A data-driven early micro-leakage detection and localization approach of hydraulic systems

Abstract

Leakage is one of the most important reasons for failure of hydraulic systems. The accurate positioning of leakage is of great significance to ensure the safe and reliable operation of hydraulic systems. For early stage of leakage, the pressure of the hydraulic circuit does not change obviously and therefore cannot be monitored by pressure sensors. Meanwhile, the pressure of the hydraulic circuit changes frequently due to the influence of load and state of the switch, which further reduces the accuracy of leakage localization. In the work, a novel Bayesian networks (BNs)-based data-driven early leakage localization approach for multi-valve systems is proposed. Wavelet transform is used for signal noise reduction and BNs-based leak localization model is used to identify the location of leakage. A normalization model is developed to improve the robustness of the leakage localization model. A hydraulic system with eight valves is used to demonstrate the application of the proposed early micro-leakage detection and localization approach.

Keywords

micro-leakage localization / normalization model / hydraulic system / Bayesian networks

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