In recent years, data-driven techniques such as deep learning (DL) have been widely represented in the literature in the field of bearing vibration condition monitoring. While these approaches achieve excellent performance in classifying bearing faults on controlled laboratory data sets, there is little information available about their applicability to more realistic working conditions. As a first step towards revealing the generalizability of DL models, we aim to understand the underlying representations that DL networks use to classify bearing defects. An interpretable DL model can give us hints on how to increase its transferability by, e.g., using data augmentation, changing input representations and/or adapting model architectures. We use the Grad-CAM algorithm along with signal transformations to identify the elements of the input spectrogram that contribute to class attribution. The results show that removing time-domain information from the spectrogram has a minor impact on its performance. Instead, the network learns distinct average frequency profiles. We therefore conclude that the networks learn signal features very specific to the physical properties of the specific test setup, such as the frequency response function, rather than more general features related to bearing defects.
How to Cite
Data Driven, Deep Learning, interpretability, bearing vibration diagnosis
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.