Condition-based maintenance is a cost effective maintenance strategy, in which maintenance schedules are predicted based on the results provided from diagnostics and prognostics. Although there are several reviews on diagnostics methods and CBM, a relatively small number of reviews on prognostics are available. Moreover, most of them either provide a simple comparison of different prognostics methods or focus on algorithms rather than interpreting the algorithms in the context of prognostics. The goal of this paper is to provide a practical review of prognostics methods so that beginners in prognostics can select appropriate methods for their field of applications in terms of implementation and prognostics performance. To achieve this goal, this paper introduces not only various prognostics algorithms, but also their attributes and pros and cons using simple examples.
How to Cite
neural network, remaining useful life (RUL), Crack Growth, Bayesian inference, Data-driven prognostics, prognostics and health management (PHM), particle filter, physics-based prognostics, Gaussian process regression
An, D., & Choi, J. H. (2012). Efficient reliability analysis based on Bayesian framework under input variable and metamodel uncertainties. Structural and Multidisciplinary Optimization, vol. 46, pp. 533-547.
An, D., Choi, J. H., & Kim, N. H. (2012). A comparison study of methods for parameter estimation in the physics-based prognostics. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, April 23-26, Honolulu, Hawaii.
An, D., Choi, J. H., & Kim, N. H. (2012). Identification of correlated damage parameters under noise and bias using Bayesian inference. Structural Health Monitoring, vol. 11(3), pp. 293-303.
An, D., & Choi, J. H. (2013). Improved MCMC method for parameter estimation based on marginal probability density function. Journal of Mechanical Science and Technology, vol. 27(6).
Andrieu, C., Freitas, de N., Doucet, A., & Jordan, M. (2003). An introduction to MCMC for machine learning. Machine Learning, vol. 50(1), pp. 5-43.
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.
Belhouari, S. B., & Bermak, A. (2004). Gaussian process for nonstationary time series prediction. Computational Statistics & Data Analysis, vol. 47, pp. 705-712.
Bicciato, S., Pandin, M., Didonè, G., & Bello, C. D. (2001). Analysis of an associative memory neural network for pattern identification in gene expression data. Biokdd01, Workshop on Data Mining in Bioinformatics, August 26, San Francisco, CA.
Bodén, M. (2002). A guide to recurrent neural networks and backpropagation. in The DALLASproject. Report from the NUTEK-supported project AIS-8: Application of Data Analysis with Learning Systems, 1999-2001. Holst, A. (ed.), SICS Technical Report T2002:03, SICS, Kista, Sweden.
Bretscher, O. (1995). Linear Algebra with Applications, 3rd ed. Upper Saddle River NJ: Prentice Hall.
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.
Chang, J. F., & Hsieh, P. Y. (2011). Particle swarm optimization based on back propagation network forecasting exchange rates. International Journal of Innovative Computing, Information and Control, vol. 7(12), pp. 6837-6847.
Chen, S. C., Lin, S. W., Tseng, T. Y., & Lin, H. C. (2006). Optimization of back-propagation network using simulated annealing approach. IEEE International Conference on Systems, Man, and Cybernetics, October 8-11, Taipei, Taiwan.
Cheng, S., & Pecht, M. (2009). A fusion prognostics method for remaining useful life prediction of electronic products. 5th Annual IEEE Conference on Automation Science and Engineering, August 22-25, Bangalore, India.
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.
Coppe, A., Haftka, R. T., Kim, N. H., & Yuan, F. G. (2009). Reducing uncertainty in damage growth properties by structural health monitoring. Annual Conference of the Prognostics and Health Management Society, September 27-October 1, San Diego, CA.
Coppe, A., Pais, M. J., Haftka, R. T., & Kim, N. H. (2012). Remarks on using a simple crack growth model in predicting remaining useful life. Journal of Aircraft, vol. 49, pp. 1965-1973.
