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.

泄漏是液压系统故障的最重要原因之一。泄漏的准确定位对于确保液压系统的安全可靠运行具有重要意义。对于泄漏的早期阶段，液压回路的压力不会发生明显变化，因此无法通过压力传感器进行监控。同时，由于负载和开关状态的影响，液压回路的压力频繁变化，这进一步降低了泄漏定位的精度。在工作中，提出了一种新颖的基于贝叶斯网络（BNs）的多阀系统数据驱动的早期泄漏定位方法。小波变换用于降低信号噪声，而基于BNs的泄漏定位模型用于识别泄漏位置。开发了归一化模型以提高泄漏定位模型的鲁棒性。具有八个阀的液压系统用于演示所提出的早期微泄漏检测和定位方法的应用。