Structural Damage Identification Using Artificial Neural Network and Synthetic data

TL;DR

This paper introduces a real-time structural damage detection method using neural networks trained on PCA-reduced frequency response data, effectively localizing and assessing damage severity in stiffened panels.

Contribution

It combines ANN and PCA techniques for damage identification, improving robustness and accuracy in noisy industrial environments.

Findings

Accurately localizes damage in stiffened panels

Predicts damage severity with high reliability

Effective on both numerical and experimental data

Abstract

This paper presents real-time vibration based identification technique using measured frequency response functions(FRFs) under random vibration loading. Artificial Neural Networks (ANNs) are trained to map damage fingerprints to damage characteristic parameters. Principal component statistical analysis(PCA) technique was used to tackle the problem of high dimensionality and high noise of data, which is common for industrial structures. The present study considers Crack, Rivet hole expansion and redundant uniform mass as damages on the structure. Frequency response function data after being reduced in size using PCA is fed to individual neural networks to localize and predict the severity of damage on the structure. The system of ANNs trained with both numerical and experimental model data to make the system reliable and robust. The methodology is applied to a numerical model of stiffened panel structure, where damages are confined close to the stiffener. The results showed that, in all the cases considered, it is possible to localize and predict severity of the damage occurrence with very good accuracy and reliability.