INTELLIGENT CONDITION MONITORING OF HYDRAULIC SYSTEMS
DOI:
https://doi.org/10.69980/snerb823Keywords:
Hydraulic systems, Intelligent condition monitoring, Predictive maintenanceAbstract
In industrial settings, hydraulic systems are used extensively, and their reliability and early detection of faults is crucial for efficient performance and reduced maintenance costs. Intelligent condition monitoring has become a successful method to continually evaluate the health of the system based on multisensory operational data and data-driven analytical techniques. The main goals of this study were to assess the operating condition of hydraulic systems through multisensory measurements, to explore the relationship between hydraulic sensor parameters and the system's operating condition, and to create an intelligent condition monitoring system to determine various operating conditions. The operational data were secondary, and a quantitative research design was used involving 2,500 operating cycles. Descriptive statistics and Pearson correlation analysis, one-way analysis of variance, multiple linear regression, multinomial logistic regression and feature importance analysis were used to assess hydraulic system performance and to determine important predictors of hydraulic system health. Results showed that the vibration level and oil temperature measurements were the best predictors of hydraulic system degradation, and measurements of pressure, flow, and efficiency-related parameters also helped to differentiate between healthy and faulty operating states. Multisensory analysis was able to effectively classify different operating conditions and facilitate the accurate evaluation of the health of a hydraulic system. The presented framework shows the importance of combining several sensor measures for intelligent fault identification and predictive maintenance. The research underscores the potential of intelligent condition monitoring techniques for enhancing the reliability of industrial hydraulic systems, minimizing unplanned downtime, and aiding in proactive maintenance decision-making.
References
1.Astolfi, D., De Caro, F., & Vaccaro, A. (2023). Condition monitoring of wind turbine systems by explainable artificial intelligence techniques. Sensors, 23(12), 5376.
2.Chen, Y., Lin, P., Zhu, G., Xiang, Y., Wang, Z., Mou, R., ... & Wang, H. (2025). Key technologies for intelligent construction and smart operation and maintenance of large-scale hydraulic tunnels. Journal of Intelligent Construction, 3(4), 1-19.
3.Coskun, M. Y., & Itik, M. (2023). Intelligent PID control of an industrial electro-hydraulic system. ISA transactions, 139, 484-498.
4.Garcia, J., Rios-Colque, L., Peña, A., & Rojas, L. (2025). Condition monitoring and predictive maintenance in industrial equipment: An nlp-assisted review of signal processing, hybrid models, and implementation challenges. Applied Sciences, 15(10), 5465.
5.Guan, H., Ren, Y., Tang, H., & Xiang, J. (2024). Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects. Measurement Science and Technology, 35(8), 082001.
6.Hong, S. H., & Jeon, H. G. (2022). Monitoring the conditions of hydraulic oil with integrated oil sensors in construction equipment. Lubricants, 10(11), 278.
7.Jian, S. H. I., Dong, L. I. U., & Shaoping, W. A. N. G. (2024). Digital twin for complex electromechanical-hydraulic system PHM: a review. Journal of Mechanical Engineering, 60(4), 66-81.
8.Jiao, Z., Wu, S., Li, Y., Zhang, C., Jin, H., Shu, S., ... & Zhang, H. Y. (2023). Development status and trends of the intelligence of hydraulic components and systems. J. Mech. Eng, 59(20), 357-384.
9.Jin, R., Huang, H., Li, L., Zuo, H., Gan, L., Ge, S. S., & Liu, Z. (2023). Artificial intelligence enabled energy-saving drive unit with speed and displacement variable pumps for electro-hydraulic systems. IEEE Transactions on Automation Science and Engineering, 21(3), 3193-3204.
10.Keleko, A. T., Kamsu-Foguem, B., Ngouna, R. H., & Tongne, A. (2023). Health condition monitoring of a complex hydraulic system using Deep Neural Network and DeepSHAP explainable XAI. Advances in Engineering Software, 175, 103339.
