Analisis Metode FMEA Dan SPC Pada Proyeksi Losses Produksi Dan Prediksi Perawatan Pompa Minyak Di PT.Pertamina Ep Zona 1 Rantau Field Kuala Simpang

Bambang Sugito(1*), Muhammad Iqbal(2), Zulham Sitorus(3),

(1) Universitas Pembangunan Panca Budi, Medan, Indonesia
(2) Universitas Pembangunan Panca Budi, Medan, Indonesia
(3) Universitas Pembangunan Panca Budi, Medan, Indonesia
(*) Corresponding Author

Abstract


Leaks in oil pumping systems are among the main causes of production losses and reduced operational efficiency in the upstream oil and gas industry. This study aims to identify dominant failure modes, monitor process stability, and predict pump leakage risk using an integrated approach that combines Failure Mode and Effect Analysis (FMEA), Statistical Process Control (SPC), and the Support Vector Machine (SVM) algorithm. The research was conducted at PT Pertamina EP Zone 1 Rantau Field using operational data from the year 2024. FMEA results show that leakage due to illegal tapping and corrosion are the most critical failures, with Risk Priority Numbers (RPN) of 216 and 180, respectively. SPC analysis using X̄-R control charts revealed weekly process fluctuations, indicating potential variability in operations. To predict leakage risk, an SVM model was trained using technical pump features such as pressure, temperature, vibration, and pipe joint age. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE), and model evaluation yielded an accuracy of 95.48%. The integration of these three methods has proven effective in supporting data-driven predictive maintenance strategies. It reduces unplanned downtime, minimizes production losses, and enhances the reliability of oil pumping systems.

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References


“PT. Pertamina EP.” Accessed: May 16, 2025. [Online]. Available: https://pep.pertamina.com/Sejarah

P. F. Orrù, A. Zoccheddu, L. Sassu, C. Mattia, R. Cozza, and S. Arena, “Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry,” Sustainability 2020, Vol. 12, Page 4776, vol. 12, no. 11, p. 4776, Jun. 2020, doi: 10.3390/SU12114776.

P. Studi Teknik Lingkungan Fakultas Teknik Universitas Batanghari Jambi, A. Nurrahman, E. Permana, and A. Musdalifah, “Analisa Kehilangan Minyak (Oil Losses) Pada Proses Produksi Di Pt X,” Jurnal Daur Lingkungan, vol. 4, no. 2, pp. 59–63, Sep. 2021, doi: 10.33087/DAURLING.V4I2.89.

“Advancements in Predictive Maintenance in the Oil and Gas Industry - Energies Media.” Accessed: May 16, 2025. [Online]. Available: https://energiesmedia.com/advancements-in-predictive-maintenance-in-the-oil-and-gas-industry/

Y. Deddy Hermawan and D. Kristanto, “Oil Losses Problem in Oil and Gas Industries,” 2021. [Online]. Available: www.intechopen.com

I. Assagaf, A. Sukandi, A. A. Abdillah, S. Arifin, and J. L. Ga, “Machine Predictive Maintenance by Using Support Vector Machines,” Recent in Engineering Science and Technology, vol. 1, no. 01, pp. 31–35, Jan. 2023, doi: 10.59511/RIESTECH.V1I01.6.

J. Hasil et al., “Implementasi Metode Failure Mode Effect and Analisys (FMEA) Pada Siklus Air PLTU,” Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri, vol. 8, no. 2, pp. 110–118, Dec. 2022, doi: 10.24014/JTI.V8I2.19369.

H. C. Liu, L. Liu, and N. Liu, “Risk evaluation approaches in failure mode and effects analysis: A literature review,” Expert Syst Appl, vol. 40, no. 2, pp. 828–838, Feb. 2013, doi: 10.1016/J.ESWA.2012.08.010.

“Analisis Oil Losses Pada Stasiun Perebusan Produksi Crude Palm Oil (CPO) Menggunakan Metode Statistical Process Control (SPC) | Jurnal Teknologi dan Manajemen Industri Terapan.” Accessed: May 16, 2025. [Online]. Available: https://jurnal-tmit.com/index.php/home/article/view/67

I. Madanhire and C. Mbohwa, “Application of Statistical Process Control (SPC) in Manufacturing Industry in a Developing Country,” Procedia CIRP, vol. 40, pp. 580–583, Jan. 2016, doi: 10.1016/J.PROCIR.2016.01.137.

Z. Sitorus, E. Hariyanto, and F. Kurniawan, “Analysis of Artificial Intelligence Machine Learning Technology for Mapping and Predicting Flood Locations in Pahlawan Batu Bara Village,” International Journal Of Computer Sciences and Mathematics Engineering, vol. 2, no. 2, pp. 281–288, Nov. 2023, doi: 10.61306/IJECOM.V2I2.54.

M. Sunjaya, Z. Sitorus, Khairul, M. Iqbal, and A. P. U. Siahaan, “Analysis of machine learning approaches to determine online shopping ratings using naïve bayes and svm,” International Journal Of Computer Sciences and Mathematics Engineering, vol. 3, no. 1, pp. 7–16, May 2024, doi: 10.61306/IJECOM.V3I1.60.

G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing, “Learning from class-imbalanced data: Review of methods and applications,” Expert Syst Appl, vol. 73, pp. 220–239, May 2017, doi: 10.1016/J.ESWA.2016.12.035.

R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, “Deep Learning and Its Applications to Machine Health Monitoring: A Survey”.




DOI: http://dx.doi.org/10.30645/jurasik.v10i1.889

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v10i1.889.g863

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