Towards deep convolutional neural network-based transfer learning for structural health monitoring with partial wavefield scans
 (Conference Presentation)

Xinyao Tang; Pei Cao; Joseph Melville; K. Supreet Alguri; Joel Harley; Soumyajit Mandal

doi:10.1117/12.2514405

1 April 2019 Towards deep convolutional neural network-based transfer learning for structural health monitoring with partial wavefield scans (Conference Presentation)

Xinyao Tang, Pei Cao, Joseph Melville, K. Supreet Alguri, Joel Harley, Soumyajit Mandal

Author Affiliations +

Proceedings Volume 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII; 1097110 (2019) https://doi.org/10.1117/12.2514405
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

Abstract

Traditional physical model-based nondestructive evaluation (NDE) and damage detection methods are often unreliable due to the complex dependence of model parameters on minor differences in material properties (e.g., thickness, temperature, or loading effects). While classic data-driven approaches appear to eliminate model complexity, their performance highly depends on feature extraction, for which domain-expertise-based data preprocessing is required. Wavefield analysis is a promising alternative for non-contact NDE but suffers from the problem of slow data acquisition. As a result, effective structural health monitoring (SHM) based on wavefield analysis of guided waves in large-scale systems, such as mechanical, civil, or aerospace structures, has remained challenging. To address these challenges, we present a deep convolutional neural network (DCNN)-based transfer learning approach to interpret ultrasonic guided waves with small training data sets, thereby achieving rapid, effective, and automated SHM. Specifically, the proposed learning framework includes a pre-trained DCNN for automated feature extraction from the raw inputs (i.e., wavelet-transformed time-frequency images) and a fully connected classification stage that is trained with partial wavefield scans. Experiments on full wavefield scans of various thin metal plates demonstrate the effectiveness and efficiency of the proposed approach: >95% classification accuracy is obtained with only 5% training data, thus enabling fast scanning and fully automated damage detection of large-scale structures.

Conference Presentation

Citation Download Citation

Xinyao Tang, Pei Cao, Joseph Melville, K. Supreet Alguri, Joel Harley, and Soumyajit Mandal "Towards deep convolutional neural network-based transfer learning for structural health monitoring with partial wavefield scans (Conference Presentation)", Proc. SPIE 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, 1097110 (1 April 2019); https://doi.org/10.1117/12.2514405

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Structural health monitoring

Neural networks

Damage detection

Data modeling

Feature extraction

Nondestructive evaluation

Waveguides

Show All Keywords

Keywords/Phrases

Search In:

Publication Years