Acoustic Emission-Based Prognostics of Slow Rotating Bearing Using Bayesian Techniques Under Dependent and Independent Samples

REFERENCES

• Al-Raheem, K. F., and W. Abdul-Karem. 2010. Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis. International Journal of Engineering, Science and Technology 2(6):278–290.
   Google Scholar
• An, D., J.-H. Choi, and N. H. Kim. 2012. A comparison study of methods for parameter estimation in the physics-based prognostics. Paper presented at the Annual Conference of Prognostics and Health Management Society, Minneapolis, Minnesota, USA, September 23–27, 2012.
   Google Scholar
• An, D., N. H. Kim, and J.-H. Choi. 2013. Options for prognostics methods: A review of data-driven and physics-based prognostics. Paper presented at the Annual Conference of the Prognostics and Health Management Society, New Orleans, October, 14–17, 2013.
   Google Scholar
• Arlot, S. (2010) A survey of cross-validation procedures for model selection. Statistics Surveys 4:40–79.
   Google Scholar
• Bishop, C. M. 2006. Pattern recognition and machine learning. Cambridge, UK: Springer.
   Google Scholar
• Bolander, N., H. Qiu, N. Eklund, E. Hindle, and T. Rosenfeld. 2009. Physics-based remaining useful life prediction for aircraft engine bearing prognosis. Paper presented at the Annual Conference of the Prognostics and Health Management Society, San Diego, CA, September 27 – October 1, 2009.
   Google Scholar
• Calinon S. 2009. Robot programming by demonstration: A probabilistic approach. EPFL/CRC Press, 2009.
   Google Scholar
• Camcia, F., K. Medjaher, N. Zerhounib, and P. Nectoux. 2012. Feature evaluation for effective bearing prognostics. Quality and Reliability Engineering International 29(4):1–15.
   Google Scholar
• Chatzis, S. P., D. Korkinof, and Y. Demiris. 2012. A nonparametric Bayesian approach toward robot learning by demonstration. Robotics and Autonomous Systems 60(6):789–802.
   Web of Science ®Google Scholar
• Chen, T., and J. Ren. 2009. Bagging for Gaussian process regression. Neurocomputing 72(7–9):1605–1610.
   Web of Science ®Google Scholar
• Gebraeel N., M. Lawley, R. Liu, and V. Parmeshwaran. 2004. Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics 51:694–700.
   Web of Science ®Google Scholar
• Goebel, K., B. Saha, and A. Saxena. 2008. A comparison of three data - driven techniques for prognostics. Paper presented at the Proceedings of the 62nd Meeting of the Society For Machinery Failure Prevention Technology (MFPT), Virginia Beach, VA, May 6–8.
   Google Scholar
• Heyns, T., J. P. de Villiers, and P. S. Heyns. 2012. Consistent haul road condition monitoring by means of vehicle response normalisation with Gaussian processes. Engineering Applications of Artificial Intelligence 25(8):1752–1760.
   Web of Science ®Google Scholar
• Hippert, H. S., and J. W. Taylor. 2010. An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting. Neural Networks 23:386–395.
   PubMed Web of Science ®Google Scholar
• Hong, S., and Z. Zhou. 2012a. Remaining useful life prognosis of bearing based on a Gaussian process regression. Paper presented at the 5th International Conference on BioMedical Engineering and Informatics (BMEI 2012), Chongqing, China, October 16–18, 2012.
   Google Scholar
• Hong, S., and Z. Zhou. 2012b. Application of Gaussian process regression for bearing degradation assessment. In Proceedings of the information science and service science and data mining (ISSDM) 2012, 6th international conference on new trends, 644–648. ISSDM/IEEE.
   Google Scholar
• Jardine, A. K., D. Lin, and D. Banjevic. 2006. A review on machinery diagnostics and prognostics implementing condition based maintenance. Mechanical Systems and Signal Processing 20(7):1483–1510.
   Web of Science ®Google Scholar
• Lee, J.-M., C. Yoo, S. W. Choi, P. A. Vanrolleghem, and I.-B. Lee. 2004. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59:223–234.
   Web of Science ®Google Scholar
• Liu, D., J. Pang, J. Zhou, and Y. Pang. 2012. Data driven prognostics for Lithium-ion battery based on Gaussian process regression. Paper presented at the 2012 Prognostics and System Health Management Conference (PHM-2012), Beijing, China, May 23–25.
   Google Scholar
• Malhi, A., and R. X. Gao. 2004. PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement 53(6):1517–1525.
   Web of Science ®Google Scholar
• Marble, S., and B. Morton. 2005. Predicting the remaining life of propulsion system bearings. In Proceedings of IEEE Aerospace Conference. IEEE.
   Google Scholar
• Nabney, I. T. 2002. NETLAB algorithms for pattern recognition. Great Britain, UK: Springer.
   Google Scholar
• Opsomer, J., Y. Wang, and Y. Yang. 2001. Nonparametric regression with correlated errors. Statistical Science 16(2):134–153.
   Web of Science ®Google Scholar
• Rasmussen, C. E., and C. K. I. Williams. 2006. Gaussian processes for machine learning. Cambridge MA: MIT Press.
   Google Scholar
• Saha, B., K. Goebel, and J. Christophersen. 2009. Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement & Control 31(3–4):293–308.
   Web of Science ®Google Scholar
• Saxena, A. 2010. Prognostics, the science of prediction. Paper presented at the Annual Conference of the Prognostics and Health Management Society, Portland, OR, October 10–14, 2010.
   Google Scholar
• Saxena, A., J. Celaya, B. Saha, S. Saha, and K. Goebel. 2009. On applying the prognostic performance metrics. Paper presented at the Annual Conference of the Prognostics and Health Management Society, San Diego, CA, September, 2009.
   Google Scholar
• Schölkopf, B., A. Smola, and K. R. Muller. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10:1299–1319.
   Web of Science ®Google Scholar
• Schölkopf, B., A. Smola, and K. R. Muller. 1999. Kernel principal component analysis. In Advances in kernel methods - Support vector learning, 327–352. Cambridge, MSA: MIT Press.
   Google Scholar
• Şengüler, T., Karatoprak E. and Şeker S. 2010. A new MLP approach for the detection of the incipient bearing damage. Advances in Electrical and Computer Engineering 10(3):34–39.
   Web of Science ®Google Scholar
• Si, X. S., W. Wang, C.-H. Hu, and D.-H. Zhou. 2011. Remaining useful life estimation- A review of the statistical data driven approaches. European Journal of Operational Research 213:1–14.
   Web of Science ®Google Scholar
• Skabar, A. 2007. Mineral potential mapping using Bayesian learning for multilayer perceptrons. Mathematical Geology 39: 439–451.
   Google Scholar
• Wang, G., L. Qian, and Z. Guo. 2013. Continuous tool wear prediction based on Gaussian mixture regression model. International Journal of Advanced Manufacturing and Technology 66:1921–1929.
   Web of Science ®Google Scholar
• Wang, M., and J. Wang. 2012. CHMM for tool condition monitoring and remaining useful life prediction. International Journal of Advanced Manufacturing and Technology 59:463–471.
   Web of Science ®Google Scholar