Incorporating Inductive Bias into Deep Learning

Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance

Ewald, Vincentius (TU Delft Structural Integrity & Composites)
Goby, Xavier (Student TU Delft)
Jansen, Hidde (Student TU Delft)
Groves, R.M. (TU Delft Structural Integrity & Composites)
Benedictus, R. (TU Delft Aerospace Structures & Materials; TU Delft Structural Integrity & Composites)

Aerospace Structures & Materials

2018

The near-term artificial intelligence, commonly referred as ‘weak AI’ in the last couple years was achieved thanks to the advances in machine learning (ML), particularly deep learning, which has currently the best in-class performance outperforming other machine learning algorithms. In the deep learning framework, many natural tasks such as object, image, and speech recognition that were impossible to be performed by classical ML algorithms in the previous decades can now be be done by typical home personal computer. Deep learning requires large amount of data that has to be rapidly collected (also known as ‘big data’) in order to create robust model parameters that are able to predict future occurrences of certain event. In some domains, a large dataset such as CIFAR-10, MNIST, or Kaggle exist already. However, in many other domains such as aircraft visual inspection, such a large dataset is not easily available and this clearly restricts deep learning to perform well to recognize material damage in aircraft structures. As many computer science researchers believe, we also think that in order to achieve a performance similar to human-level intelligence, AI could and should not start from scratch. Introducing an inductive bias into deep learning might be one solution to achieve that humanlevel intelligence. In this paper, we give an example how to incorporate aerospace domain knowledge into the development of deep learning algorithms. We performed a relatively simple procedure: we conducted fatigue testing of an aluminum plate that is typically used in aircraft fuselage and build a deep convolutional neural network that classifies crack length according to crack propagation curve obtained from fatigue test. The results of this network are then compared to the results of the same network that was not injected by domain knowledge

Automated Visual Inspection

http://resolver.tudelft.nl/uuid:5118891b-9382-4aa0-ada4-e526096c51aa

2021-12-22

10th International Symposium on NDT in Aerospace: Dresden, Germany

10th International Symposium on NDT in Aerospace, 2018-10-24 → 2018-10-26, Dresden, Germany

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2018 Vincentius Ewald, Xavier Goby, Hidde Jansen, R.M. Groves, R. Benedictus