HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES
Mouna TARIK
tarik.mouna@gmail.comFaculty 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 MachineReferences
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Authors
Mouna TARIKtarik.mouna@gmail.com
Faculty of science and techniques Morocco
https://orcid.org/0009-0008-1603-0067
Authors
Ayoub MNIAILMA, FSTT, Abdelmalek Essaadi University, Tetouan Morocco
https://orcid.org/0009-0009-9189-3257
Authors
Khalid JEBARILMA, FSTT, Abdelmalek Essaadi University, Tetouan Morocco
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