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

数据驱动的液压系统早期微小泄漏检测与定位方法

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.

泄漏是引起液压系统失效最主要的原因,泄漏的精确诊断与定位对保障液压系统的正常运行具有重要意义。在泄漏发生早期,液压系统的压力信号没有明显变化,压力传感器难以对其进行监测。同时,系统的压力受到负载及开关状态改变而变化,这将导致传感器信号变化剧烈而进一步加剧泄漏检测与定位难度。本文提出了一种数据驱动的液压系统早期微小泄漏检测与定位方法,利用小波变化对信号进行降噪处理,建立基于Bayesian网络的泄漏检测与定位模型进行泄漏检测,通过归一化模型将不同压力下声发射信号特征值转化为目标压力下声发射信号特征值,以提高压力鲁棒性。该方法在实验室的液压系统上得到了验证。