Processing for Improved Spectral Analysis
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The Fast Fourier Transform (FFT) is the workhorse of condition monitoring analysis. The FFTs’ assumption of stationarity is often violated in rotating machinery. Even in a six second acquisition on a wind turbine, the shaft speed can change by 5%. For Shaft and Gear analysis, this is mitigated through the use of the time synchronous average. For general spectrum analysis, or bearing envelope analysis, there is no such mitigation: one hopes that the effect of variation in shaft speed is small. Presented is a time synchronous resampling algorithm which corrects for variation in shaft speed, preserving the assumption of stationarity. This allows for improved spectral analysis, such as used in bearing fault detection. This is demonstrated on a real world-bearing fault.
How to Cite
##plugins.themes.bootstrap3.article.details##
Spectral Analysis, Bearing Analysis, Stationarity, FFT, Resampling
Bechhoefer, E., He, D., (Bechhoefer 2008), Bearing Prognostics using HUMS Condition Indicators, American Helicopter Society 64th Annual fourm, Montreal.
McFadden, P., Smith, J., (McFadden 1985), A Signal Processing Technique for detecting local defects in a gear from a signal average of the vibration. Proc Instn Mech Engrs.
McFadden, P., (McFadden 1987) “A revised model for the extraction of periodic waveforms by time domain averaging”, Mechanical Systems and Signal Processing 1 (1) 1987, pages 83-95
Bechhoefer, E., Kingsley, M. (Bechhoefer 2009a). “A Review of Time Synchronous Average Algorithms”. Annual Conference of the Prognostics and Health Management Society
Christian, K., Mureithi, N, Lakis, A., Thomas, M., (Christian, 2007), “On the use of Time Synchronous Averaging, Independent Component Analysis and Support Vector Machines for
Bearing Fault Diagnosis”, First International Conference on Industrial Risk Engineering, Montreal, Dec 17-19 Montreal.
Bechhoefer, E., Kingsley, M., Menon, P., (Bechhoefer, 2009b), “Bearing Envelope Analysis Window Selection Using Spectral Kurtosis Techniques”, IEEE PHM Conference, 2011.
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.