Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
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Dawn An | Joo Ho Choi | Nam Ho Kim | D. An | Nam-Ho Kim | Jooho Choi
[1] Michael Pecht,et al. A comparative review of prognostics-based reliability methods for Lithium batteries , 2011, 2011 Prognostics and System Health Managment Confernece.
[2] Luren Yang,et al. An Evaluation of Confidence Bound Estimation Methods for Neural Networks , 2002, Advances in Computational Intelligence and Learning.
[3] Michigan.,et al. Estimating photometric redshifts with artificial neural networks , 2002, astro-ph/0203250.
[4] Ashok Srivastava,et al. Stable and Efficient Gaussian Process Calculations , 2009, J. Mach. Learn. Res..
[5] Jianhua Z. Huang,et al. A full scale approximation of covariance functions for large spatial data sets , 2012 .
[6] Wee Ser,et al. Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..
[7] G. Vachtsevanos,et al. Reasoning about uncertainty in prognosis: a confidence prediction neural network approach , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.
[8] C. Lee Giles,et al. What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation , 1998 .
[9] Kai Goebel,et al. Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .
[10] M.M. Gupta,et al. Memetic Differential Evolution Trained Neural Networks For Nonlinear System Identification , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.
[11] A. P. Dawid,et al. Regression and Classification Using Gaussian Process Priors , 2009 .
[12] Andrew Gordon Wilson,et al. Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation , 2013, ArXiv.
[13] Nazri Mohd Nawi,et al. An Improved Conjugate Gradient Based Learning Algorithm for Back Propagation Neural Networks , 2008 .
[14] Weicheng Cui,et al. An engineering model of fatigue crack growth under variable amplitude loading , 2008 .
[15] Mark J. Schervish,et al. Nonstationary Covariance Functions for Gaussian Process Regression , 2003, NIPS.
[16] J. Leo van Hemmen,et al. Accelerating backpropagation through dynamic self-adaptation , 1996, Neural Networks.
[17] Haiyan Lu,et al. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model , 2012 .
[18] Christopher K. I. Williams. Computing with Infinite Networks , 1996, NIPS.
[19] Lei Xu,et al. Health management based on fusion prognostics for avionics systems , 2011 .
[20] Kevin Kam Fung Yuen,et al. Toward a Hybrid Approach of Primitive Cognitive Network Process and Particle Swarm Optimization Neural Network for Forecasting , 2013, ITQM.
[21] Jay Lee,et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .
[22] Wu Yunxin,et al. Fault Diagnosis of Rolling Element Bearing Based on Vibration Frequency Analysis , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.
[23] Robert A. Jacobs,et al. Methods For Combining Experts' Probability Assessments , 1995, Neural Computation.
[24] N. Balakrishnan,et al. Residual life estimation based on bivariate non-stationary gamma degradation process , 2015 .
[25] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[26] G. Kitagawa. Non-Gaussian State—Space Modeling of Nonstationary Time Series , 1987 .
[27] W. Gilks,et al. Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .
[28] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[29] George Chryssolouris,et al. Confidence interval prediction for neural network models , 1996, IEEE Trans. Neural Networks.
[30] Yichuang Sun,et al. Wavelet neural network approach for fault diagnosis of analogue circuits , 2004 .
[31] Shawki A. Abouel-seoud,et al. Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission , 2011 .
[32] Daniel Svozil,et al. Introduction to multi-layer feed-forward neural networks , 1997 .
[33] Bhaskar Saha,et al. An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries , 2010 .
[34] Wei Liang,et al. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..
[35] Léon Personnaz,et al. Construction of confidence intervals for neural networks based on least squares estimation , 2000, Neural Networks.
[36] Bin Zhang,et al. Application of Blind Deconvolution Denoising in Failure Prognosis , 2009, IEEE Transactions on Instrumentation and Measurement.
[37] Marek Krawczuk,et al. Improvement of damage detection methods based on experimental modal parameters , 2011 .
[38] 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..
[39] D. Rubin. Using the SIR algorithm to simulate posterior distributions , 1988 .
[40] Matthias W. Seeger,et al. Gaussian Processes For Machine Learning , 2004, Int. J. Neural Syst..
[41] Joo-Ho Choi,et al. A Comparison Study of Methods for Parameter Estimation in the Physics-based Prognostics , 2012, Annual Conference of the PHM Society.
[42] Yu Peng,et al. Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).
[43] Yongqian Liu,et al. Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine , 2013 .
[44] Carlos Henggeler Antunes,et al. Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development , 2013, Neurocomputing.
[45] A. Chattopadhyay,et al. Gaussian Process Time Series Model for Life Prognosis of Metallic Structures , 2009 .
