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Related Concept Videos

Fatigue01:21

Fatigue

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Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
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Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

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Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Fatigue Crack Evaluation with the Guided Wave-Convolutional Neural Network Ensemble and Differential Wavelet

Jian Chen1, Wenyang Wu1, Yuanqiang Ren1

  • 1Research Center of Structural Health Monitoring and Prognosis, State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, No. 29 Yudao Street, Nanjing 210016, China.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary

This study introduces a novel framework for online fatigue crack evaluation using guided wave (GW) structural health monitoring (SHM). The method employs a convolutional neural network (CNN) ensemble and differential wavelet spectrograms for accurate crack length determination.

Keywords:
convolutional neural network ensemblefatigue crack evaluationguided wavetime-frequency spectrogram

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Area of Science:

  • Structural Health Monitoring (SHM)
  • Non-destructive Testing (NDT)
  • Signal Processing

Background:

  • Online fatigue crack evaluation is vital for structural safety and cost-effective maintenance of critical systems.
  • Guided wave (GW)-based SHM is a promising technique, but traditional methods rely on expert-defined features susceptible to uncertainties.
  • Existing machine learning approaches also require manual feature engineering, limiting their robustness.

Purpose of the Study:

  • To develop an automated and robust framework for online fatigue crack evaluation using GW-based SHM.
  • To overcome the limitations of manual feature extraction in traditional and machine learning methods for GW analysis.
  • To accurately determine fatigue crack lengths in complex structures.

Main Methods:

  • A novel framework combining a convolutional neural network (CNN) ensemble with differential wavelet spectrograms is proposed.
  • Complex Gaussian wavelet transform is used to generate differential time-frequency spectrograms from baseline and monitoring GW signals.
  • An ensemble of CNNs is trained to directly process these spectrograms for crack length estimation.

Main Results:

  • The proposed method achieved a root mean square error (RMSE) of 1.4 mm on the training dataset.
  • Validation on complex lap joint structures yielded an RMSE of 1.7 mm for evaluated crack lengths.
  • The framework demonstrated effectiveness in real-world fatigue tests.

Conclusions:

  • The GW-CNN ensemble with differential wavelet spectrograms offers an effective and automated solution for online fatigue crack evaluation.
  • This approach reduces reliance on expert knowledge and enhances robustness against uncertainties in SHM.
  • The method shows significant potential for improving structural safety and reducing maintenance costs in safety-critical applications.