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Related Experiment Video

Updated: May 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Intelligent Localization of Cross-Sectional Structural Damage in Molten Salt Receiver Tubes Using Mel Spectrograms

Peiran Leng1, Man Liang1, Weihong Sun1

  • 1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an intelligent deep learning framework for precise defect localization in concentrated solar power (CSP) receiver tubes using ultrasonic guided waves. The advanced Mel+2D-CNN model significantly improves accuracy and robustness in non-destructive testing (NDT).

Area of Science:

  • Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Non-destructive testing (NDT) of receiver tubes in concentrated solar power (CSP) stations faces challenges in accurate defect localization using ultrasonic guided waves.
  • Existing methods using 1D convolutional neural networks (1D-CNN) exhibit limitations in accuracy and robustness.

Purpose of the Study:

  • To propose an intelligent localization framework using deep learning to enhance defect identification and localization accuracy in CSP receiver tubes.
  • To develop a robust method for precise defect interval localization in slender structures.

Main Methods:

  • A 1D-CNN was used for initial signal processing and coarse defect localization.
  • Mel spectrograms transformed 1D signals into 2D representations for richer feature extraction.
Keywords:
Mel spectrogramTree Seed Algorithm (TSA)molten salt receiver tubetwo-dimensional convolutional neural network (2D-CNN)ultrasonic guided wave defect localization

Related Experiment Videos

Last Updated: May 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

  • A regression-based 2D-CNN predicted defect start and end points for precise localization.
  • The Tree Seed Algorithm (TSA) optimized hyperparameters for improved training efficiency and prediction accuracy.

    • Main Results:

    • The TSA-optimized Mel+2D-CNN model achieved a Mean Absolute Error (MAE) of 75.11 sampling points and R2 of 0.90.
    • The model demonstrated a hit rate of 89.21% at an Intersection over Union (IoU) threshold of 0.3.
    • The proposed method showed significantly higher localization accuracy and stability compared to the 1D-CNN baseline.

    Conclusions:

    • The proposed deep learning framework effectively enhances the accuracy and robustness of guided wave-based defect localization in slender structures.
    • The TSA-optimized Mel+2D-CNN model offers superior performance for NDT of CSP receiver tubes.
    • Future work will focus on validating the model's generalization capability and engineering applicability across diverse industrial environments.