Daigle, M., & Goebel, K. (2011). Multiple damage progression paths in model-based prognostics. IEEE Aerospace Conference, March 05-12, Big Sky, Montana.
DeCastro, J. A., Tang, L., Loparo, K. A., Goebel, K., & Vachtsevanos, G. (2009). Exact nonlinear filtering and prediction
in process model-based prognostics. Annual Conference of the Prognostics and Health Management Society, September 27-October 1, San Diego, CA.
Dickson, D. C. M., & Waters, H. R. (1993). Gamma processes and finite time survival probabilities. Astin Bulletin, vol. 23(2), pp. 259-272.
Doucet, A., De Freitas, N., & Gordon, N. J. (2001). Sequential Monte Carlo methods in practice. Springer- Verlag.
Doukim, C. A., Dargham, J. A., & Chekima, A. (2010). Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique. 10th International Conference on Information Science, Signal Processing and their Applications, May 10-13, Kuala Lumpur, Malaysia.
Drucker, H., Cortes, C., Jackel, L. D., LeCun, Y., & Vapnik, V. (1994). Boosting and other ensemble methods. Neural Computation, vol. 6(6), pp. 1289-1301.
Duch, W., & Jankowski, N. (1999). Survey of neural transfer functions. Neural Computing Surveys, vol. 2, pp. 163-212.
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. FL: Chapman & Hall/CRC.
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, pp. 1195-1202.
Foster, L., Waagen, A., Aijaz, N., Hurley, M., Luis, A., Rinsky, J., Satyavolu, C., Way, M. J., Gazis, P., & Srivastava, A. (2009). Stable and efficient gaussian process calculations. Journal of Machine Learning Research, vol. 10, pp. 857-882.
Gelfand, A. E., & Sahu, S. K. (1994). On Markov chain Monte Carlo acceleration. Journal of Computational and Graphical Statistics, vol. 3, pp. 261-276.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis. NY: Chapman and Hall. Gilks, W. R., & Berzuini, C. (2001). Following a moving target-Monte Carlo inference for dynamic Bayesian models. Royal Statistical Society B, vol. 63, Part 1, pp. 127-146.
Giurgiutiu, V. (2002). Current issues in vibration-based fault diagnostics and prognostics. SPIE's 9th Annual International Symposium on Smart Structures and Materials and 7th Annual International Symposium on NDE for Health Monitoring and Diagnostics, March 17-21, San Diego, CA.
Goebel, K., Saha, B., & Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. Proceedings of the 62nd Meeting of the Society For Machinery Failure Prevention Technology (MFPT), May 6-8, Virginia Beach, VA.
Gómez, I., Franco, L., & Jérez, J. M. (2009). Neural network architecture selection: can function complexity help?. Neural Processing Letters, vol. 30, pp. 71-87.
Gouriveau, R., Dragomir, O., & Zerhouni, N. (2008). A fuzzy approach of online reliability modeling and estimation. Proceedings of the 34th European Safety Reliability & Data Association, ESReDA Seminar and 2nd Joint ESReDA/ESRA Seminar on Supporting Technologies for Advanced Maintenance Information Management, May 13-14, San Sebastian, Spain.
Grall, A., Bérenguer, C., & Dieulle, L. (2002). A condition- based maintenance policy for stochastically deteriorating systems. Reliability Engineering and System Safety, vol. 76(2), pp. 167-180.
Gu, J., Azarian, M. H., & Pecht, M. G. (2008). Failure prognostics of multilayer ceramic capacitors in temperature-humidity-bias conditions. 2008 International Conference on Prognostics and Health Management, October 6-9, Denver, Colorado.
Guan, X., Liu, Y., Saxena, A., Celaya, J., & Goebel, K. (2009). Entropy-Based probabilistic fatigue damage prognosis and algorithmic performance comparison. Annual Conference of the Prognostics and Health Management Society, September 27-October 1, San Diego, CA.
