연구동향 분석
박사

PRACTICAL PROGNOSTICS METHODS FOR UNCERTAINTY MANAGEMENT: NOISY, INSUFFICIENT AND INDIRECT DEGRADATION DATA

안다운 2015년

논문상세정보
인용/피인용
' PRACTICAL PROGNOSTICS METHODS FOR UNCERTAINTY MANAGEMENT: NOISY, INSUFFICIENT AND INDIRECT DEGRADATION DATA' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • bayesian method
  • bearing
  • crack
  • gaussian process
  • neural network
  • particle filter
  • prognostics
  • review
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,065 0

0.0%

' PRACTICAL PROGNOSTICS METHODS FOR UNCERTAINTY MANAGEMENT: NOISY, INSUFFICIENT AND INDIRECT DEGRADATION DATA' 의 참고문헌

  • Zio E, Peloni G. Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering and System Safety 2011;96(3):4039.
  • Zio E, Maio FD. A Data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering and System Safety 2010;95:4957.
  • Zhang G, Yuen KKF. Toward a hybrid approach of primitive cognitive network process and particle swarm optimization neural network for forecasting. Procedia Computer Science 2013;17:441-8.
  • Yu WK, Harris TA. A new stress-based fatigue life model for ball bearings. Tribology Transactions 2001;44(1):1118.
  • Yang L, Kavli T, Carlin M, Clausen S, Groot PFM. An evaluation of confidence bound estimation methods for neural networks. In: Proceedings of the European symposium on intelligent techniques, Aachen, Germany, September 1415, 2000.
  • Williams CKI. Computing with infinite networks. In: Mozer MC, Jordan MI, Petsche T, editors. Advances in neural information processing systems. Massachusetts: The MIT Press; 1997.
  • Wilamowski BM, Iplikci S, Kaynak O, Efe MO . An algorithm for fast convergence in training neural networks. Proceedings of the International Joint Conference on Neural Networks 2001;3:177882.
  • Wang Z, Nakamura T. Simulations of crack propagation in elastic-plastic graded materials. Mechanics of Materials 2004;36:60122.
  • Wang WP, Liao S, Xing TW. Particle filter for state and parameter estimation in passive ranging. In: Proceedings of the IEEE international conference on intelligent computing and intelligent systems, Shanghai, China, November 2022, 2009.
  • Walker JS. Fast Fourier transforms. 2nd ed. Florida: CRC Press; 1996.
  • Virkler DA, Hillberry BM, Goel PK. The statistical nature of fatigue crack propagation. ASME Journal of Engineering Materials and Technology 1979;101:14853.
  • Veaux RD, Schumi J, Schweinsberg J, Ungar LH. Prediction intervals for neural networks via nonlinear regression. Technometrics 1998;40(4):27382.
  • Van Der Merwe R, Doucet A, DeFreitas N, Wan E. The unscented particle filter. In: NIPS 2000;58490.
  • UK offshore commercial air transport helicopter safety record. Oil & Gas UK. 19812010. Available from: http://www.oilandgasuk.co.uk/cmsfiles/modules/publications/pdfs/HS02 7.pdf.
  • Tipping ME. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 2001;1:21144.
  • Svozil D, Kvasnika V, Pospichal J. Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems 1997;39:4362.
  • Subudhi B, Jena D, Gupta MM. Memetic differential evolution trained neural networks for nonlinear system identification. In: Proceedings of the IEEE region 10 colloquium and the third international conference on industrial and information systems, Kharagpur, India, December 810, 2008.
  • Storvik G. Particle filters in state space models with the presence of unknown static parameters. IEEE Transactions on Signal Processing 2002;50(2):2819.
  • Specht DF. Probabilistic neural networks. Neural Networks 1990;3:10918.
  • Soares S, Antunes CH, Araujo R. Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development. Neurocomputing 2013;121:498-511.
