Feature selection of frequency spectrum for modeling difficulty to measure process parameters

  • Authors:

  • Jian Tang;Li-Jie Zhao;Yi-miao Li;Tian-you Chai;S. Joe Qin

  • Affiliations:

  • Unit 92941, PLA, Huludao, China,Research Center of Automation, Northeastern University, Shenyang, China;College of Information Engineering, Shenyang University, of Chemical Technology, Shenyang, China,Research Center of Automation, Northeastern University, Shenyang, China;Control Engineering of China, Northeastern University, Shenyang, China;Research Center of Automation, Northeastern University, Shenyang, China;Work Family Department of Chemical Engineering and Materials Science, Ming Hsieh, Department of Electrical Engineering, University of Southern California, Los Angeles

  • Venue:
  • ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2012

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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.