DOI : https://doi.org/10.18517/ijaseit.15.1.12407

Evaluating Machine Learning and Deep Learning Algorithms for Predictive Maintenance of Hydraulic Systems

Ayat Al-Khulaqi (1), Naveen Palanichamy (2), Su Cheng Haw (3), S Charles Raja (4)
(1) Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
(2) Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
(3) Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
(4) Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India
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A. Al-Khulaqi, N. Palanichamy, S. C. Haw, and S. C. Raja, “Evaluating Machine Learning and Deep Learning Algorithms for Predictive Maintenance of Hydraulic Systems”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 52–59, Feb. 2025.
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Hydraulic systems are essential in industries like aerospace and petroleum. However, equipment degradation can lead to failures over time, resulting in costly downtime. Condition monitoring and predictive maintenance can be implemented to predict the equipment's failure before the machine's total failure. Current data-driven methods for predicting faults in hydraulic systems are insufficient due to inaccurate predictions. Our primary objective of this research is to investigate the potential of different classifier models’ predictive capabilities in enhancing the reliability of hydraulic systems. This paper implements two machine learning models (ML), random forest (RF) and categorical boost (Catboost), and one deep learning model (DL), long-short-term memory (LSTM), to predict the maintenance needs of hydraulic system equipment using the data from ZeMA gGmbH. The results of the models are evaluated through different metrics, such as Precision, Recall, F1-Score, and Accuracy. The outcomes of the experiments validated the paramount importance of the RF model, which has proven to be the most efficient and successful in accurately predicting the instances of equipment failure before the occurrence of total system failure. Critical hydraulic system condition components revealed their varying performance across different components, with LSTM excelling in predictiveness of the valve, RF dominating pump predictions, and overall reliability observed for Accumulator and Stable Flag. The experimental findings demonstrate that the proposed method for predicting the state of hydraulic systems outperforms alternative approaches.

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