  • Sinclair GB, Pierie RV. On obtaining fatigue crack growth parameters from the literature. International Journal of Fatigue 1990;12(1):5762.
  • Silva RE, Gouriveau R, Jemei S, Hissel D, Boulon L, Agbossou K, Steiner NY. Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems. International Journal of Hydrogen Energy 2014;39(21):1112844.
  • Si XS, Wang W, Hu CH, Chen MY, Zhou DH. A Wiener process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing 2013;35(1-2):21937.
  • Sheela KG, Deepa SN. Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering 2013;2013, Article ID 425740, 11 pages.
  • Shannon CE. A mathematical theory of communication. Bell System Technical Journal 1948;27:379423, 62356.
  • Seeger M. Gaussian processes for machine learning. International Journal of Neural Systems 2004:14(2):69106.
  • Saxena A, Celaya J, Saha B, Saha S, Goebel K. On applying the prognostic performance metrics. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, California, USA, September 27 October 1, 2009.
  • Sargent RG. Verification and validation of simulation models. Journal of Simulation 2013;7:12-24.
  • Santner TJ, Williams BJ, Notz WI. The design and analysis of computer experiments. New York: Springer-Verlag; 2003.
  • Sankararaman S, Ling Y, Shantz C, Mahadevan S. Uncertainty quantification in fatigue damage prognosis. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, California, USA, September 27 October 1, 2009.
  • Sang H, Huang JZ. A full scale approximation of covariance functions for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2012;74(1):111-32.
  • Salomon R, Hemmen JLV. Accelerating backpropagation through dynamic self-adaptation. Neural Networks 1996;9(4):589601.
  • Sacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and analysis of computer experiments. Statistical Science 1989;4(4):40923.
  • Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland J, editors. Parallel distributed processing: Ex-plorations in the microstructure of cognition. Massachusetts: The MIT Press; 1986.
  • Rubin DB. Using the SIR Algorithm to simulate posterior distributions. Bayesian Statistics 1988;3(1):395402.
  • Rovithakis GA, Maniadakis M, Zervakis M. A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 2004;34(1):695702.
  • Rivals I, Personnaz L. Construction of confidence intervals for neural networks based on least squares estimation. Neural Networks 2000;13(45):46384.
  • Ristic B, Arulampalam S, Gordon N. Beyond the Kalman filter: Particle filters for tracking applications. Artech House; 2004.
  • Rice J. Mathematical statistics and data analysis. 2nd ed. California: Duxbury Press; 1995.
  • Rebba R, Mahadevan S, Huang S. Validation and error estimation of computational models. Reliability Engineering and System Safety 2006;91:13907.
  • Rasmussen CE, Williams CKI. Gaussian processes for machine learning. Massachusetts: The MIT Press; 2006.
  • Prognosis program begins at DARPA. A newspaper report, Space Daily. December 8, 2003. Available from: http://www.spacedaily.com/news/materials- 03zv.html.
  • Petalas P, Spyridonos P, Glotsos D, Cavouras D, Ravazoula P, Nikiforidis G. Probabilistic neural network analysis of quantitative nuclear features in predicting the risk of cancer recurrence at different follow-up times. In: Proceedings of the 3rd international symposium on image and signal processing and analysis, Rome, Italy, September 1820, 2003.
  • Parzen E. On estimation of a probability density function and mode. Annals of Mathematical Statistics 1962;33(3):106576.
  • Park JI, Bae SJ. Direct prediction methods on lifetime distribution of organic light-emitting diodes from accelerated degradation tests. IEEE Transactions on Reliability 2010;59:7490.
  • Paris PC, Erdogan F. A critical analysis of crack propagation laws. ASME Journal of Basic Engineering 1963;85:52834.
  • Papazian JM, Anagnostoua EL, Engela SJ, Hoitsmaa D, Madsena J, Silbersteina RP, Welsh G, Whiteside JB. A structural integrity prognosis system. Engineering Fracture Mechanics 2009;76(5):62032.
