ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Delamination detection in aerospace composite panels using convolutional autoencoders

Rautela, M and Monaco, E and Gopalakrishnan, S (2021) Delamination detection in aerospace composite panels using convolutional autoencoders. In: Health Monitoring of Structural and Biological Systems XV 2021, 22-26 Mar 2021.

[img] PDF
SPIE_2021_Rautela2.pdf - Published Version
Restricted to Registered users only

Download (828kB)
Official URL: https://doi.org/10.1117/12.2582993


Modern aerospace structures demand lightweight design procedures and require scheduled maintenance intervals. Supervised deep learning strategies can allow reliable damage detection provided a large amount of data is available to train. These learning algorithms may face problems in the absence of possible damage scenarios in the training dataset. This class imbalance problem in supervised deep learning may curtail the learning process and can possess issues related to generalization on unseen examples. On the other hand, unsupervised deep learning algorithms like autoencoders can handle such situations in the absence of labeled data. In this study, an aerospace composite panel is interrogated with a circular array of piezoelectric transducers using ultrasonic guided waves in a round-robin fashion. The time-series signals are collected for both the healthy and unhealthy state of the structure and transformed into a time-frequency dataset using continuous wavelet transformation. A convolutional autoencoder algorithm trained on healthy signals is used to identify anomalies in the form of delamination in the structure. The proposed methodology can successfully identify delamination in the structure with good accuracy. © 2021 SPIE

Item Type: Conference Paper
Publication: Proceedings of SPIE - The International Society for Optical Engineering
Publisher: SPIE
Additional Information: The copyright for this article belongs to SPIE
Keywords: Aerospace applications; Biological systems; Convolution; Damage detection; Deep learning; Guided electromagnetic wave propagation; Learning systems; Structural health monitoring; Ultrasonic applications, Aerospace composites; Class imbalance problems; Continuous wavelet transformation; Delamination detections; Round-robin fashions; Scheduled maintenance; Time series signals; Ultrasonic guided wave, Learning algorithms
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 25 Aug 2021 12:00
Last Modified: 26 Aug 2021 05:39
URI: http://eprints.iisc.ac.in/id/eprint/69357

Actions (login required)

View Item View Item