Feature Extraction for Bearing Prognostics using Correlation Coefficient Weight

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Seokgoo Kim Chaeyoung Lim Joo-Ho Choi

Abstract

Bearing is an essential mechanical component in rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management (PHM). Key issues in bearing PHM is to establish a proper health indicator (HI) reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics. This paper proposes a feature extraction method using traditional envelope analysis and weighted sum with correlation coefficient. The developed methods are demonstrated using IMS bearing data from NASA Ames Prognostics Data Repository. In the end, proposed feature is compared with traditional time-domain features.

How to Cite

Kim, S., Lim, C., & Choi, J.-H. (2017). Feature Extraction for Bearing Prognostics using Correlation Coefficient Weight. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2456
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Keywords

bearing fault diagnosis, Vibration, Feature extraction

References
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, vol.23, pp. 724-739.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, vol. 20, pp. 1483-1510.
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.
Li, H., & Zheng, H. (2008). Bearing fault detection using envelope spectrum based on EMD and TKEO. Fuzzy Systems and Knowledge Discovery, 2008. FSKD'08. Fifth International Conference on, vol. 3, pp. 142-146
Qiu, H., Lee, J., Lin, J., & Yu, G. (2003).Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics, vol. 17, pp. 127-140
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—a tutorial. Mechanical systems and signal processing, vol. 25, pp. 485-52 Siegel, D., Lee, J., & Ly, C. (2011, June). Methodology and framework for predicting rolling element helicopter bearing failure. Prognostics and Health Management (PHM), 2011 IEEE Conference on, pp. 1-9
Tabrizi, A., Garibaldi, L., Fasana, A., & Marchesiello, S. (2015). A novel feature extraction for anomaly detection of roller bearings based on performance improved ensemble empirical mode decomposition and Teager–Kaiser energy operator. International Journal of Progn Health Management, vol. 6, pp.1-10.
Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2011). Estimation of the remaining useful life by using wavelet packet decomposition and HMMs. In Aerospace Conference, 2011, pp. 1-10
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M. E., & Vachtsevanos, G. (2011). A probabilistic fault detection approach: Application to bearing fault detection. IEEE Transactions on Industrial Electronics, vol. 58.
Section
Technical Papers

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