  • Pandey MD, Noortwijk JMV. Gamma process model for time-dependent structural reliability analysis. In: Proceedings of the second international conference on bridge maintenance, safety and management, Kyoto, Japan, October 1822, 2004.
  • Paciorek C, Schervish MJ. Nonstationary covariance functions for gaussian process regression. In: Thrun S, Saul L, Scholkopf B, editors. Advances in neural information processing systems. Massachusetts: The MIT Press; 2004.
  • Ostafe D. Neural network hidden layer number determination using pattern recognition techniques. In: Proceedings of the 2nd Romanian-Hungarian joint symposium on applied computational intelligence, Timisoara, Romania, May 1214, 2005.
  • Orchard ME, Vachtsevanos GJ. A particle filtering approach for on-line failure prognosis in a planetary carrier plate. International Journal of Fuzzy Logic and Intelligent Systems 2007;7(4):2217.
  • Operation of the defense acquisition system. DoD Instruction 5000.2. April 5, 2002. Available from: http://www.acq.osd.mil/ie/bei/pm/ref- library/dodi/p50002r.pdf.
  • Oden JT, Prudencio EE, Bauman PT. Virtual model validation of complex multiscale systems: applications to nonlinear elastostatics. Computer Methods in Applied Mechanics and Engineering 2013;266:162-84.
  • Newman Jr JC, Phillips EP, Swain MH. Fatigue-Life prediction methodology using small-crack theory. International Journal of Fatigue 1999;21:10919.
  • Nelson W. Accelerated testing: Statistical models, test plans, and data analysis. New Jersey: John Wiley & Sons; 1990.
  • Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Morello B, Zerhouni N, Varnier C. Pronostia: An experimental platform for bearings accelerated degradation test. In: Proceedings of the IEEE international conference on prognostics and health management, Denver, Colorado, USA, Jun 1821, 2012.
  • Neal RM. Regression and classification using Gaussian process priors. In: Bernardo JM, Berger JO, Dawid AP, Smith AFM, editors. Bayesian statistics. New York: Oxford University Press; 1998.
  • Neal RM. Bayesian learning for neural networks [dissertation]. Ontario, Canada: University of Toronto; 1995.
  • Nawi NM, Ransing RS, Ransing MR. An improved conjugate gradient based learning algorithm for back propagation neural networks. International Journal of Computational Intelligence 2008;4(1):4655.
  • Naftaly U, Intrator N, Horn D. Optimal ensemble averaging of neural networks. Network: Computation in Neural Systems 1997;8(3):28396.
  • Mohanty S, Teale R, Chattopadhyay A, Peralta P, Willhauck C. Mixed Gaussian process and state-space approach for fatigue crack growth prediction. In: Proceedings of the international workshop on structural heath monitoring, Stanford, California, USA, September 1113, 2007.
  • Mohanty S, Das D, Chattopadhyay A, Peralta P. Gaussian process time series model for life prognosis of metallic structures. Journal of Intelligent Material Systems and Structures 2009;20:88796.
  • Miao Q, Xie L, Cui H, Liang W, Pecht M. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectronics Reliability 2013;53:80510.
  • Melkumyan A, Ramos F. A sparse covariance function for exact Gaussian process inference in large datasets. In: Proceedings of the 21st international joint conference on artificial intelligence, Pasadena, California, USA, July 1117, 2009.
  • Mao KZ, Tan K-C, Ser W. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks 2000;11(4):100916.
  • Mackay DJC. Introduction to Gaussian processes. Technical report, Cambridge University, UK, 1997. Available from: http://www.cs.utoronto.ca/~mackay/gpB.pdf
  • MacKay DJC. Gaussian processes-A replacement for supervised neural networks?. Tutorial lecture notes for NIPS, UK, http://www.inference.phy.cam.ac.uk/mackay/BayesGP.html, 1997.