[46] Robert B. Gramacy,et al. Cases for the nugget in modeling computer experiments , 2010, Statistics and Computing.
[47] O. A. Hodhod,et al. Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete , 2013 .
[48] Yan-Ping Wang,et al. Bayesian inference and prediction analysis of the power law process based on a natural conjugate prior , 2015 .
[49] Raphael T. Haftka,et al. Reducing Uncertainty in Damage Growth Properties by Structural Health Monitoring , 2009 .
[50] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[51] Michael Osterman,et al. Prognostics of Lithium-ion Batteries using Extended Kalman Filtering , 2011 .
[52] Saeid Nahavandi,et al. Quantifying uncertainties of neural network-based electricity price forecasts , 2013 .
[53] Donghua Zhou,et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation , 2013 .
[54] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[55] Sankaran Mahadevan,et al. Quantitative model validation techniques: New insights , 2012, Reliab. Eng. Syst. Saf..
[56] S. T. Buckland,et al. An Introduction to the Bootstrap. , 1994 .
[57] Bart L. M. Happel,et al. Design and evolution of modular neural network architectures , 1994, Neural Networks.
[58] Victor Giurgiutiu,et al. Current issues in vibration-based fault diagnostics and prognostics , 2002, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.
[59] Hongxing Li,et al. Fuzzy Neural Network Theory and Application , 2004, Series in Machine Perception and Artificial Intelligence.
[60] R. D. Veaux,et al. Prediction intervals for neural networks via nonlinear regression , 1998 .
[61] Nam H. Kim,et al. Identification of correlated damage parameters under noise and bias using Bayesian inference , 2011 .
[62] Lei Deng,et al. Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine , 2014 .
[63] É MikaelBoden. A guide to recurrent neural networks and backpropagation , 2001 .
[64] Neil D. Lawrence,et al. Fast Sparse Gaussian Process Methods: The Informative Vector Machine , 2002, NIPS.
[65] Meng Chao,et al. Neural network ensembles based on copula methods and Distributed Multiobjective Central Force Optimization algorithm , 2014, Eng. Appl. Artif. Intell..
[66] Daniel Hissel,et al. Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems . , 2014 .
[67] Peter Challenor,et al. Computational Statistics and Data Analysis the Effect of the Nugget on Gaussian Process Emulators of Computer Models , 2022 .
[68] P. C. Paris,et al. A Critical Analysis of Crack Propagation Laws , 1963 .
[69] T. Bayes. An essay towards solving a problem in the doctrine of chances , 2003 .
[70] David Mackay,et al. Gaussian Processes - A Replacement for Supervised Neural Networks? , 1997 .
[71] Jerry Nedelman,et al. Book review: “Bayesian Data Analysis,” Second Edition by A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin Chapman & Hall/CRC, 2004 , 2005, Comput. Stat..
[72] A. Gelfand,et al. On Markov Chain Monte Carlo Acceleration , 1994 .
[73] Mikael Bodén,et al. A guide to recurrent neural networks and backpropagation , 2001 .
[74] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[75] Xuefei Guan,et al. Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison , 2009 .
[76] Yoshihiro Deguchi,et al. Applications of laser diagnostics to thermal power plants and engines , 2014 .
[77] George J. Vachtsevanos,et al. A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate , 2007, Int. J. Fuzzy Log. Intell. Syst..
[78] G.A. Rovithakis,et al. A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[79] T. Higuchi. Monte carlo filter using the genetic algorithm operators , 1997 .
[80] Ting-wen Xing,et al. Particle filter for state and parameter estimation in passive ranging , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.
[81] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[82] Donghua Zhou,et al. Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..
[83] Thomas J. Santner,et al. Design and analysis of computer experiments , 1998 .
[84] Ying Peng,et al. A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods , 2012 .
[85] J. Tinsley Oden,et al. Virtual model validation of complex multiscale systems: Applications to nonlinear elastostatics , 2013 .
[86] V. Sugumaran,et al. Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine , 2008, Expert Syst. Appl..
[87] Harris Drucker,et al. Boosting and Other Ensemble Methods , 1994, Neural Computation.
[88] Linxia Liao,et al. Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.
[89] Xiao Liu,et al. Condition-based maintenance for continuously monitored degrading systems with multiple failure modes , 2013 .
[90] Daming Lin,et al. A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .
[91] Jeong-Soo Park,et al. Sequential Monte Carlo filters for abruptly changing state estimation , 2011 .
[92] Fabio Tozeto Ramos,et al. A Sparse Covariance Function for Exact Gaussian Process Inference in Large Datasets , 2009, IJCAI.
