A New Prognostics Approach for Bearing based on Entropy Decrease and Comparison with existing Methods

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Seokgoo Kim Sungho Park Ju-Won Kim Junghwa Han Dawn An Nam Ho Kim Joo-Ho Choi

Abstract

In this paper, a new method is proposed for the bearing prognosis based on the energy entropy, in which the normalized energy in the frequency spectrum is calculated over the cycles, frequency band is selected that shows greater decrease relative to the others, and entropy is computed as a trending feature. As opposed to the traditional features, which exhibit noisy fluctuation, non-monotonic change or only an abrupt increase near the end of life, the proposed energy entropy shows the smooth and constant decrease over the cycles which may represent the degree of fault progression. In order to illustrate the advantage, four traditional features - RMS, kurtosis, MAS kurtosis and envelope and the new feature - energy entropy are examined and compared using the three cases of bearing data named FEMTO, IMS and LOCAL, all from the bearing life test.

How to Cite

Kim, S., Park, S., Kim, J.-W., Han, J., An, D., Kim, N. H., & Choi, J.-H. (2016). A New Prognostics Approach for Bearing based on Entropy Decrease and Comparison with existing Methods. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2540
Abstract 38 | PDF Downloads 37

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
An, D., Kim, N., & Choi, J ., (2016). Bearing Prognostics Method Based on Entropy Decrease at Specific Frequency. In 18th AIAA Non-Deterministic Approaches Conference (p. 1678).
Caesarendra, W., Widodo, A., & Yang, B. S. (2010). Application of relevance vector machine and logistic regression for machine degradation assessment. Mechanical Systems and Signal Processing, 24(4), 1161-1171.
FEMTO-ST, “IEEE PHM 2012 Data Challenge,” online website, last accessed on May 31, 2012. http://www.femto-st.fr/en/Researchdepartments/ AS2M/Research-groups/PHM/IEEE-PHM-2012-Datachallenge.php
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical systems and signal processing, 23(3), 724-739.
Jardine, A. K.(2006). A review on machinery diagnostic and prognostics implementing condition-based maintenance, Mechanical systems and signal processing
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, 42(1), 314-334.
Lebold, M., McClintic, K., Campbell, R., Byington, C., & Maynard, K. (2000, May). Review of vibration analysis methods for gearbox diagnostics and prognostics. In Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology (Vol. 634, p. 16).
Lee, J., Qiu, H., Yu, G., & Lin, J. (2009). Rexnord Technical Services (2007).'Bearing Data Set', IMS, University of Cincinnati. NASA Ames Prognostics Data Repository.
McInerny, S. A., & Dai, Y. (2003). Basic vibration signal processing for bearing fault detection. IEEE Transactions on education, 46(1), 149-156.
Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics .Journal of sound and vibration, 289(4), 1066-1090.
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—a tutorial. Mechanical Systems and Signal Processing, 25(2), 485-520.ISO 690 Siew, W. S., Smith, W. A., Peng, Z., & Randall, R. B. FAULT SEVERITY TRENDING IN ROLLING ELEMENT BEARINGS.
Siegel, D., Ly, C., & Lee, J. (2011). Evaluation of vibration based health assessment and diagnostic techniques for helicopter bearing components. In 2011 DSTO International Conference on Health and Usage Monitoring, Melbourne, Australia.
Sutrisno, E., Oh, H., Vasan, A. S. S., & Pecht, M. (2012, June). Estimation of remaining useful life of ball bearings using data driven methodologies. In Prognostics and Health Management (PHM), 2012 IEEE Conference on (pp. 1-7). IEEE.
Wang, T. (2012, June). Bearing life prediction based on vibration signals: A case study and lessons learned. In Prognostics and Health Management (PHM), 2012 IEEE Conference on (pp. 1-7). IEEE.
Yan, W., Qiu, H., & Iyer, N. (2008). Feature extraction for bearing prognostics and health management (phm)-a survey (preprint) (No. AFRL-RX-WP-TP-2008-4309). AIR FORCE RESEARCH LAB WRIGHT-PATTERSON AFB OH MATERIALS AND MANUFACTURING DIRECTORATE.
Section
Technical Papers

Most read articles by the same author(s)