Abstract
Some difficulty to measure process parameters can be obtained using the vibration and acoustical frequency spectra. The dimension of the frequency spectrum is very large. This poses a difficulty in selecting effective frequency band for modeling. In this paper, the partial least squares (PLS) algorithm is used to analyze the sensitivity of the frequency spectrum to these parameters. A sphere criterion is used to select different frequency bands from vibration and acoustical spectrum. The soft sensor model is constructed using the selected vibration and acoustical frequency band. The results show that the proposed approach has higher accuracy and better predictive performance than existing approaches.
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References
Zhou, P., Chai, T.Y., Wang, H.: Intelligent Optimal-Setting Control for Grinding Circuits of Mineral Processing. IEEE Transactions on Automation Science and Engineering 6, 730–743 (2009)
Zhou, P., Chai, T.Y.: Intelligent Monitoring and Control of Mill Load for Grinding Processes. Chinese Control Theory & Applications 25, 1095–1099 (2008) (Chinese)
Bai, R., Chai, T.Y.: Optimization Control of Ball Mill Load in Blending Process with Data Fusion and Case-based Reasoning. Journal of Chemical Industry and Engineering 60, 1746–1751 (2009) (Chinese)
Zeng, Y., Forssberg, E.: Monitoring Grinding Parameters by Vibration Signal Measurement-a Primary Application. Minerals Engineering 7, 495–501 (1994)
Fukunaga, K.: Effects of Sample Size in Classifier Design. IEEE Transaction on Pattern Analysis and Machine Intelligence 11, 873–885 (1989)
Qin, S.J.: Recursive PLS Algorithms for Adaptive Data Modeling. Computers & Chemical Engineering 22, 503–514 (1998)
Leardi, R., Seasholtz, M.B., Pell, R.J.: Variable Selection for Multivariate Calibration Using a Genetic Algorithm: Prediction of Additive Concentrations in Polymer Films from Fourier Transform-infrared Spectral Data. Analytica Chimica Acta 461, 189–200 (2002)
Tang, J., Zhao, L.J., Zhou, J.W., Yue, H., Chai, T.Y.: Experimental Analysis of Wet Mill Load based on Vibration Signals of Laboratory-scale Ball Mill Shell. Minerals Engineering 23, 720–730 (2010)
Yue, H.H., Qin, S.J., Markle, R.J., Nauert, C., Gatto, M.: Fault Detection of Plasma Ethchers Using Optical Emission Spectra. IEEE Transaction on Semiconductor Manufacturing 11, 374–385 (2000)
Dayal, B.S., MacGregor, J.F.: Improved PLS Algorithm. Journal of Chemometrics 11, 73–85 (1997)
Jackson, J.E.: A User’s Guide to Principal Compenents. Wiley-Interscience, New York (1991)
Wang, H.W.: Partial Least-Squares Regression Method and Applications. National Defence Industry Press, Beijing (1999)
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Tang, J., Zhao, LJ., Li, Ym., Chai, Ty., Qin, S.J. (2012). Feature Selection of Frequency Spectrum for Modeling Difficulty to Measure Process Parameters. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_10
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DOI: https://doi.org/10.1007/978-3-642-31362-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
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