Statistical Aspects in Neural Network for the Purpose of Prognostics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Dawn An Nam-Ho Kim Joo-Ho Choi

Abstract

Neural network (NN) is a representative data-driven method, which is one of prognostics approaches that is to predict future damage/degradation and the remaining useful life of in-service systems based on the damage data measured at previous usage conditions. Even though NN has a wide range of applications, there are a relatively small number of literature on prognostics compared to the usage in other fields such as diagnostics and pattern recognition. Especially, it is difficult to find studies on statistical aspects of NN for the purpose of prognostics. Therefore, this paper presents the aspects of statistical characteristics of NN that are presumable in practical usages, which arise from measurement data, weight parameters related to the neural network model, and loading conditions. The Bayesian framework and Johnson distribution are employed to handle uncertainties, and crack growth problem is addressed as an example.

How to Cite

An, D., Kim, N.-H., & Choi, J.-H. (2014). Statistical Aspects in Neural Network for the Purpose of Prognostics. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1547
Abstract 59 | PDF Downloads 22

##plugins.themes.bootstrap3.article.details##

Keywords

neural network, Data-driven prognostics, Bayesian framework, Statistical uncertainties, Johnson distribution

References
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, vol. 53, pp. 370-418.
Chakraborty, K., Mehrotra, K., Mohan, C. K., & Ranka, S. (1992). Forecasting the behavior of multivariate time series using neural networks. Neural Networks, vol. 5, pp. 961-970.
Chryssoloiuris, G., Lee, M., & Ramsey, A. (1996). Confidence interval prediction for neural network models. IEEE Transactions on Neural Networks, vol. 7(1), pp. 229-232.
Duch, W. & Jankowski, N. (1999). Survey of neural transfer functions. Neural Computing Surveys, vol. 2, pp. 163- 212.
Firth, A. E., Lahav, O., & Somerville, R. S. (2003). Estimating photometric Redshifts with artificial neural networks. Monthly Notices of the Royal Astronomical Society, vol. 339, 1195, DOI: 10.1046/j.1365-8711.2003.06271.
Freitas, de J. F. G. (2003). Bayesian methods for neural networks. Doctoral dissertation. University of Cambridge, UK.
Huang, X., Torgeir, M., & Cui, W. (2008). An engineering model of fatigue crack growth under variable amplitude loading. International Journal of Fatigue, vol. 30(1), pp. 2-10.
Johnson, N. L. (1949). Systems of frequency curves generated by methods of translation. Biometrika, vol. 36, pp. 149-176.
Lee, J., Qiu, H., Yu, G., Lin, J., & Rexnord Technical Services. (2007). 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, CA.
Leonard, J. A., Kramer, M. A., & Ungar, L. H. (1992). A neural network architecture that computes its own reliability. Computers in Chemical Engineering, vol. 16(9), pp. 819-835.
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010). An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. Annual Conference of the Prognostics and Health Management Society, October 10-16, Portland, Oregon.
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on System, Man and Cybernetics, vol. 38(5), pp. 1156-1168.
Mohanty, S., Teale, R., Chattopadhyay, A., Peralta, P., & Willhauck, C. (2007). Mixed Gaussian process and state-space approach for fatigue crack growth prediction. International Workshop on Structural Heath Monitoring, vol. 2, pp. 1108-1115.
Neal, R. M. (1995). Bayesian learning for neural networks. Doctoral dissertation. University of Toronto, Ontario, Canada.
Paris, P. C. & Erdogan, F. (1963). A critical analysis of crack propagation laws. Transactions of the ASME, Journal of Basic Engineering, Series D, vol. 85(3), pp. 528-534.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Ex-plorations in the Microstructure of Cognition, vol. 1: foundations, MIT Press, pp. 318-362.
Schwabacher, M. A. (2005). A survey of data-driven prognostics. AIAA Infotech@Aerospace Conference, September 26-29, Reston,VA.
Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, vol. 14(2), pp. 69-106.
Svozil, D., Kvasnička, V., & Pospíchal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, vol. 39, pp. 43-62.
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, vol. 1, pp. 211-244.
Tran, V. T. & Yang, B. S. (2009). Data-driven approach to machine condition prognosis using least square regression tree. Journal of Mechanical Science and Technology, vol. 23, pp. 1468-1475.
Veaux, R. D., Schumi, J., Schweinsberg, J., & Ungar, L. H. (1998). Prediction intervals for neural networks via nonlinear regression. Technometrics, vol. 40(4), pp. 273-282.
Yan, J. & Lee, J. (2007). A hybrid method for on-line performance assessment and life prediction in drilling operations. IEEE International Conference on Automation and Logistics, August 18-21, Jinan, Shandong , China.
Yang, L., Kavli, T., Carlin, M., Clausen, S., & Groot, P. F. M. (2000). An evaluation of confidence bound estimation methods for neural networks. European Symposium on Intelligent Techniques 2000, September 14-15, Aachen, Germany.
Yao, X. (1999). Evolving Artificial neural networks. Proceedings of the IEEE, vol. 87(9), pp. 1423-1447.
Section
Technical Papers

Similar Articles

1 2 3 4 5 6 7 > >> 

You may also start an advanced similarity search for this article.