstrate the potential for significant financial savings through early defect detection.

Main Methods:

  • Analysis of vibration power spectrum and acoustic emissions during device operation or under induced stress.
  • Utilizing artificial pulses to probe the object's condition.
  • Software-based signal processing, including power spectrum density analysis in MATLAB.
  • Data filtering and comparison using Statistica software.
  • Application of neural networks for automated sample analysis after Fast Fourier Transform (FFT).

Main Results:

  • Achieved an 85.7% success rate in approximating the condition of examined objects.
  • Demonstrated the capability to filter relevant data from large datasets generated by FFT.
  • Identified potential for further improvement in accuracy with enhanced filtering techniques.
  • Successfully detected defective conditions and predicted limiting states.

Conclusions:

  • The proposed method offers a reliable approach to nondestructive diagnostics.
  • Automated analysis using neural networks significantly enhances diagnostic capabilities.
  • Early detection of material defects can lead to substantial cost reductions, exemplified in industrial processes like iron casting.