Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System

Nisa'ul Hafidhoh; Ardian Prima Atmaja; Gus Nanang Syaifuddiin; Ikhwan Baidlowi Sumafta; Salva Mahardhika Pratama; Hafsah Nur Khasanah

doi:10.14710/jmasif.15.1.63641

DOI: https://doi.org/10.14710/jmasif.15.1.63641

Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System

Nisa'ul Hafidhoh

, Ardian Prima Atmaja, Gus Nanang Syaifuddiin, Ikhwan Baidlowi Sumafta, Salva Mahardhika Pratama, Hafsah Nur Khasanah

Politeknik Negeri Madiun, Indonesia

Received: 8 May 2024; Revised: 22 May 2024; Accepted: 25 May 2024; Available online: 31 May 2024; Published: 31 May 2024.

Editor(s): Salman Alfarisi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

BibTex Citation Data :

@article{JMASIF63641,
    author = {Nisa'ul Hafidhoh and Ardian Prima Atmaja and Gus Nanang Syaifuddiin and Ikhwan Baidlowi Sumafta and Salva Mahardhika Pratama and Hafsah Nur Khasanah},
    title = {Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System},
    journal = {Jurnal Masyarakat Informatika},
  volume = {15},
    number = {1},
    year = {2024},
    keywords = {CRISP-DM, exploratory data analysis, kegagalan mesin, machine learning, predictive maintenance system},
    abstract = {  Dalam menghadapi Revolusi Industri 4.0, teknologi seperti Internet of Things, Big Data, dan Kecerdasan Buatan menjadi kunci dalam modernisasi industri. Pendekatan Machine Learning digunakan untuk memproses data multivariabel berdimensi tinggi dan mengekstrak hubungan tersembunyi dalam lingkungan industri yang kompleks. Machine Learning digunakan untuk mengklasifikasikan kegagalan mesin dalam membangun Predictive Maintenance System. Penelitian ini mengadopsi siklus CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari tahap business understanding, data understanding, data preparation, modelling, evaluation dan deployment. Predictive Maintenance Dataset berupa data sintetis yang digunakan dalam penelitian ini mencerminkan situasi industri nyata terdiri dari 10.000 baris data dengan sepuluh fitur. Jenis kegagalan mesin diklasifikasikan menjadi Heat Dissipation Failure, Power Failure, Overstrain Failure, dan Tool Wear Failure. Exploratory Data Analysis dilakukan untuk mendapatkan ringkasan dan visualisasi data. Pendekatan machine learning menggunakan metode Logistic Regression dan hasil evaluasi model mencapai akurasi 96,87%, sesuai dengan kriteria sukses data. Hasil pemodelan machine learning yang dikembangkan kemudian diimplementasikan dalam aplikasi Predictive Maintenance System berbasis web untuk memudahkan pemantauan kondisi mesin dan prediksi kegagalan mesin oleh pengguna.                                            },
   issn = {2777-0648},   pages = {56--66}  doi = {10.14710/jmasif.15.1.63641},
    url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/63641}
}

Citation Format:

Abstract

Dalam menghadapi Revolusi Industri 4.0, teknologi seperti Internet of Things, Big Data, dan Kecerdasan Buatan menjadi kunci dalam modernisasi industri. Pendekatan Machine Learning digunakan untuk memproses data multivariabel berdimensi tinggi dan mengekstrak hubungan tersembunyi dalam lingkungan industri yang kompleks. Machine Learning digunakan untuk mengklasifikasikan kegagalan mesin dalam membangun Predictive Maintenance System. Penelitian ini mengadopsi siklus CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari tahap business understanding, data understanding, data preparation, modelling, evaluation dan deployment. Predictive Maintenance Dataset berupa data sintetis yang digunakan dalam penelitian ini mencerminkan situasi industri nyata terdiri dari 10.000 baris data dengan sepuluh fitur. Jenis kegagalan mesin diklasifikasikan menjadi Heat Dissipation Failure, Power Failure, Overstrain Failure, dan Tool Wear Failure. Exploratory Data Analysis dilakukan untuk mendapatkan ringkasan dan visualisasi data. Pendekatan machine learning menggunakan metode Logistic Regression dan hasil evaluasi model mencapai akurasi 96,87%, sesuai dengan kriteria sukses data. Hasil pemodelan machine learning yang dikembangkan kemudian diimplementasikan dalam aplikasi Predictive Maintenance System berbasis web untuk memudahkan pemantauan kondisi mesin dan prediksi kegagalan mesin oleh pengguna.

Fulltext View|Download

Keywords: CRISP-DM, exploratory data analysis, kegagalan mesin, machine learning, predictive maintenance system

Article Metrics:

Article Info

Section: Research Article

Language : ID

In Vol 15, No 1 (2024): Jurnal Masyarakat Informatika

Recent articles

Implementasi Profile Matching untuk Mengukur Kualitas Website Sistem Informasi Desa Sidokerto Menggunakan Model McCall Analisis Pengaruh Kualitas Website Shopee Terhadap Kepuasan Pengguna Berdasarkan Metode WebQual 4.0 (Studi Kasus : Mahasiswa Pengguna Shopee di Universitas Diponegoro) Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System More recent articles

