Flaking is typical failure mode in rolling bearings. Therefore, flaking diagnosis plays a critical role in condition monitoring of general rotating machinery. In recent years, there has been an increasing interest in deep learning technique for bearing flaking diagnosis, because it can learn the flaking induced vibration features with no information of bearing specifications nor that of rotating speed. However, most of the studies have only focused on laboratory data using one test rig as well as a small dataset under the limited operating condition. Accordingly, no discussion has been found on the generalization performance of the diagnostic model, i.e., availability for actual rotating machinery, in which vibration feature is affected by various operating conditions and unknown disturbance. In this study, more than 21,000 timeseries waveforms of normal and bearing flaking induced machine vibration were prepared from three types of test rig and three bearing types under various operating condition. And deep learning such as Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) models were applied to recognize flaking bearing vibration. The applied models trained with various condition data showed higher accuracy of various condition test data diagnosis than other models trained using single condition data. Furthermore, the applied diagnostic models also showed less accuracy degradation for test data in which additional artificial noise was imposed, than the models trained with single condition data.
How to Cite
machine health monitoring; bearing; flaking; diagnosis; deep learning;
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.