Condition Monitoring of Rolling Element Bearing Based on Moving Average Cross-Correlation of Power Spectral Density
The purpose of bearing condition monitoring (CM) is to monitor the health of bearings and to provide the information, by means of suitable indices, for prognostics, which eventually estimates the remaining useful life (RUL) of the bearing. The reliability of bearing CM and RUL estimation depends on the consistency and on the performances, in wide sense, of the health index used. The health indices based on machine learning require a large amount of historical data, which are not available in many cases. Other health indices, which are characterized by monotonical growth, may be affected by the difficulty to determine the correct failure threshold in different operating conditions. A new health index, called ‘MACPSD’, is proposed in this paper, which is the moving average cross-correlation of power spectral density (PSD) of signals. The rationale of MACPSD is that different health condition would have different energy distribution in the frequency domain. MACPSD has the further advantage that it ranges from zero to one. MACPSD can track the changes of the energy distribution as the bearing damage develops. The performance of MACPSD are shown by using a set of data of bearing run-to-failure, which was generated by the NSF I/UCR Center for Intelligent Maintenance System.
Keywordsbearing condition monitoring health index power spectral density moving average cross-correlation coefficient remaining useful life
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- 1.Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J.: Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799-834 (2018).Google Scholar
- 2.Cerrada, M., Sánchez, R. V., Li, C., Pacheco, F., Cabrera, D., de Oliveira, J. V., & Vásquez, R. E.: A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169-196 (2018).Google Scholar
- 3.Pennacchi, P., Chatterton, S., Vania, A., Ricci, R., & Borghesani, P.: Experimental evidences in bearing diagnostics for traction system of high speed trains. Chemical Engineering Transactions, 33, 739-744 (2013).Google Scholar
- 4.Pennacchi, P., Chatterton, S., & Vania, A.: Development and testing of health monitoring of the bearings of traction system of a regional train locomotive during commercial service. In ISMA2018 International Conference on Noise and Vibration Engineering, pp. 1849-1862. KU Leuven-Departement Werktuigkunde (2018).Google Scholar
- 5.Pennacchi, P., Chatterton, S., Vania, A., & Xu, L.: Diagnostics of Bearings in Rolling Stocks: Results of Long Lasting Tests for a Regional Train Locomotive. Mechanisms and Machine Science, 61, 321-335 (2019).Google Scholar
- 6.Xu, L., Chatterton, S., & Pennacchi, P.: A Novel Method of Frequency Band Selection for Squared Envelope Analysis for Fault Diagnosing of Rolling Element Bearings in a Locomotive Powertrain. Sensors, 18(12), 4344 (2018).Google Scholar
- 7.Medjaher, K., Zerhouni, N., & Baklouti, J.: Data-driven prognostics based on health indicator construction: Application to PRONOSTIA’s data. In Control Conference (ECC), 2013 European, pp. 1451-1456. European (2013).Google Scholar
- 8.Li, N., Lei, Y., Lin, J., & Ding, S. X.: An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 62(12), 7762-7773 (2015).Google Scholar
- 9.Zhang, B., Zhang, L., & Xu, J.: Degradation feature selection for remaining useful life prediction of rolling element bearings. Quality and Reliability Engineering International, 32(2), 547-554 (2016).Google Scholar
- 10.Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F.: Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16-27 (2015).Google Scholar
- 11.Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F.: Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150-172 (2015).Google Scholar
- 12.El-Thalji, I., & Jantunen, E.: A descriptive model of wear evolution in rolling bearings. Engineering failure analysis, 45, 204-224 (2014).Google Scholar
- 14.Piersol, A.G., & Paez, T.L.: Harris’ shock and vibration handbook. 6th edn. New York: Mcgraw-hill (2010).Google Scholar
- 15.Qiu, H., Lee, J., Lin, J., & Yu, G.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of sound and vibration, 289(4-5), 1066-1090 (2006).Google Scholar
- 16.NASA Ames Prognostics Data Repository, http://ti.arc.nasa.gov/project/prognostic-data-repository, last accessed 2018/12/12.
- 17.Li, N., Lei, Y., Lin, J., & Ding, S. X.: An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 62(12), 7762-7773 (2015).Google Scholar