Prediction of remaining useful life under different conditions using accelerated life testing data

[1]  Dawn An,et al.  Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab , 2013, Reliab. Eng. Syst. Saf..

[2]  Zhaobin Wang,et al.  Study on feasibility of storage accelerated testing based on parameter degradation for aerospace relays , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[3]  J. Celaya,et al.  Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies , 2011 .

[4]  Nam H. Kim,et al.  Identification of correlated damage parameters under noise and bias using Bayesian inference , 2011 .

[5]  Suk Joo Bae,et al.  Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests , 2010, IEEE Transactions on Reliability.

[6]  Sankalita Saha,et al.  On Applying the Prognostic Performance Metrics , 2009 .

[7]  Bo-Suk Yang,et al.  Data-driven approach to machine condition prognosis using least square regression tree , 2009 .

[8]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Jay Lee,et al.  A Hybrid Method for On-line Performance Assessment and Life Prediction in Drilling Operations , 2007, 2007 IEEE International Conference on Automation and Logistics.

[10]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[11]  Mark Schwabacher,et al.  A Survey of Data -Driven Prognostics , 2005 .

[12]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[13]  Matthias W. Seeger,et al.  Gaussian Processes For Machine Learning , 2004, Int. J. Neural Syst..

[14]  Michael E. Tipping Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[15]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[16]  J. Newman,et al.  Fatigue-life prediction methodology using small-crack theory , 1999 .

[17]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[18]  Howard R. Waters,et al.  Gamma Processes and Finite Time Survival Probabilities , 1993, ASTIN Bulletin.

[19]  Kishan G. Mehrotra,et al.  Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.

[20]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[21]  W. Meeker Accelerated Testing: Statistical Models, Test Plans, and Data Analyses , 1991 .

[22]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[23]  H. Sorenson,et al.  Bayesian Parameter Estimation , 2006, Statistical Inference for Engineers and Data Scientists.

[24]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[25]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[26]  Dawn An,et al.  Practical options for selecting data-driven or physics-based prognostics algorithms with reviews , 2015, Reliab. Eng. Syst. Saf..

[27]  Noureddine Zerhouni,et al.  Accelerated life tests for prognostic and health management of MEMS devices. , 2014 .

[28]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..

[29]  Jonathan A. DeCastro,et al.  Exact Nonlinear Filtering and Prediction in Process Model-Based Prognostics , 2009 .

[30]  Christina Willhauck,et al.  Mixed Gaussian process and state-space approach for fatigue crack growth prediction , 2007 .

[31]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[32]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[33]  G. Nahler,et al.  : Accelerated testing , 1999 .

[34]  T. Bayes LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S , 1763, Philosophical Transactions of the Royal Society of London.