Happel, B. L. M., & Murre, J. M. J. (1994). The design and evolution of modular neural network architectures. Neural Networks, vol. 7, pp. 985-1004.
He, Y., Tan, Y., & Sun, Y. (2004) Wavelet neural network approach for fault diagnosis of analogue circuits. IEE Proceedings-Circuits, Devices and Systems, vol. 151(4), pp. 379-384.
Higuchi, T. (1997). Monte Carlo filter using the genetic algorithm operators. Journal of Statistical Computation and Simulation, vol. 59(1), pp. 1-23.
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.
Jacobs, R. A. (1995). Methods for combining experts’ probability assessments. Neural Computation, vol. 7(5), pp. 867-888.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, vol. 20(7), pp. 1483-1510.
Julier, S. J., & Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, vol. 92(3), pp. 401-422.
Juricic, D., Znidarsic, A., & Fussel, D. (1997). Generation of diagnostic trees by means of simplified process models and machine learning. Engineering Applications of Artificial Intelligence, vol. 10(1), pp. 15-29.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transaction of the ASME- Journal of Basic Engineering, No. 82, pp. 35-45.
Khawaja, T., Vachtsevanos, G., & Wu, B. (2005). Reasoning about uncertainty in prognosis: a confidence prediction neural network approach. 2005 Annual Meeting of the North American Fuzzy Information Processing Society, June 22-25, Ann Arbor, Michigan.
Kim, S., & Park, J. S. (2011). Sequential Monte Carlo filters for abruptly changing state estimation. Probabilist Engineering Mechcnics, vol. 26, pp. 194-201.
Kitagawa, G. (1987). Non–Gaussian state space modeling of nonstationary time series (with discussion). Journal of the American Statistical Association, vol. 82(400), pp. 1032-1063.
Kleijnen, J. P. C. (1995). Statistical validation of simulation models. European Journal of Operational Research, vol. 87, pp. 21-34.
Kramer, S. C., & Sorenson, H. W. (1998). Bayesian parameter estimation. IEEE Transactions on Automatic Control, vol. 33(2), pp. 217-222.
Krogh, A. (2008). What are artificial neural networks?. Nature Biotechnology, vol. 26(2), pp. 195-197.
Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning. In Tesauro G., Touretzky D., & Leen T. (Eds.), Advances in Neural Information Processing Systems (231-238). The MIT Press.
Lawrence, N., Seeger, M., & Herbrich, R. (2003). Fast sparse Gaussian Process methods: the information vector machine. In Becker S., Thrun S., & Obermayer K. (Eds.), Advances in Neural Information Processing Systems(625-
632), Vol. 15, MIT Press.
Lawrence, S., Giles, C. L., & Tsoi, A. C. (1998). What size neural network gives optimal generalization? convergence properties of backpropagation. Technical Reports. UM Computer Science Department, UMIACS.
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, D., Pang, J., Zhou, J., & Peng, Y. (2012). Data-driven prognostics for lithium-ion battery based on Gaussian process regression. 2012 Prognostics and System Health Management Conference, May 23-25, Beijing, China.
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.
Liu, P., & Li, H. (2004). Fuzzy Neural Network Theory and Application. Singapore: World Scientific.
MacKay, D. J. C. (1997). Gaussian processes-a replacement for supervised neural networks?, Tutorial lecture notes for NIPS, UK,
Mao, K. Z., Tan, K.-C., & Ser, W. (2000). Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks, vol. 11(4), pp. 1009-1016.
Martin, K. F. (1994). A review by discussion of condition monitoring and fault diagnosis in machine tools.
International Journal of Machine Tools and Manufacture, vol. 34(4), pp. 527-551.
Melkumyan, A., & Ramos, F. (2009). A sparse covariance function for exact gaussian process inference in large datasets. IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, July 11-17, Pasadena, CA.
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.
Mohanty, S., Das, D., Chattopadhyay, A., & Peralta, P. (2009). Gaussian process time series model for life prognosis of metallic structures. Journal of Intelligent Material Systems and Structures, vol. 20, pp. 887-896.
Naftaly, U., Intrator, N., & Horn, D. (1997). Optimal ensemble averaging of neural networks. Network: Computation in Neural Systems, vol. 8(3), pp. 283-296.
Navone, H. D., Granitto, P. M., Verdes, P. F., & Ceccatto, H. A. (2001). A Learning algorithm for neural network ensembles. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial. No. 12, pp 70-74.
Nawi, N. M., Ransing, R. S., & Ransing, M. R. (2007). An improved conjugate gradient based learning algorithm for back propagation neural networks. International Journal of Computational Intelligence, vol. 4(1), pp. 46-55.
Neal, R. M. (1998). Regression and classification using Gaussian process priors. In Bernardo J. M., Berger J. O., Dawid A. P., & Smith A. F. M. (Eds.), Bayesian statistics (475-501), Vol. 6, NY: Oxford University Press.
Orchard, M. E., & Vachtsevanos, G. J. (2007). A particle filtering approach for on-line failure prognosis in a planetary carrier plate. International Journal of Fuzzy Logic and Intelligent Systems, vol. 7(4), pp. 221-227.
Ostafe, D. (2005). Neural network hidden layer number determination using pattern recognition techniques. 2nd Romanian-Hungarian Joint Symposium on Applied Computational Intelligence, May 12-14, Timisoara, Romania.
Paciorek, C., & Schervish, M. J. (2004). Nonstationary covariance functions for gaussian process regression. In Thrun S., Saul L., & Schölkopf B. (Eds.), Advances in neural information processing systems, Vol. 16, MIT Press.
Pandey, M. D., & Noortwijk, J. M. V. (2004). Gamma process model for time-dependent structural reliability analysis. In Watanabe E., Frangopol D. M., & Utsonomiya T. (Eds.), Bridge maintenance, safety, management and cost (101-102). London: Taylor and Francis Group.
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.
Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, vol. 33(3), pp. 1065-1076.
Perrone, M. P., & Cooper, L. N. (1993). When networks disagree: ensemble method for neural networks. In Mammone R. J. (Ed.), Neural Networks for Speech and Image processing, Chapman-Hall.
Petalas, P., Spyridonos, P., Glotsos, D., Cavouras, D., Ravazoula, P., & Nikiforidis, G. (2003). Probabilistic neural network analysis of quantitative nuclear features in predicting the risk of cancer recurrence at different follow-up times. Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, September 18-20, Rome, Italy.
Psichogios, D. C., & Ungar, L. H. (1992). A hybrid neural network-first principles approach to process modeling. AIChE Journal, vol. 38(10), pp. 1499-1511.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, vol. 77(2), pp. 257-286.
Rasmussen, C. E., & Williams, C. K. I., (2006). Gaussian Processes for Machine Learning. Cambridge, MA: The MIT Press.
Rebba, R., Huang, S., Liu, Y., & Mahadevan, S. (2006). Statistical validation of simulation models. International Journal of Materials and Product Technology, vol. 25(1/2/3), pp. 164-181.
Rebba, R., Mahadevan, S., & Huang, S. (2006). Validation and error estimation of computational models. Reliability Engineering and System Safety, vol. 91, pp. 1390-1397.
Rovithakis, G. A., Maniadakis, M., & Zervakis, M., (2004). A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 34(1), pp. 695-702.
Rubin, D. B. (1998). Using the SIR algorithm to simulate posterior distributions. In Bernardo J. M., DeGroot M. H., Lindley D. V., & Smith A. F. M. (Eds.), Bayesian statistics(395-402), vol. 3, Cambridge, MA: Oxford University Press.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation.
Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: foundations, MIT Press, pp. 318-362.
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, vol. 4(4), pp. 409-423.
Saha, B., Goebel, K., & Christophersen, J. (2009). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement & Control, vol. 31(3-4), pp. 293-308.
