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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2018

06.02.2018

Study on Prediction Methods for the Fault State of Rotating Machinery Based on Dynamic Grey Model and Metabolism Grey Model

verfasst von: Mingjiang Shi, Lu Jiang, Yuanhua Fu

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

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Abstract

Rotors and bearings are the key parts of rotating machinery. Mechanical faults will occur easily when rotors and bearings are running for a long time in the condition of high speed and full load. In this paper, first the dynamic grey model and metabolism grey model (MGM) are respectively used to predict the trend of the vibration amplitude of rotors and bearings, and the prediction results are compared. Then based on the root mean square value of the vibration amplitude of rotors and bearings, a back propagation network prediction model of fault feature information is established, which can predict the fault of rotors and bearings in advance. Experiments show that the dynamic grey model can predict both the rising and comprehensive growth trends of the vibration signal amplitude of rotors and bearings. However, the prediction error will increase with an increase of vibration amplitude. Experiments also indicate that the accuracy of prediction based on the MGM is higher than that of dynamic grey model.
Vorheriger Artikel Distributed Intelligent Pension System Based on BP Neural Network
Nächster Artikel Stochastic Rule Control Algorithm Based Enlistment of Induction Motor Parameters Monitoring in IoT Applications

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Literatur
1.
Fei, C. W., Bai, G. Z., & Li, X. Y. (2012). Method of rotor vibration fault diagnosis from process power spectrum entropy and SVM. Journal of Propulsion Technology, 33(2), 293–298.
2.
Chen, H. W. (2009). Vibration characteristics of rotating machines and diagnostic method. Noise and Vibration Control, 2(1), 134–137.
3.
Zhang, W. B., Zhou, X. J., Lin, Y., et al. (2009). Harmonic wavelet package method used to extract fault signal of a rotation machinery. Journal of Vibration and Shock, 28(3), 87–89.
4.
Wang, S. T., Zhang, J. M., Li, Y. Y., & ZHANG, X. Q. (2012). Rotating machinery fault diagnosis based on mathematical morphology and fuzzy clustering. Chinese Journal of Scientific Instrument, 33(5), 1055–1061.
5.
Chen, F. F., Tang, B. P., & Dong, S. J. (2011). Rotating machinery fault diagnosis based on LS-WSVM with particle swarm optimization. Chinese Journal of Scientific Instrument, 32(12), 2748–2753.
6.
Liu, X. X., Cui, X. H., Wang, J. Z., & Zhao, X. S. (2010). Design of for fault diagnosis system machine tool gearbox based on virtual instrument. Journal of Electronic Measurement and Instrument, 24(5), 481–485.CrossRef
7.
Zang, Y. P., Zhang, D. J., & Wang, W. Z. (2009). Per-level threshold de-noising method using wavelet and its application in engine vibration analysis. Journal of Vibration and Shock, 28(8), 57–60.
8.
Yuan, L., He, Y., Huang, J., et al. (2010). A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. Instrumentation and Measurement, IEEE Transactions on, 59(3), 586–595.CrossRef
9.
Qiang, M. H., Zhu, M., & Chen, L. (2010). Information entropy for fault diagnosis of inertia navigation. Journal of System Simulation, 22(1), 216–219.
10.
Zhao, Y. J., Hu, Y. H., & Liu, J. J. (2017). Random triggering-based sub-Nyquist sampling system for sparse multiband signal. IEEE Transactions on Instrumentation and Measurement, 66(7), 1789–1797.CrossRef
11.
Alcaraz, R., & Rieta, J. J. (2010). A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms. Biomedical Signal Processing and Control, 5(1), 1–14.CrossRef
12.
Zhao, Y. J., Wang, L., Wang, H. J., et al. (2015). Minimum rate sampling and spectrum blind reconstruction in random equivalent sampling. Circuits Systems and Signal Processing, 34(8), 2667–2680.CrossRef
13.
Men, Z., & Liang, Z. (2013). Fault diagnosis method for single channel rotating machinery based on EMMD and BSS. Chinese Journal of Scientific Instrument, 34(3), 636–641.
14.
Zhao, Z. H., & Yang, S. P. (2011). Fault diagnosis of roller bearing based on relative wavelet energy. Journal of Electronic Measurement and Instrument, 25(1), 44–49.CrossRef
Metadaten
Titel
Study on Prediction Methods for the Fault State of Rotating Machinery Based on Dynamic Grey Model and Metabolism Grey Model
verfasst von
Mingjiang Shi
Lu Jiang
Yuanhua Fu
Publikationsdatum
06.02.2018
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5395-0

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