Extreme prognostics for remaining useful life analysis of composite structures
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[1] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[2] Bin Deng,et al. Determination of the Weibull parameters from the mean value and the coefficient of variation of the measured strength for brittle ceramics , 2017, Journal of Advanced Ceramics.
[3] Ying Peng,et al. A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction , 2011 .
[4] Zhiwen Liu,et al. A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time , 2012, Sensors.
[5] Ming J. Zuo,et al. Multistate degradation and supervised estimation methods for a condition-monitored device , 2014 .
[6] Theodoros Loutas,et al. In-situ fatigue damage assessment of carbon-fibre reinforced polymer structures using advanced experimental techniques , 2016 .
[7] Theodoros Loutas,et al. Fatigue damage diagnostics and prognostics of composites utilizing structural health monitoring data and stochastic processes , 2016 .
[8] Xiao-Sheng Si,et al. Data-Driven Remaining Useful Life Prognosis Techniques , 2017 .
[9] H. Saunders,et al. Probabilistic models of cumulative damage , 1985 .
[10] Ming Jian Zuo,et al. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process , 2014, Reliab. Eng. Syst. Saf..
[11] Xiaofei Lu,et al. Hazard rate function in dynamic environment , 2014, Reliab. Eng. Syst. Saf..
[12] George J. Vachtsevanos,et al. A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .