Abstract
In this paper a novel approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented. In the proposal, the data was first pre-processed using the Noise Clustering algorithm. This permits to eliminate outliers and reduce the confusion as a first part of the classification process. Secondly, the Kernel Fuzzy C-means algorithm was used to achieve greater separability among the classes, and reduce the classification errors. Finally, it can be implemented a step for optimizing the parameters of the NC and KFCM algorithms. The proposed approach was validated using the iris benchmark data sets. The obtained results indicate the feasibility of the proposal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asuncion, A., Newman, D.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA (2007). http://archive.ics.uci.edu/beta
Bedoya, C., Uribe, C., Isaza, C.: Unsupervised feature selection based on fuzzy clustering for fault detection of the tennessee eastman process. Advances in Artificial Intelligence. LNAI, vol. 7637, pp. 350–360. Springer (2012)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
Chintalapudi, K.K., Kam, M.: A noise resistant fuzzy C-means algorithm for clustering. In: IEEE Conference on Fuzzy Systems Proceedings, vol. 2, pp. 1458–1463 (1998)
Dave, R.N.: Characterization and detection of noise in clustering. Pattern Recogn. Lett. 12, 657–664 (1991)
Dave, R.N., Krishnapuram, R.: Robust clustering methods: a unified view. IEEE Trans. Fuzzy Syst. 5, 270–293 (1997)
Fan, J., Wang, Y.: Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis. Inf. Sci. 259, 369–379 (2014)
Gosain, A., Dahika, S.: Performance analysis of various fuzzy clustering algorithms: a review. In: 2016 Proceedings of the 7th International Conference on Communication, Computing and Virtualization, vol. 79, pp. 100–111 (2016)
Hwang, I., Kim, S., Kim, Y., Seah, C.: A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans. Control Syst. Technol. 18, 636–656 (2010)
Isermann, R.: Fault-Diagnosis Applications: Model-based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors and Fault-tolerant Systems. Springer, Heidelberg (2011)
Kaur, P.: A density oriented fuzzy C-means clustering algorithm for recognising original cluster shapes from noisy data. Int. J. Innov. Comput. Appl. 3, 77–87 (2011)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1, 98–110 (1993)
Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy C-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13, 517–530 (2005)
Qin, S.J.: Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control 36(2), 220–234 (2012)
Sina, S., Sadough, Z.N., Khorasani, K.: Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing 125, 153–165 (2014)
Wang, J., Hu, H.: Vibration-based fault diagnosis of pump using fuzzy technique. Measurement 39, 176–185 (2009)
Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32, 37–50 (2004)
Acknowledgements
The authors acknowledge the financial support provided by the project TIN2014-55024-P from the Spanish Ministry of Economy and Competitiveness and P11-TIC-8001 from the Andalusian Government, both with FEDER funds; FAPERJ, Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico; CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, research supporting agencies from Brazil; and CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Rodríguez Ramos, A., Bernal de Lázaro, J.M., da Silva Neto, A.J., Cruz Corona, C., Verdegay, J.L., Llanes-Santiago, O. (2018). An Approach to Fault Diagnosis Using Fuzzy Clustering Techniques. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-319-66827-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-66827-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66826-0
Online ISBN: 978-3-319-66827-7
eBook Packages: EngineeringEngineering (R0)