An Evidential Evolving Prognostic Approach and its Application to PRONOSTIA’s Data Streams
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The research activity in the PHM community is in full bloom and many efforts are being made to develop more realistic and reliable methodologies. However, there still exist very few real-world applications due to the complexity of the systems of interest. Nonlinear dynamical systems identification and behavior prediction are difficult problems encountered in prognosis. The difficulty in switching from theory to practice can partially be explained by the existence of different kinds of uncertainty at each step of the implementation that must be taken into account with the appropriate tools. In this paper, we propose an evolving multi-modeling approach for the detection, the adaptation and the combination of local models in order to analyze complex systems behavior. It relies on belief functions in order to take into consideration the uncertainty related to the available data describing the system as well as the uncertainty generated by the nonlinearity of the system. The information of doubt explicitly represented in the belief functions framework is exploited to properly segment the data and take into account the uncertainty related to the transitions between the operating regions. The proposed algorithm is validated on a data provided by PRONOSTIA platform.
How to Cite
##plugins.themes.bootstrap3.article.details##
Online evidential clustering, Multi-modeling, Belief functions theory, Behavior modeling, Virtual centroids
Angelov, P., Filev, D., & Kasabov, N. (2010). Evolv- ing intelligent systems: Methodology and applications. New york: IEEE Press Series on Computational Intelligence, John Wiley.
Angelov, P., Lughofer, E., & Zhou, X. (2008). Evolving fuzzy classifiers using different model architectures. Fuzzy Sets and Systems, 3160 – 3182.
Boukhris, A., Mourot, G., & Ragot, J. (2000). Nonlinear dynamic system identification: a multiple-model approach. International Journal of Control, 72, 591 – 604.
Chandrashekhar, M., & Ganguli, R. (2009). Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation. Mechanical Systems and Signal
Processing, 23(2), 384 – 404.
Cobb, B. R., & Shenoy, P. P. (2006). On the plausibility transformation method for translating belief function models to probability models. International Journal of Approximate Reasoning, 41, 314 – 330.
Dubois, D., Prade, H., & Smets, P. (2001). New Semantics for Quantitative Possibility Theory. In 2nd International Symposium on Imprecise Probabilities and Their Applications. Ithaca, New York.
El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2011). Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system. Microelectronics Reliability, 51, 310 – 330.
G. J.Klir. (2006). Uncertainty and information. Foundations of generalized information theory. New York: Wiley.
Georgieva, O., & Filev, D. (2009). Gustafson-Kessel algorithm for evolving data stream clustering. In International Conference on Computer Systems and Technologies - CompSysTech 09.
Haag, T., Herrmann, J., & Hanss, M. (2010). Identification procedure for epistemic uncertainties using inverse fuzzy arithmetic. Mechanical Systems and Signal Processing, 24(7), 2021 – 2034.
He, H., & Garci, E. A. (2009). Learning from Imbalanced Data. IEEE Tr. On Knowledge and Data Engineering, 21, 1263 – 1284.
Madani, K., Rybnik, M., & Chebira, A. (2003). Non Linear Process Identification Using a Neural Network Based Multiple Models Generator. LNCS series, 647 – 654.
Masson, M.-H., & Denoeux, T. (2008). ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition, 41(4), 1384 – 1397.
Murray-Smith, R. (1997). Multiple model approaches to modeling and control. Taylor & Francis Publishers.
Nelles, O. (1995). On the identification with neural networks as series-parallel and parallel models. In International Conference on ANN. Paris, France.
Ramdani, M., Mourot, G., & Ragot, J. (2005). A Multi- Modeling Strategy based on Belief Function Theory. In CDC-ECC 05.
Serir, L., Ramasso, E., Nectoux, P., Bauer, O., & Zerhouni, N. (2011). Evidential Evolving Gustafson-Kessel Algorithm (E2GK) and Its Application to PRONOSTIA’s Data Streams Partitioning. In IEEE Int. Conf. on Decision and Control (p. 8273-8278).
Serir, L., Ramasso, E., & Zerhouni, N. (2011). E2GK- pro: An evidential evolving multimodeling approach for systems behavior prediction. In PHM Society. Mon- treal, Canada.
Serir, L., Ramasso, E., & Zerhouni, N. (2012, July). Evidential evolving Gustafson-Kessel algorithm for online data streams partitioning using belief function theory. International Journal of Approximate Reasoning, 53, 747 – 768.
Shafer, G. (1976). A mathematical theory of evidence. Prince- ton University Press.
Smets, P., & Kennes, R. (1994). The Transferable Belief Model. Artificial Intelligence, 66, 191 – 234.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 15, 116 –
132.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.