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All Journal CommIT (Communication & Information Technology) Union: Jurnal Ilmiah Pendidikan Matematika Jurnal Mekanik Terapan EPIGRAM (e-journal) JURNAL POLI-TEKNOLOGI Recent in Engineering Science and Technology
Abdul Azis Abdillah
Politeknik Negeri Jakarta
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation

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Machine Predictive Maintenance by Using Support Vector Machines Idrus Assagaf; Agus Sukandi; Abdul Azis Abdillah; Samsul Arifin; Jonri Lomi Ga
Recent in Engineering Science and Technology Vol. 1 No. 1 (2023): RiESTech Vol. 1 No. 1 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.6

Abstract

Predictive Maintenance (PdM) is an adoptable worth strategy when we deal with the maintenance business, due to a necessity of minimizing stop time into a minimum and reduce expenses.  Recently, the research of PdM is now begin in utilizing the artificial intelligence by using the machine data itself and sensors. Data collected then analyzed and modelled so that the decision can be made for the near and next future. One of the popular artificial intelligences in handling such classification problem is Support Vector Machines (SVM). The purpose of the study is to detect machine failure by using the SVM model. The study is using database approach from the model of Machine Learning. The data collection comes from the sensors installed on the machine itself, so that it can predict the failure of machine function. The study also to test the performance and seek for the best parameter value for building a detection model of machine predictive maintenance The result shows based on dataset AI4I 2020 Predictive Maintenance, SVM is able to detect machine failure with the accuracy of 80%.
Co-Authors Agus Sukandi Assagaf, Idrus Azwardi Azwardi Belyamin Belyamin Bobbi Rahman Darul Nurjanah Dewi Yanti Liliana Ega Edistria Ega Edistria Erifmi Zahada Fuad Zainuri Fuad Zainuri Haidir Juna I Gusti Agung Anom Yudistira Imam Wahyudi Indrawan, Imam Wahyudi Indra Bayu Muktyas Isnanda Nuriskasari Ivana Bianca Iwan Susanto Jonri Lomi Ga Lentang B. Sibarani Memed Sumatono Muhammad Amien Ibrahim Muhammad Hidayat Tullah Muhammad Rezakusuma Muhammad Toby Adigunanugraha Nerru Pranuta Murnaka, Nerru Pranuta Nidia Sofa Puguh Wahyu Prasetyo, Puguh Rahmat Subarkah Rimalia Anggraini Rinda Nariswari Samsul Arifin Sari Puspita Dewi Sonki Prasetya Widiyatmoko, Widiyatmoko Zahran Muzakki