[93] Leonardo Franco,et al. Neural Network Architecture Selection: Can Function Complexity Help? , 2009, Neural Processing Letters.
[94] Joo-Ho Choi,et al. Efficient reliability analysis based on Bayesian framework under input variable and metamodel uncertainties , 2012 .
[95] Joo-Ho Choi,et al. MCMC Approach for Parameter Estimation in the Structural Analysis and Prognosis , 2010 .
[96] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.
[97] Donald F. Specht,et al. Probabilistic neural networks , 1990, Neural Networks.
[98] Antoine Grall,et al. A condition-based maintenance policy for stochastically deteriorating systems , 2002, Reliab. Eng. Syst. Saf..
[99] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[100] Jay Lee,et al. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.
[101] Shih-Wei Lin,et al. Optimization of Back-Propagation Network Using Simulated Annealing Approach , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.
[102] Nathan Intrator,et al. Optimal ensemble averaging of neural networks , 1997 .
[103] D. An,et al. In-Situ Monitoring and Prediction of Progressive Joint Wear Using Bayesian Statistics , 2010 .
[104] Wilson Wang,et al. Enhanced fuzzy-filtered neural networks for material fatigue prognosis , 2013, Appl. Soft Comput..
[105] Jan Lundberg,et al. Remaining useful life prediction of grinding mill liners using an artificial neural network , 2013 .
[106] Nam H. Kim,et al. Bayesian Approach for Par ameter Estimation in the Str uctur al Analysis and Prognosis , 2010 .
[107] Raphael T. Haftka,et al. Using a Simple Crack Growth Model in Predicting Remaining Useful Life , 2012 .
[108] Carlo Di Bello,et al. Analysis of an associative memory neural network for pattern identification in gene expression data , 2001, BIOKDD.
[109] Jay Lee,et al. A review on prognostics and health monitoring of Li-ion battery , 2011 .
[110] K. Gnana Sheela,et al. Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .
[111] K. F. Martin,et al. A review by discussion of condition monitoring and fault diagnosis in machine tools , 1994 .
[112] Jian Ma,et al. A new neural network model for the state-of-charge estimation in the battery degradation process , 2014 .
[113] Robert X. Gao,et al. Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .
[114] G. Storvik. Particle filters in state space models with the presence of unknown static parameters YYYY No org found YYY , 2000 .
[115] Robert Tibshirani,et al. An Introduction to the Bootstrap , 1994 .
[116] Amir F. Atiya,et al. Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances , 2011, IEEE Transactions on Neural Networks.
[117] Okyay Kaynak,et al. An algorithm for fast convergence in training neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[118] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[119] Sankaran Mahadevan,et al. Validation and error estimation of computational models , 2006, Reliab. Eng. Syst. Saf..
[120] Noureddine Zerhouni,et al. Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..
[121] Dawn An,et al. Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab , 2013, Reliab. Eng. Syst. Saf..
[122] Norbert Jankowski,et al. Survey of Neural Transfer Functions , 1999 .
[123] Kishan G. Mehrotra,et al. Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.
[124] Qiang Miao,et al. Prognostics and health monitoring for lithium-ion battery , 2011, Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics.
[125] Darryll J. Pines,et al. A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .
[126] A. Saltelli,et al. Reliability Engineering and System Safety , 2008 .
[127] Aitor Arnaiz,et al. Ball bearing damage detection using traditional signal processing algorithms , 2013, IEEE Instrumentation & Measurement Magazine.
[128] Raphael T. Haftka,et al. Technical Notes Uncertainty Identification of Damage Growth Parameters Using Nonlinear Regression , 2011 .
[129] Gomes de Freitas,et al. Bayesian methods for neural networks , 2000 .
[130] M.H. Azarian,et al. Failure prognostics of multilayer ceramic capacitors in temperature-humidity-bias conditions , 2008, 2008 International Conference on Prognostics and Health Management.
[131] Jui-Fang Chang,et al. PARTICLE SWARM OPTIMIZATION BASED ON BACK PROPAGATION NETWORK FORECASTING EXCHANGE RATES , 2011 .
[132] Nando de Freitas,et al. The Unscented Particle Filter , 2000, NIPS.
[133] K. Goebel,et al. Multiple damage progression paths in model-based prognostics , 2011, 2011 Aerospace Conference.
[134] Joo-Ho Choi,et al. Improved MCMC Method for Parameter Estimation Based on Marginal Probability Density Function , 2011 .
[135] Amine Bermak,et al. Gaussian process for nonstationary time series prediction , 2004, Comput. Stat. Data Anal..
[136] Dumitru Ostafe. Neural Network Hidden Layer Number Determination Using Pattern Recognition Techniques , 2005 .