A Data-Driven Predictive Maintenance Approach for Hydraulic Systems in Industrial Applications
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Aerospace and petroleum are two sectors that rely heavily on hydraulic systems. Nevertheless, equipment deterioration might eventually cause breakdowns, leading to expensive downtime. To avoid a complete breakdown of the machine, it is possible to use Condition Monitoring and Predictive Maintenance to anticipate when the equipment will fail. Because of their inherent fallibility, existing data-driven approaches to hydraulic system failure prediction are inadequate. This study is primarily concerned with the development of an enhanced failure prediction system via advanced machine learning methods. The accuracy and reliability of the two models, Random Forest (RF) and LightGBM (LGBM), are assessed to determine which one is better suited for identifying machine failures. Experimental results demonstrated that both models achieved high classification performance, while LightGBM provided the best results with 99.12% accuracy, 99.07% precision, 99.17% recall, and 99.12% F1-score. The proposed RF and LGBM models showed superiority over traditional models, including Logistic Regression (LR), CNN (Convolutional Neural Network), and SVM (Support Vector Machine). The results suggest that predictive maintenance based on ML is a valuable tool for proactively detecting failures and optimizing maintenance plans. By identifying potential equipment failure in hydraulic systems in a timely manner, the proposed approach can be of great benefit to industries to minimize unexpected downtime, minimize operational costs, increase equipment reliability, and improve workplace safety.
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