DocumentCode
518605
Title
Notice of Retraction
Based on Spectral Information Fusion Soft Senser Modeling of Wet Ball Mill Load
Author
Zhao Li-Jie ; Tang Jian ; Yue Heng ; Chai Tian-you
Author_Institution
Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China
Volume
2
fYear
2010
fDate
27-29 March 2010
Firstpage
436
Lastpage
440
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Ball mill load measurement is essential to control and operational optimization for the wet grinding process, which affects the production efficiency and energy consumption. A frequency spectral information fusion soft sensor model is proposed to estimation the mill load in the paper. PCA is used to extract the feature of frequency spectrum to deal with many, noise and collinear variables. FFT is used to estimate the power spectral density(PSD)of the vibration and acoustic signal. PLS were combined with PCA scores inputs to develop mill load. Principal component numbers are selected by an optimize model. A case study shows that the proposed frequency spectral information fusion soft sensor model is effective, and produces better predictive performance than single sensor model.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Ball mill load measurement is essential to control and operational optimization for the wet grinding process, which affects the production efficiency and energy consumption. A frequency spectral information fusion soft sensor model is proposed to estimation the mill load in the paper. PCA is used to extract the feature of frequency spectrum to deal with many, noise and collinear variables. FFT is used to estimate the power spectral density(PSD)of the vibration and acoustic signal. PLS were combined with PCA scores inputs to develop mill load. Principal component numbers are selected by an optimize model. A case study shows that the proposed frequency spectral information fusion soft sensor model is effective, and produces better predictive performance than single sensor model.
Keywords
acoustic signal processing; ball milling; fast Fourier transforms; feature extraction; grinding; grinding machines; principal component analysis; sensor fusion; vibrations; FFT; PCA; acoustic signals; ball mill load measurement; energy consumption; feature extraction; operational optimization; power spectral density; spectral information fusion soft sensor modeling; vibration signals; wet grinding process; Acoustic sensors; Ball milling; Energy consumption; Energy measurement; Frequency estimation; Milling machines; Predictive models; Principal component analysis; Production; Sensor fusion; Ball mill load; PCA; PLS; Soft sensor; frequency spectrum;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5486641
Filename
5486641
Link To Document