自确认气动执行器健康状况评估算法  

Health evaluation algorithm for self-validating pneumatic actuator

作　　者：冯志刚 杨佳琪 FENG Zhigang;YANG Jiaqi(School of Automation,Shenyang Aerospace University,Shenyang 110000,China)

机构地区：[1]沈阳航空航天大学自动化学院,沈阳110000

出　　处：《哈尔滨工业大学学报》2023年第7期133-140,共8页Journal of Harbin Institute of Technology

基　　金：国家自然科学基金(611104023)。

摘　　要：为了解决自确认气动执行器健康状况评估的问题,提出了一种基于数据驱动的方法,利用执行器正常数据,建立关联向量机回归预测模型,与执行器实际输出作差,获得残差特征值作为事件集;定义健康、亚健康、故障边缘和故障等4个不同健康水平的评价指标作为目标对策集,选用正态分布函数和半梯形函数作为隶属度函数建立表述执行器性能退化程度的基准模型;利用层次分析法融合灰色关联算法和熵值法分别建立局部健康度和综合健康度的权值分配模型;最后采用最小二乘支持向量机确定健康水平。结果表明:该方法可以实现从整体和局部分别对气动执行器进行健康状况评估,具有较好的实用性,能够如实的反映自确认气动执行器的健康状况。In order to solve the health evaluation problem of self-validating pneumatic actuators,a data-driven method was proposed.A prediction model was constructed using relevance vector machine(RVM)regression based on the normal working data of actuators.The residual feature was obtained as the event set by subtracting the actual output of the actuator from the predicted results.The target countermeasure set was established as consisting of four evaluation indexes of health,sub-health,marginal failure,and failure.The normal distribution function and the semi-trapezoidal function were selected as the membership functions to build the benchmark models that express the performance degradation degree of the actuator.The weight distribution models of local health degree and comprehensive health degree were established by using the analytic hierarchy process,grey relation algorithm,and entropy method.Finally,the least squares support vector machine was used to determine the health level.Results show that the method realized the overall and partial health assessment of pneumatic actuators,which is more practical and can reflect the performance status of self-validating pneumatic actuators.

关 键 词：自确认气动执行器 健康状况可信度 模糊综合评价 灰色关联 层次分析法 熵值法 

分 类 号：TP214[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]