A Review of Time Synchronous Average Algorithms
Time Synchronous Average (TSA) is an essential algorithmic tool for determining the condition of rotating equipment. Given its significance to diagnostics, it is important to understand the algorithms performance characteristics. This paper addresses four topics in relation to the TSA performance characteristics. The first topic is the evaluation of the performance (measured against gear fault detection) of 6 different TSA algorithms. The second topic is quantifying the ergodicity/noise reduction as a function of the number of revolutions in the TSA and show that noise reduction is 1/sqrt(number of revolutions). The third topic examines TSA techniques when no tachometer signal is available.The final topic shown is the distribution of the magnitude of TSA orders associated with fault and nominal components are Rice and Rayleigh distributed.
How to Cite
condition monitoring, time domain analysis
(McFadden 1987) McFadden, P., “A revised model for the extraction of periodic waveforms by time0domain averaging”, Mechanical Systems and Signal Processing 1 (1) 1987, pages 83-95
(Decker 1999) Decker, H., Zakrajsek, J., “Comparison of Interpolation Methods as Applied to Time Synchronous Averaging” ARL-TR-1960, MFPT, April 19-22, 1999
(Vachtsevanos 2006) Vachtsevanos, G., et al. Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley & Sons, Inc., 2006, page 418-419.
(Press 1992) Press, W., et al. Numerical Recipes in C, Cambridge University Press, New York, 1992, page 113 – 116.
(Vecer 2005) Vecer, P., Kreidl, M., Smid, R., “Condition Indicators for Gearbox Condition Monitoring Systems” Acta Polytechnica Vol. 45, No 6/2005.
(Proakis 1995) Proakis, John, G., Digital Communications, McGraw-Hill, Boston MA, 1995, page 45-46
(Bechhoefer 2007) Eric Bechhoefer, Andreas Bernhard, “A Generalized Process for Optimal Threshold Setting in HUMS” IEEE Aerospace Conference, Big Sky, 2007.
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.