Compound damage detection using wavelet transform and deep neural network trained on healthy and single damage states: Validation on a laboratory-scale offshore jacket model

Structural Health Monitoring, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

Structural health monitoring is vital for the early detection of damage, enabling effective life cycle management structures. Detecting compound where multiple types damage occur simultaneously in different sections a structure, particularly challenging, especially when some damages are subtle or minor. Existing methods typically treat as distinct category, separate from single types. This paper introduces novel approach to based solely on vibration responses, combining wavelet transform with deep convolutional neural network interference (MIDCNN). In this approach, MIDCNN trained using time-frequency data healthy and states, intentionally excluding training phase. During testing, model accurately distinguishes between healthy, untrained states output probabilities meet predefined conditions. The method validated laboratory-scale offshore jacket structure. results demonstrate method’s ability extract relevant features classify structural including single, damage.

Language: Английский

Deep learning-based adaptive denoising method for prediction of crack opening displacement of rock from noisy strain data

International Journal of Rock Mechanics and Mining Sciences, Journal Year: 2025, Volume and Issue: 190, P. 106112 - 106112

Published: April 10, 2025

Language: Английский

A novel vibration feature information identification of pumping station based on WT-IVMD

Journal of Civil Structural Health Monitoring, Journal Year: 2025, Volume and Issue: unknown

Published: May 22, 2025

Language: Английский