HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES

Mouna TARIK

tarik.mouna@gmail.com
Faculty of science and techniques (Morocco)
https://orcid.org/0009-0008-1603-0067

Ayoub MNIAI


LMA, FSTT, Abdelmalek Essaadi University, Tetouan (Morocco)
https://orcid.org/0009-0009-9189-3257

Khalid JEBARI


LMA, FSTT, Abdelmalek Essaadi University, Tetouan (Morocco)

Abstract

The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as oversampling and features selection for failure prediction, is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For features selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation is used in literature; it is applied to aircraft engine sensors measurements to predict engines failure, while the predicting algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.


Keywords:

Predictive Maintenance, Machine Learning, Features Selection, SMOTE-Tomek, Support Vector Machine

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Published
2023-06-30

Cited by

TARIK, M., MNIAI, A., & JEBARI, K. (2023). HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES. Applied Computer Science, 19(2), 112–124. https://doi.org/10.35784/acs-2023-18

Authors

Mouna TARIK 
tarik.mouna@gmail.com
Faculty of science and techniques Morocco
https://orcid.org/0009-0008-1603-0067

Authors

Ayoub MNIAI 

LMA, FSTT, Abdelmalek Essaadi University, Tetouan Morocco
https://orcid.org/0009-0009-9189-3257

Authors

Khalid JEBARI 

LMA, FSTT, Abdelmalek Essaadi University, Tetouan Morocco

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