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

Vincentius Ewald, Xavier Goby, Hidde Jansen, Roger Groves, Rinze Benedictus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

43 Downloads (Pure)

Abstract

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
Original languageEnglish
Title of host publication10th International Symposium on NDT in Aerospace
Subtitle of host publicationDresden, Germany
Number of pages9
Publication statusPublished - 2018
Event10th International Symposium on NDT in Aerospace - Dresden, Germany
Duration: 24 Oct 201826 Oct 2018
Conference number: 10
https://www.ndt-aerospace.com/

Conference

Conference10th International Symposium on NDT in Aerospace
CountryGermany
CityDresden
Period24/10/1826/10/18
Internet address

Keywords

  • Automated Visual Inspection

Fingerprint Dive into the research topics of 'Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance'. Together they form a unique fingerprint.

Cite this