Most cited articles

Studi Komparasi Ekstraksi Fitur pada Pengenalan Wajah Menggunakan Principal Component Analysis (PCA) dan Wavelet Daubechies ALGORITMA BACK PROPAGATION NEURAL NETWORK UNTUK PENGENALAN POLA KARAKTER HURUF JAWA KLASIFIKASI UCAPAN KATA DENGAN SUPPORT VECTOR MACHINE Pengenalan Jenis Golongan Darah Menggunakan Jaringan Syaraf Tiruan Perceptron PENYELESAIAN MASALAH JOB SHOP MENGGUNAKAN ALGORITMA GENETIKA More cited articles

B. Prasetyo and U. Trisyanti, "Revolusi Industri 4.0 dan Tantangan Perubahan Sosial", IPTEK: Journal of Proceedings Series, no. 5, pp. 22–28, 2018
D. Lase, "Pendidikan di Era Revolusi Industri 4.0", SUNDERMANN: Jurnal Ilmiah Teologi, Pendidikan, Sains, Humaniora Dan Kebudayaan, vol. 12, no.2, pp. 28-43, 2019
Y. Puspita, Y, Fitriani, S. Astuti, and Sri Novianti, “Selamat Tinggal Revolusi Industri 4.0, Selamat Datang Revolusi Industri 5.0”, in Prosiding Seminar Nasional Pendidikan Program Pascasarjana Universitas PGRI Palembang 2020, p. 122-130
Y. Ran, X. Zhou, P. Lin, Y. Wen, R. Deng, “Survey of Predictive Maintenance: Systems, Purposes and Approaches”, IEEE Communications Surveys and Tutorials 2019, arXiv 2019, arXiv:1912.07383
M. Bevilacqua and M. Braglia, “The analytic hierarchy process applied to maintenance strategy selection”, Reliability Engineering & System Safety, vol. 70, no. 1, pp. 71–83, 2020
A. Vemal, “Development of Apps for Predictive Maintenance System: a Case Study in HP”, PhD Thesis, Universiti Sains Malaysia, Malaysia, 2018
S. Safavi, M.A. Safavi, H. Hamid, and S. Fallah, “Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles”. Sensors. vol. 21, no. 7, p. 2547, 2021
Y.-C. Chiu, F.-T. Cheng, and H.-C. Huang, “Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0,” Journal of the Chinese Institute of Engineers, vol. 40, no. 7, pp. 562–571, Oct. 2017, doi: 10.1080/02533839.2017.1362357
S. Ayvaz and K. Alpay, “Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time,” Expert Systems with Applications, vol. 173, p. 114598, Jul. 2021, doi: 10.1016/j.eswa.2021.114598
A. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliability Engineering & System Safety, vol. 215, p. 107864, Nov. 2021, doi: 10.1016/j.ress.2021.107864
T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. D. P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance,” Computers & Industrial Engineering, vol. 137, p. 106024, Nov. 2019, doi: 10.1016/j.cie.2019.106024
J. A. Solano, D. C. L. Cuesta, S. F. U. Ibanez, and J. R. C. Hernandez, “Predictive Models Assesment based on CRISP-DM Methodology for Students Performance in Colombia – Saber 11 Test”, in Procedia Computer Science, 198, 2022, pp. 512-517
M. Hosseini, N. Abdolvand, dan S. R. Harandi, "Two-dimensional analysis of customer behavior in traditional and electronic banking", Digital Business, Volume 2, Issue 2, 2022
C. Schröer, F. Kruse, dan J. M. Gómez, "A Systematic Literature Review on Applying CRISP-DM Process Model", Procedia Computer Science, Volume 181, 2021, pp. 526-534
“AI4I 2020 Predictive Maintenance Dataset”, 2020, UCI Machine Learning Repository, https://doi.org/10.24432/C5HS5C
W. Y. Ayele, "Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset", International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, 2020
J. Brzozowska, J. Pizoń, G. Baytikenova, G. O. L. A. Arkadiusz, A. Zakimova, dan K. Piotrowska, “Data Engineering In Crisp-Dm Process Production Data–Case Study”, Applied Computer Science, vol. 19, no.3, p. 83-95, 2023
Z. Yang dan D. Li, “Application of Logistic Regression with Filter in Data Classification”, in Chinese Control Conference (CCC), 3755–3759
X. Hui, Comparison and Application of Logistic Regression and Support Vector Machine in Tax Forecasting, Proceedings - 2020 International Signal Processing, Communications and Engineering Management Conference, ISPCEM, p. 48–52, 2020
K. Sahoo, A. K. Samal, J. Pramanik, dan S. K. Pani, “Exploratory data analysis using Python”. International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 12, 2019

Last update:

No citation recorded.

Last update: 2024-11-13 12:06:30

No citation recorded.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The authors who submit the manuscript must understand that the article's copyright belongs to the author(s) if accepted for publication. However, the author(s) grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors should also understand that their article (and any additional files, including data sets, and analysis/computation data) will become publicly available once published under that license. See our copyright policy. By submitting the manuscript to Jmasif, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:

their article is original, written by the mentioned author(s),
has never been published before,
does not contain statements that violate the law, and
does not violate the rights of others, is subject to copyright held exclusively by the author(s), is free from the rights of third parties, and the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

Copyright and other proprietary rights related to the article, such as patents,
The right to use the substance of the article in its own future works, including lectures and books,
The right to reproduce the article for its own purposes,
The right to archive all versions of the article in any repository, and
The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (Jurnal Masyarakat Informatika).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. Jmasif will not be held responsible for anything arising because of the writer's internal dispute. Jmasif will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. Jmasif allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and Jmasif to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.