  • Liu Y, Mahadevan S. Probabilistic fatigue life prediction using an equivalent initial flaw size distribution. International Journal of Fatigue 2009;31(3):47687.
  • Liu P, Li H. Fuzzy neural network theory and application. World Scientific, Singapore, 2004.
  • Liu J, Saxena A, Goebel K, Saha B, Wang W. An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. In: Proceedings of the annual conference of the prognostics and health management society, Portland, Oregon, USA, October 1016, 2010.
  • Liu D, Pang J, Zhou J, Peng Y. Data-driven prognostics for lithium-ion battery based on Gaussian process regression. In: Proceedings of the prognostics and system health management conference, Beijing, China, May 2325, 2012.
  • Liu A, Dong M, Peng Y. A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods. Mechanical Systems and Signal Processing 2012; 32:331348.
  • Ling Y, Mahadevan S. Quantitative model validation techniques: new insights. Reliability Engineering and System Safety 2013;111:21731.
  • Li R, Sopon P, He D. Fault features extraction for bearing prognostics. Journal of Intelligent Manufacturing 2012;23:31321.
  • Li D, Wang W, Ismail F. Enhanced fuzzy-filtered neural networks for material fatigue prognosis. Applied Soft Computing 2013;13(1):28391.
  • Lee J, Qiu H, Yu G, Lin J, Rexnord Technical Services. Bearing data set. IMS, University of Cincinnati. NASA Ames Prognostics Data Repository, [http://ti.arc.nasa.gov/project/prognostic-data-repository/], NASA Ames, Moffett Field, California; 2007.
  • Lawrence S, Giles CL, Tsoi AC. What size neural network gives optimal generalization? convergence properties of backpropagation. Technical Reports, UM Computer Science Department, UMIACS, Octobor 15, 1998.
  • Lawrence N, Seeger M, Herbrich R. Fast sparse Gaussian process methods: The information vector machine. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems. Massachusetts: MIT Press; 2003.
  • Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning. In: Tesauro G, Touretzky D, Leen T, editors. Advances in neural information processing systems. Massachusetts: The MIT Press; 1995.
  • Kramer SC, Sorenson HW. Bayesian parameter estimation. IEEE Transactions on Automatic Control 1988;33(2):21722.
  • Kitagawa G. Non-Gaussian state space modeling of nonstationary time series (with discussion). Journal of the American Statistical Association 1987;82(400):103263.
  • Kim S, Park JS. Sequential Monte Carlo filters for abruptly changing state estimation. Probabilistic Engineering Mechanics 2011;26:194201.
  • Khosravi A, Nahavandi S, Creighton D. Quantifying uncertainties of neural network-based electricity price forecasts. Applied Energy 2013;112:120-129.
  • Khosravi A, Nahavandi S, Creighton D, Atiya AF. Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks 2011;22(9):134156.
  • Khawaja T, Vachtsevanos G, Wu B. Reasoning about uncertainty in prognosis: A confidence prediction neural network approach. In: Proceedings of the 2005 annual meeting of the north American fuzzy information processing society, Ann Arbor, Michigan, USA, June 2225, 2005.
  • Kang LW, Zhao X, Ma J. A new neural network model for the state-of-charge estimation in the battery degradation process. Applied Energy 2014;121;20-7.
  • Kalman RE. A new approach to linear filtering and prediction problems. Transaction of the ASME-Journal of Basic Engineering 1960;82(1):3545.
  • Julier SJ, Uhlmann JK. Unscented filtering and nonlinear estimation. Proceedings of the IEEE 2004;92(3):40122.
  • Joint strike fighter PHM vision. Joint Strike Fighter Program Office. Available from: https://www.phmsociety.org/sites/phmsociety.org/files/PHMvision.pdf.
  • Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 2006;20:1483510.
  • Jacobs RA. Methods for combining experts’ probability assessments. Neural Computation 1995;7(5):86788.
