Journal of Applied Science and Engineering

Published by Tamkang University Press


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Yangqianhui Zhang1, Rui Lin2, and Liang Zhao2This email address is being protected from spambots. You need JavaScript enabled to view it.

1School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

2School of Software Technology,Dalian University of Technology, Dalian 116620, China


Received: September 26, 2023
Accepted: November 14, 2023
Publication Date: March 8, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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During the sintering process of iron ore, due to the long production line and multiple procedures, there is always a delay in control which can lead to defects on the sintering surface. To solve this problem, we propose a novel algorithm called TCUE that predicts these defects based on time compensation mechanism. The algorithm exploits various decision tree algorithms to calculate feature importance, which helps it determine the optimal time lag for each feature and compensate for any delays accordingly. Furthermore, UMAP is used for dimensionality reduction to simplify the calculation. Finally, we employ a kernel extreme learning machine for classification. Our experimental results have shown that TCUE can accurately predict whether cracks will appear on the sintering surface. It outperforms other common machine learning methods, making it suitable for large-scale deployment in factories.

Keywords: Cracks Prediction; Machine Learning; Sintering; Time Lag

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