Sistem Prediktif Pemeliharaan Hidraulik dengan Pendekatan Algoritma Histogram Gradient Boosting
DOI:
https://doi.org/10.70609/jusifor.v5i1.9630Keywords:
Predictive maintenance, Sistem hidrolik, Histogram gradient boosting, StreamlitAbstract
Hydraulic systems are widely used in industry due to high power density and precision control; however, failures in components such as coolers, valves, pump leakage, and hydraulic accumulators can cause downtime and high maintenance costs. Conventional corrective and preventive maintenance is limited due to reliance on fixed schedules and manual inspection. Therefore, this study applies machine learning-based predictive maintenance to predict hydraulic component conditions. This study aims to predict the condition of four components (Cooler, Valve, Internal Pump Leakage, and Hydraulic Accumulator), evaluate Histogram Gradient Boosting (HGB) using Accuracy and macro F1-score, and implement the model in a Streamlit application. The Condition Monitoring of Hydraulic Systems dataset (2,205 cycles) is used. Sensor data from .txt files is transformed into tabular features per cycle, standardized using StandardScaler, and split into 70:30 training and testing sets. Four independent HGB models are trained and evaluated using confusion matrix, Accuracy, and macro F1-score. Results show strong performance: Cooler_condition (Accuracy 0.998, F1 0.998), Valve_condition (0.834, 0.786), Internal_pump_leakage (0.968, 0.963), and Hydraulic_accumulator/bar (0.989, 0.987). The trained models are saved as .pkl files and integrated into a Streamlit application for interactive prediction and CSV export.
References
[1] J. Garcia, L. Rios-Colque, P. Alvaro, and L. Rojas, “Condition Monitoring and Predictive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges,” Multidiscip. Digit. Publ. Inst., vol. 15, no. 10, 2025, doi: https://doi.org/10.3390/app15105465.
[2] J. Sun, H. Ding, N. Li, X. Sun, and X. Dong, “Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention Mechanism,” Sensors, vol. 24, no. 22, pp. 1–19, 2024, [Online]. Available: https://doi.org/10.3390/s24227267
[3] J. Azeta et al., “Artificial Intelligence and Robotics in Predictive Maintenance: A Comprehensive Review,” Front. Mech. Eng., vol. 11, 2026, doi: https://doi.org/10.3389/fmech.2025.1722114.
[4] M. Achouch et al., “On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges,” Appl. Sci., vol. 12, no. 16, 2022, doi: https://doi.org/10.3390/app12168081.
[5] E. Hermawan, S. D. Panjaitan, and E. F. Ripanti, “Sistem Prediksi Banjir Rob Kota Pontianak Berbasis Machine Learning Menggunakan Framework Streamlit,” J. Edukasi Penelit. Inform., vol. 10, no. 3, 2024, doi: https://doi.org/10.26418/jp.v10i3.79955.
[6] C. Tsallis, P. Papageorgas, D. Piromalis, and R. A. Munteanu, “Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions,” Appl. Sci., vol. 15, no. 9, 2025, doi: https://doi.org/10.3390/app15094898.
[7] M. Mahetaji and J. Brahma, “Enhancing Wellbore Stability Through Machine Learning for Sustainable Hydrocarbon Exploitation,” Sci. Rep., vol. 15, no. 1, 2025.
[8] S. Sethi, “Predictive Maintenance in IoT Environments Using Machine Learning: Opportunities, Challenges, and Innovations,” Int. J. Innov. Res. Eng. Multidisplinary Phys. Sci., vol. 11, no. 4, 2023, doi: https://doi.org/10.5281/zenodo.14945146.
[9] P. Mallioris, E. Aivazidou, and D. Bechtsis, “Predictive Maintenance in Industry 4.0: A Systematic Multi-Sector Mapping,” CIRP J. Manuf. Sci. Technol., vol. 50, pp. 80–103, 2024, doi: https://doi.org/10.1016/j.cirpj.2024.02.003.
[10] J. Wu et al., “Fault Diagnosis of the HVDC System Based on the CatBoost Algorithm Using Knowledge Graphs,” Front. Energy Res., vol. 11, 2023, doi: https://doi.org/10.3389/fenrg.2023.1144785.
[11] S. M. Almazrouei, F. Dweiri, R. Aydin, and A. Alnaqbi, “An Ensemble Machine Learning Model for Predictive Maintenance on Water Injection Pumps in the Oil and Gas Industry,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 11, 2024, doi: 10.14569/IJACSA.2024.0151141.
[12] D. Kim and T. Heo, “Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data,” Sensors, vol. 22, no. 7, 2022, doi: https://doi.org/10.3390/s22072479.
[13] H. N. Noura, T. Chu, Z. Allal, O. Salman, and K. Chahine, “A Comparative Study of Ensemble Methods and Multi-Output Classifiers for Predictive Maintenance of Hydraulic Systems,” Results Eng., vol. 24, 2024, doi: https://doi.org/10.1016/j.rineng.2024.102900.
[14] N. L. Fitriyani et al., “A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases,” Mathematics, vol. 13, no. 13, 2025, doi: https://doi.org/10.3390/math13132194.
[15] F. A. Ramirez-Rivera and F. Guerrero-Rodriguez, “Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation,” Sustainability, vol. 16, no. 18, 2024, doi: https://doi.org/10.3390/su16188015.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Faizah Via Fadhillah, Muhammad Fatchan, Wahyu Hadikristanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
