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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Advanced Signal Processing for Structural Health Monitoring

verfasst von : Ruqiang Yan, Xuefeng Chen, Subhas C. Mukhopadhyay

Erschienen in: Structural Health Monitoring

Verlag: Springer International Publishing

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Abstract

This chapter starts with an introduction on structural health monitoring (SHM) and emphasizes its importance for engineering systems. Then four different stages, i.e., operational evaluation, data acquisition, feature extraction and diagnosis and prognosis, involved in SHM are briefly discussed, followed by review of each signal processing technique used in SHM, which will be described in the book.

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Nächstes Kapitel Signal Post-processing for Accurate Evaluation of the Natural Frequencies
Literatur
1.
Farrar C.R., Worden K., “An introduction to structural health monitoring,” Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 2007, 365 (1851): 303–315.
2.
Fugate M.L., Sohn H., Farrar C.R., “Vibration-based damage detection using statistical process control,” Mechanical Systems and Signal Processing, 2001, 15 (4): 707–721.
3.
Balageas D.L., Fritzen C., Guemes A., Structural Health Monitoring, John Wiley & Sons, Inc., 2006.
4.
Volponi A.J., “Gas turbine engine health management: past, present, and future trends,” Journal of Engineering for Gas Turbines and Power, 2014, 136 (5): 051201.
5.
Samuel P.D., Pines D.J., “A review of vibration-based techniques for helicopter transmission diagnostics,” Journal of Sound and Vibration, 2005, 282 (1): 475–508.
6.
Bartelds G., “Aircraft structural health monitoring, prospects for smart solutions from a European viewpoint,” Journal of Intelligent Material Systems and Structures, 1999, 9 (11): 906–910.
7.
Farahani E.M., Hosseinzadeh N., Ektesabi M., “Comparison of fault-ride-through capability of dual and single-rotor wind turbines,” Renewable Energy, 2012, 48(6): 473–481.
8.
9.
Caselitz P., Giebhardt J., Mevenkamp M., “On-line fault detection and prediction in wind energy converters,” European Wind Energy Association Conference and Exhibition, 1994, pp. 623–627.
10.
Goyal D., Pabla B.S., “The vibration monitoring methods and signal processing techniques for structural health monitoring: a review,” Archives of Computational Methods in Engineering, 2015: 1–10.
11.
Worden K., Farrar C.R., Manson G., Park, G., “The fundamental axioms of structural health monitoring,” Proceedings of the Royal Society A, 2007, 463 (2082): 1639–1664.
12.
Farrar C.R., Worden K., Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons, Inc., 2012.
13.
Girdhar P., Scheffer C., Practical Machinery Vibration Analysis and Predictive Maintenance, Elsevier, 2004.
14.
Bishop R.E.D., Mechanical Vibrations, Allyn and Bacon, 1963.
15.
Randall R.B., “Vibration-based condition monitoring: industrial, aerospace and automotive applications,” John Wiley & Sons, Inc., 2010.
16.
Wang L., Gao R.X., Condition Monitoring and Control for Intelligent Manufacturing, Springer London, 2006.
17.
Amezquita-Sanchez J.P., Adeli H., “Signal processing techniques for vibration-based health monitoring of smart structures,” Archives of Computational Methods in Engineering, 2016, 23 (1): 1–15.
18.
Mallat S., A Wavelet Tour of Signal Processing: the Sparse Way, Academic Press, 2008.
19.
Newland D.E., Wavelet Analysis of Vibration Signals, John Wiley & Sons, Inc., 2008.
20.
Baccar D., Söffker D., “Wear detection by means of wavelet-based acoustic emission analysis,” Mechanical Systems and Signal Processing, 2015, 60: 198–207.
21.
Chen J., Li. Z, Pan J., Chen G., Zi Y., Yuan J., Chen B., He Z., “Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review,” Mechanical Systems and Signal Processing, 2016, 70: 1–35.
22.
Yan R., Gao R.X., Chen X., “Wavelets for fault diagnosis of rotary machines: a review with applications,” Signal Processing, 2014, 96: 1–15.
23.
Qian S., Chen D., “Joint time-frequency analysis,” IEEE Signal Processing Magazine, 1999, 16 (2): 52–67.
24.
Sejdić E., Djurović I., Jiang J., “Time–frequency feature representation using energy concentration: An overview of recent advances,” Digital Signal Processing, 2009, 19 (1): 153–183.
25.
Wang S., Chen X., Cai G., Chen B., Li X., He Z., “Matching demodulation transform and synchrosqueezing in time-frequency analysis,” IEEE Transactions on Signal Processing, 2014, 62 (1): 69–84.
26.
Wright J., Yang A.Y., Ganesh A., Sastry S.S., Ma Y., “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31 (2): 210–227.
27.
Elad M., Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer-Verlag, 2010.
28.
Candès E.J., Wakin M.B., “An introduction to compressive sampling,” IEEE Signal Processing Magazine, 2008, 25 (2): 21–30.
29.
Donoho D.L., “Compressed sensing,” IEEE Transactions on Information Theory, 2006, 52 (4): 1289–1306.
30.
Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N., Tung C.C., Liu H.H., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998: 903–995.
31.
Lei Y., Lin J., He Z., Zuo M., “A review on empirical mode decomposition in fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, 2013, 35 (1): 108–126.
32.
Hu N., Chen M., Wen X., “The application of stochastic resonance theory for early detecting rub-impact fault of rotor system,” Mechanical Systems and Signal Processing, 2003, 17 (4): 883–895.
33.
Benzi R., Sutera A., Vulpiani A., “The mechanism of stochastic resonance,” Journal of Physics A: Mathematical and General, 1981, 14 (11): L453.
34.
Abellan-Nebot J.V., Subirón F.R., “A review of machining monitoring systems based on artificial intelligence process models,” International Journal of Advanced Manufacturing Technology, 2010, 47 (1–4): 237–257.
35.
Zhang G., Patuwo B.E., Hu M.Y., “Forecasting with artificial neural networks: The state of the art,” International Journal of Forecasting, 1998, 14 (1): 35–62.
36.
Wu Y., Zhang B., Lu J., Du K.L., “Fuzzy logic and neuro-fuzzy systems: a systematic introduction,” Journal of Materials Processing Technology, 2011, 129 (s 1–3): 148–151.
37.
Wang WQ, Golnaraghi MF, Ismail F. Prognosis of machine health condition using neuro-fuzzy systems[J]. Mechanical Systems & Signal Processing, 2004, 18(4): 813–831.
38.
Widodo A., Yang B.S., “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, 2007, 21 (6): 2560–2574.
39.
Rabiner, L.R., “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, 1989, 77 (2): 257–286.
40.
Iamsumang C., Mosleh A., Modarres M., “Computational algorithm for dynamic hybrid Bayesian network in on-line system health management applications,” 2014 International Conference on Prognostics and Health Management, 2014, pp. 1–8.
41.
Li D., “A tutorial survey of architectures, algorithms, and applications for deep learning,” APSIPA Transactions on Signal and Information Processing, 2014, 3 e2:1–29.
42.
Jia F., Lei Y., Lin J.,, Zhou X., Lu N., “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data,” Mechanical Systems and Signal Processing, 2015, 72–73: 303–315.
Metadaten
Titel
Advanced Signal Processing for Structural Health Monitoring
verfasst von
Ruqiang Yan
Xuefeng Chen
Subhas C. Mukhopadhyay
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_1

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