Mi Juncheng, Yu Jin, Huang Guoqin
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2023 Aug 16;23(16):7211. doi: 10.3390/s23167211.
Direct-drive electro-hydraulic servo valves play a key role in aerospace control systems, and their operational stability and safety reliability are crucial to the safety, stability, and efficiency of the entire control system. Based on the prediction of the performance change of the servo valve and the resulting judgement and prediction of its life, this can effectively avoid serious accidents and economic losses caused by failure due to performance degradation in the work. On the basis of existing research, factors such as opening, oil contamination, and pressure difference are used as prerequisites for the operation of direct-drive electro-hydraulic servo valves. In addition to the current research on pressure gain and leakage, the performance parameters of servo valves, such as overlap, threshold, and symmetry, are also expanded and selected as research objects, combined with pressure design servo valve performance degradation experiments for testing instruments such as flow and position sensors, and data are obtained on changes in various performance parameters. The experimental data are analyzed and a prediction model is built to predict the performance parameters of the servo valve by combining the existing popular neural networks, and the prediction error is calculated to verify the accuracy and validity of the model. The experimental results indicate that as the working time progresses, the degree of erosion and wear on the valve core and valve sleeve of the servo valve increases. Overall, it has been observed that the performance parameters of the servo valve show a slow trend of change under different working conditions, and the rate of change is generally higher under high pollution (level 9) conditions than under other conditions. The prediction results indicate that the predicted values of various performance parameters of the servo valve by the prediction model are lower than 0.2% compared to the experimental test set data. By comparing the two dimensions of the accuracy and prediction trend, this model meets industrial needs and outperforms deep learning algorithm models such as the exponential smoothing algorithm and ARIMA model. The experiments and results of this study provide theoretical support for the life prediction model of servo valves based on neural networks and machine learning in artificial intelligence, and provide a reference for the development of direct-drive electro-hydraulic servo valves in aerospace and other industrial fields for use and failure standards.
直驱式电液伺服阀在航空航天控制系统中起着关键作用，其运行稳定性和安全可靠性对整个控制系统的安全性、稳定性和效率至关重要。基于对伺服阀性能变化的预测以及由此对其寿命的判断和预测，能够有效避免因工作中性能退化导致故障而引发的严重事故和经济损失。在现有研究的基础上，将开度、油液污染和压差等因素作为直驱式电液伺服阀运行的前提条件。除了当前对压力增益和泄漏的研究外，还扩展了伺服阀的重叠度、阈值和对称性等性能参数并将其选为研究对象，结合压力设计伺服阀性能退化实验，使用流量和位置传感器等测试仪器，获取了各种性能参数的变化数据。对实验数据进行分析，并结合现有的流行神经网络建立预测模型来预测伺服阀的性能参数，计算预测误差以验证模型的准确性和有效性。实验结果表明，随着工作时间的推进，伺服阀阀芯和阀套的侵蚀磨损程度增加。总体而言，观察到伺服阀的性能参数在不同工作条件下呈现缓慢变化趋势，且在高污染（9级）条件下的变化率通常高于其他条件。预测结果表明，预测模型对伺服阀各种性能参数的预测值与实验测试集数据相比低于0.2%。通过比较精度和预测趋势这两个维度，该模型满足工业需求，并且优于指数平滑算法和ARIMA模型等深度学习算法模型。本研究的实验和结果为基于神经网络和人工智能中的机器学习的伺服阀寿命预测模型提供了理论支持，并为航空航天等工业领域直驱式电液伺服阀的开发、使用和失效标准提供了参考。
