Abstract
Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a new intelligent fault diagnosis scheme based on Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM) is proposed. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vector input to the classifiers. A novel classifier, ELM, and its variants, error-minimized ELM (EM-ELM) and online sequential ELM (OS-ELM), were introduced in this study to diagnose the fault on bearings. ELM has been proved to be extremely fast and can provide good generalization performance on many pattern recognition cases. However, preliminary ELM is a batch learning algorithm with a fixed network structure. EM-ELM and OS-ELM are the extends of the preliminary ELM to allow network to grow in the learning process and to learn data sequentially. ELM, EM-ELM and OS-ELM classifiers were evaluated on 13 fault datasets and the empirical results showed that they are really fast and perform well on all the 13 datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cline, R.E.: Representations for the generalized inverse of a partitioned matrix. Journal of the Society for Industrial and Applied Mathematics 12(3), 588–600 (1964)
Du, W., Tao, J., Li, Y., Liu, C.: Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mechanical Systems and Signal Processing 43, 57–75 (2014)
Feng, G., Huang, G.B., Lin, Q., Gay, R.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks 20(8), 1352–1357 (2009)
Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. The Johns Hopkins University Press, Baltimore (1996)
Huang, G.B.: Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks 14(2), 274–281 (2003)
Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70, 3056–3062 (2007)
Huang, G.B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71, 3060–3068 (2008)
Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 17(4), 879–892 (2006)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: A new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, July 25-29, vol. 2, pp. 985–990 (2004)
Lei, Y., He, Z., Zi, Y., Hu, Q.: Fault diagnosis of rotating machinery based on multiple anfis combination with gas. Mechanical Systems and Signal Processing 21, 2280–2294 (2007)
Liang, N.Y., Huang, G.B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 17(6), 1411–1423 (2006)
Loparo, K.A.: Bearings vibration data set. Case Western Reserve University (2014), http://csegroups.case.edu/bearingdatacenter/home
Pan, Y.N., Chen, J., Li, X.L.: Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means. Mechanical Systems and Signal Processing 24, 559–566 (2010)
Paya, B.A., Esat, I.I., Badi, M.N.M.: Artificial nerual network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical Systems and Signal Processing 11(5), 751–765 (1997)
Randall, R.B., Antoni, J.: Rolling element bearing diagnostics - a tutorial. Mechanical Systems and Signal Processing 25, 485–520 (2011)
Serre, D.: Matrices: Theory and Applications. Springer-Verlag New York, Inc. (2002)
Shen, C., Wang, D., Kong, F., Tse, P.W.: Fault diagnosis of rotating machinery based on the statstical paramets of wavelet paving and a generic support vector regressive classifier. Measurement 46, 1551–1564 (2013)
Zhu, Q.Y., Huang, G.B.: Source codes of ELM algorithm. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore (2004), http://www.ntu.edu.sg/home/egbhuang/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lan, Y., Xiong, X., Han, X., Huang, J. (2015). Multifault Diagnosis for Rolling Element Bearings Based on Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-14066-7_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
eBook Packages: EngineeringEngineering (R0)