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Erschienen in:
Advanced Signal Processing for Structural Health Monitoring Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Sparse Representation of the Transients in Mechanical Signals

verfasst von : Zhongkui Zhu, Wei Fan, Gaigai Cai, Weiguo Huang, Juanjuan Shi

Erschienen in: Structural Health Monitoring

Verlag: Springer International Publishing

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Abstract

This chapter focuses on the sparse representation of the transients in mechanical signals. Sparse representation means that the signal can be represented by an optimal linear combination of atoms by a specialized over-complete dictionary, leading to the sparsity of representation coefficients. Signal sparse representation consists of two main aspects, i.e., dictionary construction and optimization solution. This chapter also presents the applications of sparse representation, mainly in mechanical fault feature detection, such as fault detection of rolling bearings, gearboxes and compound bearing faults.

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Vorheriges Kapitel Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery
Nächstes Kapitel Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition
Literatur
1.
Bruckstein A.M., Donoho D.L., Elad M., “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Review, 2009, 51(1): 34–81.
2.
Yang J., Wright J., Huang T.S., et al., “Image super-resolution via sparse representation,” IEEE Transactions on Image Processing, 2010, 19(11): 2861–2873.
3.
Wright J., Ma Y., Mairal J., et al., “Sparse representation for computer vision and pattern recognition,” Proceedings of the IEEE, 2010, 98(6): 1031–1044.
4.
Liu H., Liu C., Huang Y., “Adaptive feature extraction using sparse coding for machinery fault diagnosis,” Mechanical Systems and Signal Processing, 2011, 25(2): 558–574.
5.
Peng F., Yu D., Luo J., “Sparse signal decomposition method based on multi-scale Chirplet and its application to the fault diagnosis of gearboxes,” Mechanical Systems and Signal Processing, 2011, 25(2): 549–557.
6.
Yang H., Mathew J., Ma L., “Fault diagnosis of rolling element bearings using basis pursuit,” Mechanical Systems and Signal Processing, 2005, 19(2): 341–356.
7.
Elad M., Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer, Israel, 2010.
8.
Rubinstein R., Bruckstein A.M., Elad M., “Dictionaries for sparse representation modeling,” Proceedings of the IEEE, 2010, 98(6): 1045–1057.
9.
Aviyente S., “Compressed sensing framework for EEG compression,” IEEE/SP 14th Workshop on Statistical Signal Processing, 2007: 181–184.
10.
Ghoraani B., Krishnan S., “Time–frequency matrix feature extraction and classification of environmental audio signals,” IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(7): 2197–2209.
11.
Yeste-Ojeda O.A., Grajal J., López-Risue G., “Atomic decomposition for radar applications,” IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(1): 187–200.
12.
Zou H., Dai Q., Wang R., et al., “Parametric TFR via windowed exponential frequency modulated atoms,” IEEE Signal Processing Letters, 2001, 8(5): 140–142.
13.
Li X., Zhao K., Liu D., et al., “Feature extraction and identification of underground nuclear explosion and natural earthquake based on FMmlet transform and BP neural network,” Advances in Neural Networks-ISNN 2004. Springer Berlin Heidelberg, 2004: 925–930.
14.
Meng Q.J., Sun N., “A comparison study on Gabor, Chirplet, FMm let atom databases for ECG signal processing,” 3rd IEEE International Conference on Bioinformatics and Biomedical Engineering, 2009: 1–3.
15.
Mallat S.G., Zhang Z., “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415.
16.
Donoho D.L., Tsaig Y., Drori I., et al., “Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit,” IEEE Transactions on Information Theory, 2012, 58(2): 1094–1121.
17.
Needell, D., Vershynin, R.: “Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit,” Found. Comput. Math., 2009, 9(3):317–334.
18.
Chen, S.S., Donoho, D.L., Saunders, M.A., “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput., 1998, 20(1), 33–61.
19.
Donoho D.L., Huo X., “Uncertainty principles and ideal atomic decomposition,” IEEE Transactions on Information Theory, 2001, 47(7): 2845–2862.
20.
Feng K., Jiang Z., He W., et al., “Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet,” Measurement, 2011, 44(9): 1582–1591.
21.
Zheng H., Li Z., Chen X., “Gear fault diagnosis based on continuous wavelet transform,” Mechanical Systems and Signal Processing, 2002, 16(2): 447–457.
22.
Droitcour A., Boric-Lubecke O., Lubecke V., et al., “Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring,” IEEE Transactions on Microwave Theory and Techniques, 2004, 52(3):838–848,
23.
Strang G., Nguyen T., Wavelets and Filter Banks, SIAM, 1996.
24.
Daubechies I., Defrise M., De Mol C., “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun Pur Appl Math, 2003, 57: 1413–1457.
25.
Beck A., Teboulle M., “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, 2009, 2(1): 183–202.
26.
Afonso M.V., Bioucas-Dias J.M., Figureueiredo M.A.T., “Fast image recovery using variable splitting and constrained optimization,” IEEE Transactions on Image Processing, 2010, 19(9): 2345–2356.
27.
Hunter D.R., Lange K., “A tutorial on MM algorithms,” The American Statistician, 2004, 58(1): 30–37.
Metadaten
Titel
Sparse Representation of the Transients in Mechanical Signals
verfasst von
Zhongkui Zhu
Wei Fan
Gaigai Cai
Weiguo Huang
Juanjuan Shi
Copyright-Jahr
2017
Buch
Structural Health Monitoring

Print ISBN: 978-3-319-56125-7

Electronic ISBN: 978-3-319-56126-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-56126-4_9

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