Logistic Model Tree Forest for Steel Plates Faults Prediction

Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal ev...

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Bibliographic Details
Published in:Machines (Basel) 2023-07, Vol.11 (7), p.679
Main Authors: Ghasemkhani, Bita, Yilmaz, Reyat, Birant, Derya, Kut, Recep Alp
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
artificial intelligence
classification
Classifiers
Datasets
Deep learning
Equipment costs
Fault diagnosis
Fault lines
fault prediction
Internet of Things
Investigations
logistic model tree
Machine learning
Manufacturing
Materials science
Methods
Neural networks
Regression analysis
Seismic engineering
Stainless steels
Steel plates
Support vector machines
Surface defects
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Summary:Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines11070679