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Machine Predictive Maintenance by Using Support Vector Machines
Open accessAbstract
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%.
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“Meanwhile, PdM leverages data through the prognostics process, enabling proactive predictions of future health based on current and historical data (Roehrich & Raffaele, 2023;Assagaf, Sukandi, Abdillah, Arifin and Ga, 2023).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: State of the art and challenges (2025) · cites this paper as Assagaf, Sukandi, Abdillah, Arifin and Ga, 2023
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Research works printed but not linked: 15
- Recent in Engineering Science and Technology 2023, 1, 2. https://doi.org/00.0000/xxxxx www.mbi-journals.com/index.php/riestech References
- M. Paolanti, L. Romeo, A. Felicetti, A. Mancini, E. Frontoni, and J. Loncarski, "Machine Learning approach for Predictive Maintenance in Industry 4.0," 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018, Aug. 2018, doi: 10.1109/MESA.2018.8449150. · DOI · Paper page
- H. Rahman, A. A. Abdillah, A. Apriana, D. Handaya, and I. Assagaf, "Indoor CO2 Level-Based Occupancy Estimation at Low-Scale Occupant using Statistical Learning Method," Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021, pp. 496-499, 2021, doi: 10.1109/IC2IE53219.2021.9649072. · DOI
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- M. D. Dangut, Z. Skaf, and I. K. Jennions, "Handling imbalanced data for aircraft predictive maintenance using the BACHE algorithm," Appl Soft Comput, p. 108924, May 2022, doi: 10.1016/J.ASOC.2022.108924. · DOI
- J. Antunes Rodrigues, J. Torres Farinha, M. Mendes, R. Mateus, and A. Marques Cardoso, "Short and long forecast to implement predictive maintenance in a pulp industry," Eksploatacja i Niezawodność, vol. Vol. 24, no. 1, pp. 33-41, 2022, doi: 10.17531/EIN.2022.1.5. · DOI
- D. R. S, B. Setyono, and T. Herdha, "Bullet Image Classification using Support Vector Machine," in Journal of Physics: Conference Series, 2016.
- G. Birare and V. A. Chakkarwar, "Automated Detection of Brain Tumor Cells Using Support Vector Machine," 2018 9th International Conference on Computing, Communication and Net- working Technologies, ICCCNT 2018, Oct. 2018, doi: 10.1109/ICCCNT.2018.8494133. · DOI
- S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, and W. Xu, "Applications of support vector machine (SVM) learning in cancer genomics," Cancer Genomics Proteomics, vol. 15, no. 1, pp. 41-51, 2018.
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- A.B.; Author 2, C.D. Title of the article. Abbreviated Journal Name Year, Volume, page range.
Cited by
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Thai, T.-T. N., Do, P., Iung, B., & Gardoni, P. (2025) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2025.v16i4.4306 · Cited by 6
Metadata
- Authors
- Assagaf, I., Sukandi, A., Abdillah, A. A., Arifin, S., & Ga, J. L.
- Affiliations
- Jakarta State Polytechnic (ID)
- Binus University (ID)
- University of Birmingham (GB)
- Venue
- Recent in Engineering Science and Technology
- Publisher
- PT Mencerdaskan Bangsa Indonesia
- Published
- 2023-01-01
- Volume
- 1
- Issue
- 01
- Pages
- 31-35
- ISSN
- 2985-704X (ISSN-L)
- 2985-8321
- OpenAlex
- W4381295425
- Cited by
- 26
- References
- 0
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 2 · 2023: 3
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-sa
- Locations
- Recent in Engineering Science and Technology (publishedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- predictive maintenance, support vector machine, machine failure detection, sensor data, hyperparameter optimization, classification
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