Condition Monitoring of Hydraulic Systems Based on Machine Learning And Fusion Methods

Efficient operation of hydraulic systems in industrial settings is crucial for maintaining high performance and productivity. Over time, factors such as wear and tear, aging, and various operational conditions can impact the performance and reliability of hydraulic components. Condition monitoring is essential to ensure the longevity and efficiency of these systems. The proposed methods utilize advanced Machine-learning techniques for the classification of five critical conditions within hydraulic systems such as: 1-Cooling condition, 2-Valvecondition 3-internal pump leakage, 4-Hydraulic accumulator condition, and 5-stable flag condition. We employ classifiers to achieve high accuracy in predicting the condition of hydraulic systems. Additionally, an investigation of the effectiveness of multi-classifier fusion methods, including stacking, Adaboost, and bagging with majority voting, to enhance overall performance is utilized. This research has significant implications for improving maintenance practices and ensuring the efficient operation of hydraulic systems in the oil and gas industry.