  • IEEE PHM 2012 Prognostic challenge: Outline, experiments, scoring of results, winners. Available from: http://www.femto-st.fr/f/d/IEEEPHM2012- Challenge-Details.pdf.
  • Huang X, Torgeir M, Cui W. An Engineering model of fatigue crack growth under variable amplitude loading. International Journal of Fatigue 2008;30(1):210.
  • Hodhod OA, Ahmed HI. Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete. HBRC Journal 2013;9(1):15-21.
  • Higuchi T. Monte Carlo filter using the genetic algorithm operators. Journal of Statistical Computation and Simulation 1997;59(1):123.
  • He Y, Tan Y, Sun Y. Wavelet neural network approach for fault diagnosis of analogue circuits. IEE Proceedings-Circuits, Devices and Systems 2004;151(4):37984.
  • He D, Bechhoefer E. Development and validation of bearing diagnostic and prognostic tools using HUMS condition indicators. In: Proceedings of the IEEE aerospace conference, Big Sky, Montana, USA, March 18, 2008.
  • Haward I. A review of rolling element bearing vibration “detection, diagnosis and prognosis”. (No. DSTO-RR-0013). Defense Science and Technology Organization Canberra. Australia: 1994.
  • Happel BLM, Murre JMJ. The design and evolution of modular neural network architectures. Neural Networks 1994;7:9851004.
  • Haldar A, Mahadevan S. Probability, reliability, and statistical methods in engineering design. New York: John Wiley & Sons; 2000.
  • Guo Z, Zhao W, Lu H, Wang J. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy 2012;37(1):241-9.
  • Guan X, Liu Y, Saxena A, Celaya J, Goebel K. Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, California, USA, September 27 October 1, 2009.
  • Gu J, Azarian MH, Pecht MG. Failure prognostics of multilayer ceramic capacitors in temperature-humidity-bias conditions. In: Proceedings of the international conference on prognostics and health management, Denver, Colorado, USA, October 69, 2008.
  • Gramacy RB, Lee HK. Cases for the nugget in modeling computer experiments. Statistics and Computing 2012;22(3):71322.
  • Gomez I, Franco L, Jerez JM. Neural network architecture selection: Can function complexity help?. Neural Processing Letters 2009;30:7187.
  • Glynn PW, Iglehart DL. Importance sampling for stochastic simulations. Management Science 1989;35(11):136792.
  • Giurgiutiu V. Current issues in vibration-based fault diagnostics and prognostics. In: Proceedings of the SPIE's 9th annual international symposium on smart structures and materials and 7th annual international symposium on NDE for health monitoring and diagnostics, San Diego, California, USA, March 1721, 2002.
  • Giurgiutiu V, Cuc A. Embedded non-destructive evaluation for structural health monitoring, damage detection, and failure prevention. Shock and Vibration Digest 2005;37(2):83105.
  • Gilks WR, Berzuini C. Following a moving target-Monte Carlo inference for dynamic Bayesian models. Royal Statistical Society B 2001;63(Part 1):12746.
  • Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. 2nd ed. New York: Chapman & Hall; 2004.
  • Gelfand AE, Sahu SK. On Markov chain Monte Carlo acceleration. Journal of Computational and Graphical Statistics 1994;3:26176.
  • Frigg R, Werndl C. Entropy - A guide for the perplexed. In: Beisbart C, Hartmann S, editors. Probabilities in physics. Oxford: Oxford University Press; 2010.
  • Foster L, Waagen A, Aijaz N, Hurley M, Luis A, Rinsky J, Satyavolu C, Way MJ, Gazis P, Srivastava A. Stable and efficient Gaussian process calculations. Journal of Machine Learning Research 2009;10: 85782.
  • Firth AE, Lahav O, Somerville RS. Estimating photometric redshifts with artificial neural networks. Monthly Notices of the Royal Astronomical Society 2003;339(4):1195-202.
