The detection of faults and operational abnormalities in rotating machine elements like rolling element bearings and gears requires information about kinematic properties, such as ball-pass and gear mesh frequencies. Typically, condition-monitoring experts obtain such information from the manufacturers for diagnostics purposes. However, the reliability of such information can be compromised during installation and maintenance, for example, if components are replaced and do not match the documented specifications. Thus, methods enabling verification and online extraction of such kinematic properties are needed to improve diagnostic reliability. Unsupervised machine learning methods, like sparse coding with dictionary learning, enable automatic modeling and characterization of repeating signal structures in the time domain, which are naturally generated by rotating equipment. Sparse coding with dictionary learning represents a vibration signal as a linear superposition of noise and atomic waveforms. The activation rate of the atomic waveforms typically possesses a cyclic nature in rotating environments, similar to how bearing kinematic frequencies correlate with faults in a rolling element bearing. However, there is no explicit relationship between the activation rates of the atoms and the bearing kinematic frequencies. This motivates this investigation of the possibility to extract bearing kinematic frequencies from sparse representations. Former work describes the use of dictionary learning for the detection of anomalies in rolling element bearings. In this paper, we describe how a similar unsupervised machine learning method can be used to extract kinematic frequencies of bearings and gears, for example for anomaly detection purposes and comparisons with an expected signature. We study the activation rates and changes of atoms learned from vibration signals in two case studies. The first case is based on data from a well-known controlled experiment with faults seeded in the bearings. The second case is based on a public dataset recorded from the high-speed shaft of a wind turbine with a bearing failure. Furthermore, we compare the activation rates and weights of the atoms to the bearing kinematic frequencies and harmonics. Sparse coding with dictionary learning offers a possibility for self-learning of the kinematic frequencies of a bearing,
which can be useful for the further improvement of automated anomaly detection methods in condition monitoring.
How to Cite
sparse coding, dictionary learning, condition monitoring, wind turbine
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.