Modeling Difficult-to-Measure Process Parameters Based on Intrinsic Mode Functions Frequency Spectral Features of Mechanical Vibration and Acoustical Signals

Abstract:

Heavy mechanical devices of complex industrial process produce soundly mechanical vibration and acoustical signals. Some difficult-to-measure key process parameters have direct relationship with these signals. A newly ensemble empirical mode decomposition (EEMD), Fast Fourier Transform (FFT), Mutual information (MI), and Kernel partial least squares (KPLS) based modeling approach is proposed to measure these process parameters. At first, different scale intrinsic mode functions (IMFs) of mechanical vibration and acoustical signals are obtained using EEMD technology. Then, FFT transforms these multi-scale IMFs into frequency domain, and MI based feature selection method selects interesting frequency spectral features. Finally, KPLS constructs the final soft sensor models using the selected features. Experimental results based on vibration and acoustical signals of ball mill demonstrate this approach is more effective than other exist multi-scale decomposition based methods.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 3671-3674
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.3671
• Online since: July 2014
• Authors: Jian Tang*, Zhuo Liu, Yong Jian Wu, Li Jie Zhao
• Keywords: Ensemble Empirical Mode Decomposition (EEMD), Frequency Spectral Features, Intrinsic Mode Function (IMF), Kernel Partial Least Squares (KPLS), Mutual Information (MI)

References

[1] T.Y. Chai: Acta Automatica Sinica, Vol. 39 (2013), p.1744.

[2] J. Tang, L.J. Zhao, J.W. Zhou and T.Y. Chai: Minerals Engineering, Vol. 23 (2010), p.720.

[3] J. Tang, T.Y. Chai, Y. Wen and L.J. Zhao: Control Engineer Practice, Vol. 20 (2012), p.991.

[4] J. Tang, T.Y. Chai, W. Yu and L.J. Zhao: IEEE Transactions on Automation Science and Engineering, Vol. 10 (2013), p.726.

[5] N.E. Huang, Z. Shen and S.R. Long: Proc. Royal Soc, Vol. 454 (1998), p.903.

[6] J. Tang, L.J. Zhao, H. Yue, W. Yu and T.Y. Chai: Procedia Engineering, Vol. 16 (2011), p.646.

[7] L.J. Zhao, J. Tang and W.R. Zheng: Journal of Theoretical and Applied Information Technology, Vol. 45 (2012), p.179.

[8] J. Tang, L.J. Zhao, J. Jia, T.Y. Chai and W. Yu: Lecture Notes in Computer Science, Vol. 7367 (2012), p.489.

[9] Y.G. Lei, J. Lin, Z.J. He and M.J. Zuo: Mechanical System and Signal Processing, Vol. 35 (2013), p.108.

[10] H.W. Liu, J.G. Sun, L. Liu and H.J. Zhang: Vol. 42 (2009), p.1330.

[11] R.C. Cai, Z.F. Hao, X.W. Yang and W. Wen: Neurocomputing, Vol. 72 (2009), p.991.