  • Efron B, Tibshirani RJ. An introduction to the bootstrap. Florida: Chapman & Hall/CRC; 1994.
  • Dupuis R. Application of oil debris monitoring for wind turbine gearbox prognostics and health management. In: Proceedings of the annual conference of the prognostics and health management society, Portland, Oregon, USA, October 1016, 2010.
  • Duch W, Jankowski N. Survey of neural transfer functions. Neural Computing Surveys 1999;2:163212.
  • Drucker H, Cortes C, Jackel LD, LeCun Y, Vapnik V. Boosting and other ensemble methods. Neural Computation 1994;6(6):1289301.
  • Doukim CA, Dargham JA, Chekima A. Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique. In: Proceedings of the 10th international conference on information science, signal processing and their applications, Kuala Lumpur, Malaysia, May 1013, 2010.
  • Doucet A, DeFreitas N, Gordon NJ. Sequential Monte Carlo methods in practice. New York: Springer-Verlag; 2001.
  • Ding C, Xu J, Xu L. ISHM-based intelligent fusion prognostics for space avionics. Aerospace Science and Technology 2013;29(1):2005.
  • DeFreitas, JFG. Bayesian methods for neural networks [dissertation]. Cambridge, UK: University of Cambridge; 2003.
  • DeCastro JA, Tang L, Loparo KA, Goebel K, Vachtsevanos G. Exact nonlinear filtering and prediction in process model-based prognostics. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, California, USA, September 27 October 1, 2009.
  • Dalal M, Ma J, He D. Lithium-ion battery life prognostic health management system using particle filtering framework. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2011;225:8190.
  • Daigle M, Goebel K. Multiple damage progression paths in model-based prognostics. IEEE Aerospace Conference, Big Sky, Montana, USA, March 512, 2011.
  • Coppe A, Pais MJ, Haftka RT, Kim NH. Remarks on Using a Simple Crack Growth Model in Predicting Remaining Useful Life,” Journal of Aircraft 2012;49:196573.
  • Coppe A, Haftka RT, Kim NH. Uncertainty reduction of damage growth properties using structural health monitoring. Journal of Aircraft 2010;47(6):20308.
  • Coppe A, Haftka RT, Kim NH. Uncertainty identification of damage growth parameters using nonlinear regression. AIAA Journal 2011;49(12):281821.
  • Coppe A, Haftka RT, Kim NH. Optimization of distribution parameters for estimating probability of crack detection. Journal of Aircraft 2009;46(6):20907.
  • Coppe A, Haftka RT, Kim NH, Yuan FG. Reducing uncertainty in damage growth properties by structural health monitoring. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, California, USA, September 27 October 1, 2009.
  • Chryssoloiuris G, Lee M, Ramsey A. Confidence interval prediction for neural network models. IEEE Transactions ON Neural Networks 1996;7(1):22932.
  • Cheng S, Pecht M. A fusion prognostics method for remaining useful life prediction of electronic products. In: Proceedings of the 5th annual IEEE conference on automation science and engineering, Bangalore, India, August 2225, 2009.
  • Chen SC, Lin SW, Tseng TY, Lin HC. Optimization of back-propagation network using simulated annealing approach. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, Taipei, Taiwan, October 811, 2006.
  • Chao M, Zhi S, Liu X, Min S. Neural network ensembles based on copula methods and distributed multiobjective central force optimization algorithm. Engineering Applications of Artificial Intelligence 2014;32:20312.
  • Chang JF, Hsieh PY. Particle swarm optimization based on back propagation network forecasting exchange rates. International Journal of Innovative Computing, Information and Control 2011;7(12):683747.
  • Chakraborty K, Mehrotra K, Mohan CK, Ranka S. Forecasting the behavior of multivariate time series using neural networks. Neural Networks 1992;5:96170.
