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

## TL;DR

This paper presents a deep learning method using acoustic emissions to estimate fatigue crack lengths in metallic plates with high accuracy.

## Contribution

A novel CNN-based framework using transfer learning for fatigue crack length estimation from acoustic emission data is introduced.

## Key findings

- Transfer learning models achieved 99% accuracy in categorizing crack lengths, outperforming custom CNNs.
- AE signals transformed into time-frequency images enabled effective clustering and classification of crack propagation data.
- PCA reduced feature dimensions, revealing close data point clustering in AE-based fatigue analysis.

## Abstract

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis (PCA) is used to reduce the feature vectors to two dimensions for display. The results show how close together the data points are in the clusters. Second, convolutional neural network (CNN) models are trained using the AE dataset to categorize fracture lengths into three separate ranges. Using the pre-trained models ResNet50V2 and VGG16, we compare the performance of a bespoke CNN using transfer learning. It is clear from the data that transfer learning models outperform the custom CNN by a wide margin, with an accuracy of approximately 99% compared to 93%. This research confirms that convolutional neural networks (CNNs), particularly when trained with transfer learning, are highly successful at understanding AE data for data-driven structural health monitoring.