Abstract
Operating status of roller bearings directly affects the safety of the urban rail vehicle, so accurate identification of the state has considerably practical significance. The fault detection and isolation method of the roller bearing operational was performed by the comprehensive utilization of fast independent component analysis (Fast ICA), and kernel fisher discriminant. Fast ICA method was used to denoise and extract signal with fault frequency. The fault characteristic signal separation used Fast ICA with the normal signal applied as a signal source. The Pearson product-moment correlation coefficient had been used to determine which component was the extracted one we needed. Then eight indicators were extracted as roller bearings’ state features after Fast ICA. Based on the features under different states, the states of roller bearings on the urban rail vehicles were identified by the K-Fisher Discriminant. The experiment results indicated that the accuracies of the multiple states identification was more than 96 %, and verified the superiority of the proposed method.
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
Choe HC, Wan Y, Chan AK (1997) Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features. Proc SPIE Int Soc Opt Eng 3078:480–496
Qin TL, Yang Y, Cheng H (2008) Rolling bearing state identification based on intrinsic mode function energy moment and bp neural network. J Vibr Meas Diagn 28:229–232
Hyvarinen A, Oja EA (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Networks 10(3):626–634
Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9(7):1483–1492
Lee TW, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput 11(2):417–441
Mikat S, fitscht G, Weston J, Scholkopft B (1999) Mullert K-R Fisher discriminant analysis with kernel. In: Hu Y-H, Larsen J, Wilson E, Douglas S (eds) Neural networks for signal processing, IX, IEEE pp 41–48
Nikias CL, Raghuveer MR (1987) Bispectrum estimation: a digital signal processing framework. Proc IEEE 75(5):869–891
Huang C, Fan x, Chen T (2006) Train’s online fault diagnosis technology and its application. National defence industry press, Beijing, p 7
http://zh.wikipedia.org/wiki/%E7%9B%B8%E5%85%B3%E7%B3%BB%E6%95%B0
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kou, L., Qin, Y., Cheng, X., Zhang, Z. (2015). A Fault Detection and Isolation Method Based on R-Fast ICA and K-Fisher for Roller Bearings on the Urban Rail Vehicles. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46463-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-662-46463-2_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46462-5
Online ISBN: 978-3-662-46463-2
eBook Packages: EngineeringEngineering (R0)