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Related Experiment Video

Updated: Jan 29, 2026

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

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Published on: January 16, 2019

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks.

Asaad Migot^1,2, Ahmed Saaudi^3,2, Roshan Joseph⁴

¹Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

This study uses deep learning and acoustic emission (AE) signals to estimate fatigue crack length in metal plates. Transfer learning with convolutional neural networks (CNNs) achieved 99% accuracy, enhancing structural health monitoring.

Keywords:

CNN SHM acoustic emission crack length estimation deep learning fatigue crack growth

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Area of Science:

Materials Science
Mechanical Engineering
Artificial Intelligence

Background:

Fatigue crack propagation is a critical failure mechanism in engineering structures.
Effective monitoring is essential for timely maintenance and preventing catastrophic failures.
Acoustic emission (AE) signals offer a promising non-destructive method for detecting and analyzing crack growth.

Purpose of the Study:

To develop a deep learning framework for estimating fatigue fracture length in metallic plates using AE signals.
To investigate the effectiveness of convolutional neural networks (CNNs) and transfer learning for analyzing AE data.
To enhance structural health monitoring capabilities through data-driven approaches.

Main Methods:

AE waveforms were transformed into time-frequency images using the Choi-Williams distribution.
A CNN-based model was used for feature extraction, and K-means clustering categorized fatigue lengths.
Transfer learning models (ResNet50V2, VGG16) were compared against a custom CNN for fracture length classification.

Main Results:

The AE dataset was successfully clustered, visualizing data point proximity using PCA.
CNN models accurately categorized fracture lengths into three distinct ranges.
Transfer learning models achieved significantly higher accuracy (approx. 99%) compared to the custom CNN (approx. 93%).

Conclusions:

Deep learning, especially with transfer learning, is highly effective for analyzing AE data.
CNNs demonstrate strong capabilities in understanding AE signals for fatigue crack monitoring.
The proposed framework advances data-driven structural health monitoring for metallic components.