Autonomous Bearing Tone Tracking Algorithm
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
To date, much of the research done in the field of condition monitoring of rotating machinery is conducted in the frequency domain. The frequency domain analysis specifically for bearings is based on extracting features from the spectrum of the vibration signature. These features are mostly based on the amplitude at the bearing tones along with their sidebands and high order harmonics. Therefore, it is important to determine the location of the mentioned bearing tones in the spectrum accurately and automatically. For the case of ball bearings this process can be problematic due to slippage of the rolling elements and variations in the angle of contact. These may cause the bearing tone to deviate from its nominal value.
To this day, the common practice for bearing diagnostics is based on the vibration level at the analytical bearing tones or involvement of experts to identify the true location of the bearing tone. In this research an autonomous algorithm for bearing tone extraction, based on pattern matching, was developed. The proposed algorithm is based on the common assumption that the spectrum of a faulted bearing contains a certain known pattern of prominent peaks. The algorithm “scans” the entire spectrum and determines the frequency value which has the highest correlation to the mentioned pattern.
The proposed algorithm was validated and its capabilities are illustrated using experimental data. This algorithm is able to assist any diagnostic approach towards automatic and reliable feature extraction process, both for physics based and data driven approaches.
How to Cite
##plugins.themes.bootstrap3.article.details##
Condition monitoring, Diagnostics, Vibration signals, Bearing tone extraction, Frequency domain
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.