11.Kong, X., Cai, B., Yu, Y., Yang, J., Wang, B., Liu, Z., ... & Yang, C. (2025). Intelligent diagnosis method for early faults of electric-hydraulic control system based on residual analysis. Reliability Engineering & System Safety, 261, 111142.
12.Kumar, P., Park, S., Zhang, Y., Jo, S. H., Kim, H. S., & Kim, T. (2024). A review of hydraulic cylinder faults, diagnostics, and prognostics. International Journal of Precision Engineering and Manufacturing-Green Technology, 11(5), 1637-1661.
13.Liu, J., Yuan, C., Matias, L., Bowen, C., Dhokia, V., Pan, M., & Roscow, J. (2024). Sensor technologies for hydraulic valve and system performance monitoring: challenges and perspectives. Advanced Sensor Research, 3(7), 2300130.
14.Liu, W., Zheng, J., Shi, G., Yuan, Q., & Lu, Y. (2025). Research on parameter identification and fault prediction method of hydraulic system in intelligent sensing agriculture. Measurement: Sensors, 38, 101813.
15.Marfo, W., Tosh, D. K., & Moore, S. V. (2025, February). Federated learning for efficient condition monitoring and anomaly detection in industrial cyber-physical systems. In 2025 International Conference on Computing, Networking and Communications (ICNC) (pp. 740-746). IEEE.
16.Patil, S., Sable, N. P., & Mahalle, P. N. (2025, September). Intelligent Framework for Predictive Maintenance in Hydraulic System Condition Monitoring. In 2025 2nd International Conference on Integration of Computational Intelligent System (ICICIS) (pp. 1-6). IEEE.
17.Qiu, Z., Yuan, X., Wang, D., Fan, S., & Wang, Q. (2023). Physical model driven fault diagnosis method for shield Machine hydraulic system. Measurement, 220, 113436.
18.Ren, X., Guo, Q., & Li, T. (2025). Initial condition-free prescribed performance fault-tolerant control of electro-hydraulic servo systems with state constraints: X. Ren et al. Nonlinear Dynamics, 113(10), 11723-11743.
19.Saeed, M. U., Sun, Z., & Elias, S. (2022). Research developments in adaptive intelligent vibration control of smart civil structures. Journal of Low Frequency Noise, Vibration and Active Control, 41(1), 292-329.
20.Tao, H., Jia, P., Wang, X., & Wang, L. (2024). Real-time fault diagnosis for hydraulic system based on multi-sensor convolutional neural network. Sensors, 24(2), 353.
21.Wang, L., Liu, Y., Yin, H., & Sun, W. (2022). Fault diagnosis and predictive maintenance for hydraulic system based on digital twin model. AIP advances, 12(6).
22.Yan, W., Shi, Y., Ji, Z., Sui, Y., Tian, Z., Wang, W., & Cao, Q. (2023). Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph. Engineering Applications of Artificial Intelligence, 126, 106798.
23.Yang, J., Cai, B., Kong, X., Shao, X., Wang, B., Yu, Y., ... & Liu, Y. (2024). A digital twin-assisted intelligent fault diagnosis method for hydraulic systems. Journal of Industrial Information Integration, 42, 100725.
24.Yang, Y., Ding, L., Xiao, J., Fang, G., & Li, J. (2022). Current status and applications for hydraulic pump fault diagnosis: A review. Sensors, 22(24), 9714.
25.Yao, Y., Han, T., Yu, J., & Xie, M. (2024). Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems. Energy, 291, 130419.
26.Zhang, G., Zhang, K., Shen, W., Xu, G., & Lin, Y. J. (2025). Intelligent fault diagnosis of multi-way directional valves in hydraulic systems using digital twin and deep learning approaches. Mechanical Systems and Signal Processing, 230, 112579.
27.Zhu, Y., Wu, Q., Tang, S., Khoo, B. C., & Chang, Z. (2023). Intelligent fault diagnosis methods for hydraulic piston pumps: a review. Journal of Marine Science and Engineering, 11(8), 1609.