Salomon, R., & Hemmen, J. L. V. (1996). Accelerating backpropagation through dynamic self-adaptation. Neural Networks, vol. 9(4), pp. 589-601.
Samanta, B., & Al-Balushi, K. R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, vol. 17(2), pp. 317–328.
Samuel, P. D., & Pines, D. J. (2005). A review of vibration- based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration, vol. 282(1-2), pp. 475-508.
Sankavaram, C., Pattipati, B., Kodali, A., Pattipati, K., Azam, M., Kumar, S., & Pecht, M. (2009). Model- based and data-driven prognosis of automotive and electronic systems. 5th Annual IEEE Conference on Automation Science and Engineering, August 22-25, Bangalore, India.
Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The design and analysis of computer experiments. NY: Springer Verlag.
Sargent, R. G. (2009). Verification and validation of simulation models. Proceedings of the 2009 Winter Simulation Conference, December 13-16, Austin, TX.
Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, vol. 14(2), pp. 69-106.
Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H (2011). Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, vol. 213, pp. 1-14.
Singh, G. K., & Al Kazzaz, S. A. S. (2003). Induction machine drive condition monitoring and diagnostic research-a survey. Electric Power Systems Research, vol. 64, pp. 145-158.
Smola, A. J., & Bartlett, P. L. (2001). Sparse greedy Gaussian process regression. In Leen T. K., Dietterich T. G., & Tresp V. (Eds.), Advances in Neural Information Processing Systems (619-625), Vol. 13. MIT Press.
Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, vol. 3, pp. 109-118.
Srivastava, A. N., & Das, S. (2009). Detection and prognostics on low-dimensional systems. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, vol. 39(1), pp. 44-54.
Storvik, G. (2002). Particle filters in state space models with the presence of unknown static parameters. IEEE Transactions on Signal Processing, vol. 50(2), pp. 281- 289.
Subudhi, B, Jena, D., & Gupta, M. M. (2008). Memetic differential evolution trained neural networks for nonlinear system identification. IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, December 8 -10, Kharagpur, India.
Sugumaran , V., Sabareesh, G. R., & Ramachandran, K. I. (2008). Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Expert Systems with Applications, vol. 34, pp. 3090-3098.
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.
Wang, W. P., Liao, S., & Xing, T. W. (2009). Particle filter for state and parameter estimation in passive ranging. IEEE International Conference on Intelligent Computing and Intelligent Systems. November 20-22, Shanghai, China.
Williams, C. K. I. (1997). Computing with infinite networks. In Mozer M. C., Jordan M. I., & Petsche T. (Eds.), Advances in neural information processing systems, Cambridge, MA: MIT Press.
Xing, Y., Miao, Q., Tsui, K.-L., & Pecht, M. (2011). Prognostics and health monitoring for lithium-ion battery,” 2011 IEEE International Conference, July 10- 12, Beijing, China.
Xing, Y., Williard, N., Tsui, K.-L., & Pecht, M. (2011). A comparative review of prognostics-based reliability methods for lithium batteries. Prognostics and System Health Management Conference, May 24-25, Shenzhen, China.
Xu, J., & Xu, L. (2011). Health management based on fusion prognostics for avionics systems,” Journal of Systems Engineering and Electronics, vol. 22(3), pp. 428–436.
Yam, R. C. M., Tse, P. W., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. International Journal of Advanced Manufacturing Technology, vol. 17(5), pp.383-391.
Yan, R., & Gao, R. X. (2007). Approximate entropy as a diagnostic tool for machine health monitoring. Mechanical Systems and Signal Processing, vol. 21, pp. 824–839.
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. Zhang, J., & Lee, J. (2011). A review on prognostics and health monitoring of li-ion battery. Journal of Power Sources, vol. 196, pp. 6007-6014.
Zio, E., & Maio, F. D. (2010). A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering and System Safety, vol. 95, pp. 49-57.
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