  • Celaya JR, Saxena A, Saha S, Goebel K. Prognostics of power MOSFETs under thermal stress accelerated aging using data-driven and model-based methodologies. In: Proceedings of the annual conference of the prognostics and health management society. Montreal, Quebec, Canada, September 2529, 2011.
  • Carpinteri A, Paggi M. Are the Paris' law parameters dependent on each other?. Frattura ed Integrita Strutturale 2007;2:106.
  • Brillouin L. Science and information theory. Dover Publications; 1956.
  • Bretscher O. Linear algebra with applications. 3rd ed. New Jersey: Prentice Hall; 1995
  • Bolander N, Qiu H, Eklund N, Hindle E, Rosenfeld T. Physics-based remaining useful life prediction for aircraft engine bearing prognosis. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, California, USA, September 27 October 1, 2009.
  • Boden M. A guide to recurrent neural networks and backpropagation. SICS technical report, The Dallas Project, 2002.
  • Bicciato S, Pandin M, Didone G, Bello CD. Analysis of an associative memory neural network for pattern identification in gene expression data. In: Proceedings of the workshop on data mining in bioinformatics, San Francisco, California, USA, August 26, 2001.
  • Benkedjouh T, Medjaher K, Zerhouni N, Rechak S. Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, article published online 19 April 2013, DOI 10.1007/s10845-013-0774-6.
  • Belhouari SB, Bermak A. Gaussian process for nonstationary time series prediction. Computational Statistics and Data Analysis 2004;47:705712.
  • Bechhoefer E, Bernhard A, He D, Banerjee P. Use of hidden semi-Markov models in the prognostics of shaft failure. In: Proceedings of the American helicopter society 62th annual forum, Phoenix, Arizona, USA, May 911, 2006.
  • Bayes T. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London 1763;53:370418.
  • Bao T, Peng Y, Cong P, Wang J. Analysis of crack propagation in concrete structures with structural information entropy. Science China Technological Sciences 2010;53(7):19438.
  • Andrieu C, DeFreitas N, Doucet A, Jordan M. An introduction to MCMC for machine learning. Machine Learning 2003;50(1):543.
  • Andrianakis I, Challenor PG. The effect of the nugget on Gaussian process emulators of computer models. Computational Statistics and Data Analysis 2012;56:421528.
  • An D, Kim NH, Choi JH. Statistical aspects in neural network for the purpose of prognostics. In: Proceedings of the AIAA SciTech, 16th AIAA non- deterministic approaches conference, National Harbor, Maryland, USA, January 1317, 2014.
  • An D, Kim NH, Choi JH. Practical options for selecting data-driven or physics- based prognostics algorithms with reviews. Reliability Engineering and System Safety 2015;133:22336.
  • An D, Choi JH. Improved MCMC method for parameter estimation based on marginal probability density function. Journal of Mechanical Science and Technology 2013;27(6):1771-79.
  • An D, Choi JH. Efficient reliability analysis based on Bayesian framework under input variable and metamodel uncertainties. Structural and Multidisciplinary Optimization 2012;46:53347.
  • An D, Choi JH, Schmitz TL, Kim NH. In-situ monitoring and prediction of progressive joint wear using Bayesian statistics. Wear 2011;270(1112):82838.
  • An D, Choi JH, Kim NH. Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliability Engineering and System Safety 2013;115:1619.
  • An D, Choi JH, Kim NH. Practical use of accelerated test data for the prognostics methods. In: Proceedings of the annual conference of the prognostics and health management society, New Orleans, Louisiana, USA, October 1417, 2013.
  • An D, Choi JH, Kim NH. Identification of correlated damage parameters under noise and bias using Bayesian inference. Structural Health Monitoring 2012;11(3):293303.
  • An D, Choi JH, Kim NH. A Comparison study of methods for parameter estimation in the physics-based prognostics. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Honolulu, Hawaii, USA, April 2326, 2012.
  • Ahmadzadeh F, Lundberg J. Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering 2013;53:18.