References
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
BSI. (2009). BS ISO 7919-2: Mechanical Vibration - Evaluation Of Machine Vibration By Measurements On Rotating Shafts (Tech. Rep.). British Standards Institute.
Catterson, V. M., Melone, J., & Gracia, M. S. (2016). Prognostics Of Transformer Paper Insulation Using Statistical Particle Filtering Of Online Data. IEEE Electrical Insulation.
Coble, J., Humberstone, M., & Hines, J. W. (2010). Adaptive Monitoring, Fault Detection And Diagnostics, And Prognostics System for The IRIS Nuclear Plant (Tech. Rep.). DTIC Document.
Di Maio, F., Ng, S. S., Tsui, K.-L., & Zio, E. (2011). Na¨ıve Bayesian classifier for on-line remaining useful life prediction of degrading bearings. In MMR2011 (pp. 1–14).
Gebraeel, N. Z., Lawley, M. A., Li, R., & Ryan, J. K. (2005). Residual-Life Distributions From Component Degradation Signals: A Bayesian Approach. IIE Transactions, 37(6), 543–557.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian Data Analysis (2nd ed.). Boca Raton, Florida.
Gu, J., Barker, D., & Pecht, M. (2009). Health Monitoring And Prognostics Of Electronics Subject To Vibration Load Conditions. IEEE Sensors Journal, 9(11), 1479-1485.
Hess, A., & Fila, L. (2002). The Joint Strike Fighter (JSF) PHM Concept: Potential Impact On Aging Aircraft Problems. In (p. 3021 - 3026). IEEE Aerospace Conference.
Kan, M. S., Tan, A. C., & Mathew, J. (2015). A Review On Prognostic Techniques For Non-stationary and Nonlinear Rotating Systems. Mechanical Systems and Signal Processing, 62-63, 1–20.
Killick, R., & Eckley, I. (2014). Changepoint: An R Package For Changepoint Analysis. Journal of Statistical Software, 58(3), 1–19.
Leyzerovich, A. S. (2008). Steam Turbines for Modern Fossil-Fuel Power Plants. Georgia, USA: The Fairmont Press, Inc.
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge, MA: The MIT Press.
Qiancheng, W., Shunong, Z., & Rui, K. (2011). Research Of Small Samples Avionics Prognostics Based On Support Vector Machine. In Prognostics and System Health Management Conference, Shenzhen, China (pp. 1–5).
Rudd, S., Catterson, V. M., McArthur, S., & Johnstone, C. (2011). Circuit Breaker Prognostics Using SF6 Circuit Breaker Prognostics Using SF6 Data. IEEE Power and Energy Society General Meeting.
Saha, B., Celaya, J. R., Goebel, K., & Wysocki, P. F. (2009). Towards Prognostics For Electronics Components. In IEEE Aerospace Conference, Montana, USA (p. 1-7).
Sun, B., Zeng, S., Kang, R., & Pecht, M. (2012). Benefits and Challenges of System Prognostics. IEEE Transactions on Reliability, 61(2), 323-335.
Zaidan, M., Mills, A., & Harrison, R. (2013). Bayesian framework for aerospace gas turbine engine prognostics. In IEEE Aerospace Conference, Montana, USA (p. 1-8).
Zheng, Y., Wu, L., Li, X., & Yin, C. (2014). A Relevance Vector Machine-Based Approach For Remaining Useful Life Prediction Of Power MOSFETs. In Prognostics and System Health Management Conference, Hunan, China (p. 642-646).