Applications of Prognostics

This chapter introduces several prognostics applications based on the real test data or data that are generated by simulating practical cases. Three major challenges are addressed for prognostics methods to be practical throughout this chapter.

[1]  N. L. Johnson,et al.  Systems of frequency curves generated by methods of translation. , 1949, Biometrika.

[2]  Alberto Carpinteri,et al.  Are the Paris’ law parameters dependent on each other? , 2008 .

[3]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[4]  J. Archard.  Contact and Rubbing of Flat Surfaces , 1953 .

[5]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[6]  G. Zavarise,et al.  Numerical simulation of wear-mechanism maps , 2002 .

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

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

[9]  Toshio Nakamura,et al.  Simulations of crack propagation in elastic-plastic graded materials , 2004 .

[10]  James S. Walker.  Fast Fourier Transforms , 1991 .

[11]  Bernard Bobée,et al.  Comparison of Methodologies to Assess the Convergence of Markov Chain Monte Carlo Methods , 2003, Comput. Stat. Data Anal..

[12]  L. Brillouin,et al.  Science and information theory , 1956 .

[13]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[14]  Claude E. Shannon,et al.  The mathematical theory of communication , 1950 .

[15]  M. Pecht,et al.  Estimation of remaining useful life of ball bearings using data driven methodologies , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[16]  Yan Peng,et al.  Analysis of crack propagation in concrete structures with structural information entropy , 2010 .

[17]  Theodoros H. Loutas,et al.  Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression , 2013, IEEE Transactions on Reliability.

[18]  Ruoyu Li,et al.  Fault features extraction for bearing prognostics , 2012, J. Intell. Manuf..

[19]  Carlos J. Perez,et al.  Bayesian analysis of a generalized lognormal distribution , 2009, Comput. Stat. Data Anal..

[20]  W. Sawyer,et al.  Finite element analysis and experiments of metal/metal wear in oscillatory contacts , 2005 .

[21]  Jason E. Action,et al.  Wear-Rate Uncertainty Analysis , 2004 .

[22]  Brigitte Chebel-Morello,et al.  PRONOSTIA : An experimental platform for bearings accelerated degradation tests. , 2012 .

[23]  Ian Howard,et al.  A Review of Rolling Element Bearing Vibration 'Detection, Diagnosis and Prognosis', , 1994 .

[24]  Joseph Mathew,et al.  Bearing fault prognosis based on health state probability estimation , 2012, Expert Syst. Appl..

[25]  R. V. Pieri,et al.  On obtaining fatigue crack growth parameters from the literature , 1990 .

[26]  Pavle Boskoski,et al.  Bearing fault prognostics based on signal complexity and Gaussian process models , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[27]  M. Ashby,et al.  Wear-mechanism maps , 1990 .

[28]  E. Bechhoefer,et al.  Development and Validation of Bearing Diagnostic and Prognostic Tools using HUMS Condition Indicators , 2008, 2008 IEEE Aerospace Conference.

[29]  W. Sawyer,et al.  Analysis of planar multibody systems with revolute joint wear , 2010 .

[30]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[31]  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.

[32]  T. A. Harris,et al.  A New Stress-Based Fatigue Life Model for Ball Bearings , 2001 .