Abstract
Prediction and classification of particular faults in rotating machinery, based on a given set of measurements, could significantly reduce the overall costs of maintenance and repair. Usually the vibration signal is sampled with a very high frequency due to its nature, thus it is quite difficult to do considerably long forecasting based on the methods, which are suitable for e.g. financial time series (where the sampling frequency is smaller). In this paper new forecasting and classification technique for particular vibration signal characteristics is proposed. Suggested approach allows creating a part of control system responsible for early fault detection, which could be used for preventive maintenance of industrial equipment. Presented approach can be extended to high frequency financial data for the prediction of “faults” on the market.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rilling, G., Flandrin, P., Gonëcalv‘es, P.: On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Mathematical, Physical and Engineering Sciences 454(1971), 903–995 (1998)
Magrin-Chagnolleau, I., Baraniuk, R.G.: Empirical mode decomposition based on it’s time-frequency attributes. In: Proceedings of the 69th SEG Meeting, Rice University, Houston, Texas
Barkova, N.A.: Introduction to Vibration Diagnostics of Rotating Machinery and Equipment. In: SPbSMTU (in Russian) (2003)
Barkov, A.V., Barkova, N.A., Azovcev, A.U.: Monitoring and Diagnostics of Rotor Machines by Vibration. In: SPbSMTU (in Russian) (2000)
Minin, A., Kuperin, Y., Lang, B.: Increasing the quality of neural forecasts with the help of Empirical Mode Decomposition. In: Proceedings of the International Scientific Conferences “Intelligent systems” (AIS 2007) and “Intelligent CAD’s” (CAD-2007), Physmathlit, vol. 4, pp. 68–74 (2007)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1, 4–27 (1990)
Ludd, K.: Michael Small Towards long-term predictions. Physica D 136, 31–44 (2000)
Lang, B., Mokhov, I.: Neural Clouds for monitoring of complex plant conditions (to appear)
Broyden, C.G.: Journal of the Institute of Mathematics and Its Applications 6, 76–90 (1970)
Sanza, J., Pererab, R., Huertab, C.: Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration 302, 981–999 (2007)
Hoffman, A.J., van der Merwe, N.T.: The application of neural networks to vibration diagnostics for multiple fault conditions. Computer Standards & Interfaces 24, 139–149 (2002)
Kalkat, M., Yildirim, S., Uzmay, I.: Design of artificial neural networks for rotor dynamics analysis of rotating machine systems. Mechatronics 15, 573–588 (2005)
Hagan, M.T., Menhaj, M.: Training feed forward networks with the Marquardt algorithm. IEEE Transactions on neural networks 5(6), 989–993
Howard, I.: A review of rolling element bearing vibration “Detection, diagnosis and prognosis”, DSTO Aeronautical and Maritime Research Laboratory (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mokhov, I., Minin, A. (2007). Advanced Forecasting and Classification Technique for Condition Monitoring of Rotating Machinery. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)