Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

Li Jing Han; Jian Hong Yang; Min Lin; Jin Wu Xu

doi:10.4028/www.scientific.net/AMR.572.25

Paper Titles

New Six-High Mills in Cold Tandem Rolling - Control Strategies and Performance Characteristics
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Hot Rolling State Analysis Based on Kernel Principal Component Analysis and Information Entropy
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Rolling Theory Analysis and Forces Calculation of Heavy Cylinder Rolling Mill with Two Drive Rolls
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Calculation of Rolling Force of 3700mm Cylindrical Shell Rolling Mill
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Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum
p.25

Research on High Pressure Abrasive Water Jet for Cold Rolling Descaling
p.31

New Methods of Quality Control Systems on Strip Rolling Mills
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Technology Innovation of Short Stress Path Mill and Flying Shear
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Research of Smith Monitor AGC Based on Active Disturbance Rejection Control
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 572Hot Strip Tail Flick Recognition Based on...

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

Abstract:

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

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Periodical:

Advanced Materials Research (Volume 572)

Pages:

25-30

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.572.25

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Online since:

October 2012

Authors:

Li Jing Han, Jian Hong Yang, Min Lin, Jin Wu Xu

Keywords:

Mathematical Morphology, Pattern Spectrum, Support Vector Machine (SVM), Tail Flick

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