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Open AccessArticle

State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance

by Chaojun Wang and Fei He *
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(10), 1019; https://doi.org/10.3390/e21101019
Received: 12 August 2019 / Revised: 2 October 2019 / Accepted: 9 October 2019 / Published: 21 October 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
In the hot strip rolling process, many process parameters are related to the quality of the final products. Sometimes, the process parameters corresponding to different steel grades are close to, or even overlap, each other. In reality, locating overlap regions and detecting products with abnormal quality are crucial, yet challenging. To address this challenge, in this work, a novel method named kernel entropy component analysis (KECA)-weighted cosine distance is introduced for fault detection and overlap region locating. First, KECA is used to cluster the training samples of multiple steel grades, and the samples with incorrect classes are seen as the boundary of the sample distribution. Next, the concepts of recursive-based regional center and weighted cosine distance are introduced. For each steel grade, the regional center and the weight coefficients are determined. Finally, the weighted cosine distance between the testing sample and the regional center is chosen as the index to judge abnormal batches. The samples in the overlap region of multiple steel grades need to be focused on in the real production process, which is conducive to quality grade and combined production. The weighted cosine distances between the testing sample and different regional centers are used to locate the overlap region. A dataset from a hot steel rolling process is used to evaluate the performance of the proposed methods. View Full-Text
Keywords: hot strip rolling; kernel entropy component analysis; weighted cosine distance; recursive-based regional center hot strip rolling; kernel entropy component analysis; weighted cosine distance; recursive-based regional center
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Wang, C.; He, F. State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance. Entropy 2019, 21, 1019.

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