Paper Info
Title
State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance.
Abstract
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.
Year
DOI
Venue
2019
10.3390/e21101019
ENTROPY
Keywords
Field
DocType
hot strip rolling,kernel entropy component analysis,weighted cosine distance,recursive-based regional center
Sampling distribution,Mathematical optimization,Trigonometric functions,Fault detection and isolation,Cosine Distance,Algorithm,Scheduling (production processes),Cluster analysis,Recursion,Mathematics,Kernel entropy component analysis
Journal
Volume
Issue
Citations 
21
10
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chaojun Wang100.34
Fei He241.11