Abstract
The aim of feature selection applied to fault diagnosis is to select an optimal feature subset that is relevant to the faults. The optimal feature subset with fewer features contains more discriminative information which can improve the performance of fault diagnosis models. A novel sequential backward feature selection method based on dissimilarity is proposed to detect the difference of features between normal and fault data. The proposed feature selection method can be used to find relevant features with fault. Furthermore, the fault diagnosis model combines the proposed feature selection method with support vector machine. Experimental results on a chemical process indicate that the proposed feature selection method is useful and superior in fault diagnosis.
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
Similar content being viewed by others
References
Saravanan, N., Kumar Siddabattuni, V.N.S., Ramachandran, K.I.: A comparative study on classification of features by SVM and PSVM extracted using morlet wavelet for fault diagnosis of spur bevel gear box. Expert Syst. Appl. 35(3), 1351–1366 (2008)
Cheng, J., Yu, D., Tang, J., Yang, Y.: Application of SVM and SVD technique based on emd to the fault diagnosis of the rotating machinery. Shock Vibr. 16(1), 89–98 (2013)
Liu, C., Jiang, D., Yang, W.: Global geometric similarity scheme for feature selection in fault diagnosis. Expert Syst. Appl. 41(8), 3585–3595 (2014)
Wang, L., Yu, J.: Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 832–840. Springer, Heidelberg (2005). doi:10.1007/11539902_102
Lu, L., Yan, J., Silva, C.W.D.: Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. J. Sound Vibr. 344, 464–483 (2015)
Sulaiman, M.A., Labadin, J.: Feature selection based on mutual information. In: International Conference on It in Asia, pp. 1–6. IEEE Press, New York (2015)
Lei, Y., He, Z., Zi, Y., Chen, X.: New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mech. Syst. Sig. Process. 22(2), 419–435 (2008)
Zhang, K., Li, Y., Scarf, P., Ball, A.: Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing 74(17), 2941–2952 (2011)
Chen, F.L., Li, F.C.: Combination of feature selection approaches with SVM in credit scoring. Expert Syst. Appl. Int. J. 37(7), 4902–4909 (2010)
Chen, Y.W., Lin, C.J.: Combining SVMs with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction. SFSC, vol. 207, pp. 315–324. Springer, Heidelberg (2006). doi:10.1007/978-3-540-35488-8_13
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. JMLR.org (2003)
Dash, M., Liu, H.: Feature Selection for Classification. IOS Press, Amsterdam (1997)
Kano, M., Hasebe, S., Hashimoto, I., Ohno, H.: Statistical process monitoring based on dissimilarity of process data. AIChE J. 48(6), 1231–1240 (2002)
Aha, D.W., Bankert, R.L.: A comparative evaluation of sequential feature selection algorithms. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data. LNS, vol. 112, pp. 199–206. Springer, New York (1996). doi:10.1007/978-1-4612-2404-4_19
Fukunaga, K., Koontz, W.L.G.: Application of the Karhunen-Loeve expansion to feature selection and ordering. IEEE Trans. Comput. C–19(4), 311–318 (1970)
Zhang, L., Zhou, W.D., Chang, P.C., Liu, J., Yan, Z., Wang, T., Li, P.Z.: Kernel sparse representation-based classifier. Neural Process. Lett. 60(1), 1684–1695 (2016)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, and by the Soochow Scholar Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xue, Y., Zhang, L., Wang, B. (2017). Dissimilarity-Based Sequential Backward Feature Selection Algorithm for Fault Diagnosis. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)