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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© 2012 Springer-Verlag Berlin Heidelberg
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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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