Abstract
Aiming at the problem of incomplete fault data samples, a fault diagnosis method based on Support vector data description and Support vector machine (SVDD-SVM) is presented. First, the data description model is build based on the normal data samples and known fault data samples, and SVM model is built based on known fault data samples. Then the test data samples are tackled by the data description model to reject or accept. The specific categories of accepted samples are diagnosed by the SVM model and the rejected samples are unknown fault types. Tests show that this method can efficiently solve the fault diagnosis problem of incomplete fault samples.
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
Cortes C, Vapnik V (1995) Support-vector networks [J]. Mach Learn 20(3):273–297
Yu D, Yang Y, Cheng J (2005) Fault diagnosis approach for gears based on EMD and SVM [J]. Chin J Mech Eng 41(1):140–144
Lian K, Wang H, Long B (2007) Study on SVM based analog electronic system multiple fault diagnosis [J]. Chin J Sci Instrum 28(6):1029–1034
Tax DMJ, Duin RPW (2004) Support vector data description [J]. Mach Learn 54(1):45–66
Li L, Han J, Wang K, Hao W (2008) Mechanical fault diagnosis based on one-class classification [J]. J Mech Strength 30(5):697–701
Yi H (2011) A study of support vector machines based fault diagnosis and its applications [D]. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
Lv G, Chen H, Zhang H et al (2005) Fault diagnosis of power transformer based on multi-layer SVM classifier [J]. Electr Power Syst Res 75(1):9–15
Chang CC, Lin CJ (2002) LIBSVM: a library for support vector machines [OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Acknowledgement
This work is supported by National Natural Science Foundation of China under Grant (61175059), and the nature science foundation of Hebei under contract (F2014205115).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lv, F., Li, H., Sun, H., Li, X., Zhang, Z. (2016). Method of Fault Diagnosis Based on SVDD-SVM Classifier. In: Jia, Y., Du, J., Li, H., Zhang, W. (eds) Proceedings of the 2015 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48386-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-662-48386-2_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48384-8
Online ISBN: 978-3-662-48386-2
eBook Packages: EngineeringEngineering (R0)