Integration of Hierarchical Classification to Improve the Prognostic Results of Fuzzy Similarity

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

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

Published Jul 5, 2016
Jie Liu Enrico Zio

Abstract

Fuzzy similarity has been widely used for prognostics. Normally, a library of failure scenarios is available and a number of them most similar to the observed scenario are selected to generate the Remaining Useful Life (RUL) of the observed scenario, using a distance weighted-sum approach. By clustering the library of failure scenarios, those most similar to the observed scenario can be selected considering the strength of membership to the different clusters. To this aim, in this paper, hierarchical classification is integrated into the fuzzy similarity approach. First, hierarchical classification is built by Support Vector Machine (SVM), considering different failure modes. Then, for the observed scenario, fuzzy similarity is applied to select the most similar failure scenarios from different classes, considering the membership of the observed scenario to the different clusters. The selected scenarios are aggregated to generate the RUL along the observed scenario. A real case study of a system composed of a pneumatic valve and a centrifugal pump in a nuclear power plant is considered to verify the RUL prediction power of the proposed framework.

How to Cite

Liu, J., & Zio, E. (2016). Integration of Hierarchical Classification to Improve the Prognostic Results of Fuzzy Similarity. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1657
Abstract 68 | PDF Downloads 63

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

Keywords

PHM

References
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Guepie, B. K., & Lecoeuche, S. (2015, June). Similarity-based residual useful life prediction for partially unknown cycle varying degradation. In Prognostics and Health Management (PHM), 2015 IEEE Conference on (pp. 1-7). IEEE.
Gunn, S. R. (1998). Support vector machines for classification and regression. ISIS technical report, 14.
Li, S. T., & Ho, H. F. (2009). Predicting financial activity with evolutionary fuzzy case-based reasoning. Expert Systems with Applications, 36(1), 411-422.
Li, X., & Yao, X. (2005). Application of fuzzy similarity to prediction of epileptic seizures using EEG signals. In Fuzzy Systems and Knowledge Discovery (pp. 645-652). Springer Berlin Heidelberg.
Lin, Y. H., Li, Y. F., & Zio, E. (2015). Fuzzy reliability assessment of systems with multiple-dependent competing degradation processes. Fuzzy Systems, IEEE Transactions on, 23(5), 1428-1438.
Riordan, D., & Hansen, B. K. (2002). A fuzzy case-based system for weather prediction. Engineering Intelligent Systems for Electrical Engineering and Communications, 10(3), 139-146.
Rocco, C. M., & Moreno, J. A. (2002). Fast Monte Carlo reliability evaluation using support vector machine. Reliability Engineering & System Safety, 76(3), 237-243.
Rocco, C. M., & Zio, E. (2007). A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems. Reliability Engineering & System Safety, 92(5), 593-600.
Senjyu, T., Higa, S., & Uezato, K. (1998, July). Future load curve shaping based on similarity using fuzzy logic approach. In Generation, Transmission and Distribution, IEE Proceedings- (Vol. 145, No. 4, pp. 375-380). IET.
Widyantoro, D. H., & Yen, J. (2000, May). A fuzzy similarity approach in text classification task. In IEEE International conference on fuzzy systems (Vol. 2, pp. 653-658).
Zio, E., & Di Maio, F. (2010). A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering & System Safety, 95(1), 49-57.
Section
Technical Papers