[[1] Jardine, A.K.S., Lin, D.M., Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems Signal Processing, 20 (7), 1483–1510.10.1016/j.ymssp.2005.09.012]Search in Google Scholar
[[2] Huang, H.F., Ouyang, H.J., Gao, H.L., Guo, L., Li, D., Wen, J. (2016). A feature extraction method for vibration signal of bearing incipient degradation. Measurement Science Review, 16 (3), 149-159.10.1515/msr-2016-0018]Open DOISearch in Google Scholar
[[3] Tandon, N., Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32 (8), 469-480.10.1016/S0301-679X(99)00077-8]Search in Google Scholar
[[4] Gebraeel, N., Lawley, M., Liu, R., Parmeshwaran, V. (2004). Residual life predictions from vibration-based degradation signals: A Neural Network approach. IEEE Transactions on Industrial Electronics, 51 (3), 694-700.10.1109/TIE.2004.824875]Open DOISearch in Google Scholar
[[5] Qiu, H., Lee, J., Lin, J., Yu, G. (2003). Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics, 17 (3), 127-140.10.1016/j.aei.2004.08.001]Open DOISearch in Google Scholar
[[6] Huang, R.Q., Xi, L.F., Li, X.L., Liu, C.R., Qiu, H., Lee, J. (2007). Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems Signal Processing, 21 (1), 193–207.10.1016/j.ymssp.2005.11.008]Search in Google Scholar
[[7] Ocak, H., Loparo, K.A., Discenzo, F.M. (2007). Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics. Journal of Sound Vibration, 302 (4), 951–961.10.1016/j.jsv.2007.01.001]Search in Google Scholar
[[8] Pan, Y.N., Chen, J., Guo, L. (2009). Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description. Mechanical Systems Signal Processing, 23 (3), 669–681.10.1016/j.ymssp.2008.05.011]Search in Google Scholar
[[9] Shen, Z.J., He, Z.J., Chen, X.F., Sun, C., Liu, Z. (2012). A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time. Sensors, 12 (8), 10109-10135.10.3390/s120810109347281923112591]Search in Google Scholar
[[10] Zhu, X.R., Zhang, Y.Y., Zhu, Y.S. (2013). Bearing performance degradation assessment based on the rough support vector data description. Mechanical Systems Signal Processing, 34 (1), 203–217.10.1016/j.ymssp.2012.08.008]Search in Google Scholar
[[11] Yu, J.B. (2011). Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mechanical Systems Signal Processing, 25 (7), 2573–2588.10.1016/j.ymssp.2011.02.006]Search in Google Scholar
[[12] Caesarendra, W., Widodo, A., Thom, P.H., Yang, B.S., Setiawan, J.D. (2011). Combine probability approach and indirect data-driven method for bearing degradation prognostics. IEEE Transactions Reliability, 60 (1), 14-20.10.1109/TR.2011.2104716]Search in Google Scholar
[[13] Kan, M.S., Tan, A.C.C., Mathew, J. (2015). A review on prognostic techniques for non-stationary and nonlinear rotating systems. Mechanical Systems Signal Processing, 62-63, 1–20.]Search in Google Scholar
[[14] Peng, Y., Dong, M., Zuo, M.J. (2010). Current status of machine prognostics in condition-based maintenance: A review. International Journal of Advance Manufacturing Technology, 50 (1), 297-31310.1007/s00170-009-2482-0]Search in Google Scholar
[[15] Dong, G.M., Chen, J. (2010). Study on cyclic energy indicator for degradation assessment of rolling element bearings. Journal of Vibration and Control, 17 (12), 1805-1816.]Search in Google Scholar
[[16] Yang, Y., Yu, D.J., Cheng, J.S. (2006). A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration, 294 (1), 269-277.]Search in Google Scholar
[[17] Yan, R.Q., Gao, R.X. (2007). Approximate entropy as a diagnostic tool for machine health monitoring. Mechanical Systems Signal Processing, 21 (2), 824-839.10.1016/j.ymssp.2006.02.009]Search in Google Scholar
[[18] Zhang, L., Xiong, G.L., Liu, H.S., Zou, H., Guo, W. (2010). Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Systems with Applications, 37 (8), 6017-6085.10.1016/j.eswa.2010.02.118]Search in Google Scholar
[[19] Zhu, K.H., Song, X.G., Xue, D.X. (2014). A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement, 47, 669-675.10.1016/j.measurement.2013.09.019]Search in Google Scholar
[[20] Pincus, S.M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88 (6), 2297-2301.10.1073/pnas.88.6.22975121811607165]Search in Google Scholar
[[21] Richman, J.S., Moorman, J.R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278 (6), H2039-H2049.10.1152/ajpheart.2000.278.6.H203910843903]Search in Google Scholar
[[22] Chen, W.T., Wang, Z.Z., Xie, H.B., Yu, W. (2007). Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15 (2), 266-272.10.1109/TNSRE.2007.89702517601197]Open DOISearch in Google Scholar
[[23] Xiong, G.L., Zhang, L., Liu, H.S., Zou, H.J., Guo, W.Z. (2010). A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction. Journal of Zhejiang University SCIENCE A, 11 (4), 270-279.10.1631/jzus.A0900360]Open DOISearch in Google Scholar
[[24] Zheng, J.D., Cheng, J.S., Yang, Y., Luo, S. (2014). A rolling bearing fault diagnosis method based on multiscale fuzzy entropy and variable predictive modelbased class discrimination. Mechanism and Machine Theory, 78, 187-200.10.1016/j.mechmachtheory.2014.03.014]Open DOISearch in Google Scholar
[[25] Liu, C.Y., Li, K., Zhao, L.N., Liu, F., Zheng, D., Liu, C., Liu, S. (2013). Analysis of heart rate variability using fuzzy measure entropy. Computers in Biology and Medicine, 43 (2), 100-10810.1016/j.compbiomed.2012.11.00523273774]Open DOISearch in Google Scholar
[[26] Lee, J., Qiu, H., Yu, G., Lin, J. (2007). “Bearing Data Set”, NASA Ames Prognostics Data Repository.http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository, IMS, University of Cincinnati, Rexnord Technical Services. (Accessed 13 April 2014)]Search in Google Scholar