Machine failure prediction using joint reserve intelligence with feature selection technique
Author:
Affiliation:
1. University of Bahrain, Sakhir, Bahrain
2. Mutah Universityät, Karak, Jordan
3. University of East London, London, UK
Publisher
Informa UK Limited
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Hardware and Architecture,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/1206212X.2023.2260619
Reference26 articles.
1. Matzka S. Explainable artificial intelligence for predictive maintenance applications. In: 2020 Third International Conference on Artificial Intelligence for Industries (AI4I). IEEE; 2020. p. 69–74. Irvine CA: USA
2. Molnar C. Interpretable machine learning. Lulu.com; 2020.
3. XAI—Explainable artificial intelligence
4. Remaining useful life estimation based on nonlinear feature reduction and support vector regression
5. Matzka S. Using process quality prediction to increase resource efficiency in manufacturing processes. In: 2018 First International Conference on Artificial Intelligence for Industries (AI4I). IEEE; 2018. p. 110–111. Laguna Hills California USA
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1. Impacts of Feature Selection on Predicting Machine Failures by Machine Learning Algorithms;Applied Sciences;2024-04-15
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