摘要
分析了机械属性评价方法的优缺点,给出了气动执行器和电动执行器系统机械属性的主要评价因素,并列出了执行器系统评价因素数据获取的3个途径,提出了一种基于最小二乘法的执行器系统组合权重机械属性评价方法.首先运用信息熵理论和层次分析法(AHP,Analytic Hierarchy Process)理论,分别得到两种执行器系统的机械属性权重,然后运用最小二乘法求得执行器系统的组合权重,计算出执行器系统综合评价值,选出最优方案,最后运用具体实例进行了评价方法验证.结果表明,以特定工况下的气动和电动执行器系统为例,运用组合权重机械属性评价方法,得出气动执行器系统比电动执行器系统机械属性更好,符合现场工况;执行器系统的组合权重机械属性评价结果的标准偏差比信息熵和层次分析-模糊综合评价(AHP-FCE,AHP-Fuzzy Comprehensive Evaluation)的都小.
The advantages and disadvantages of mechanical properties evaluation methods were analyzed.The main evaluation factors of mechanical properties of pneumatic and electric actuators system were given,and three ways of acquiring actuator system evaluation factor data were listed. A method of mechanical properties combination weight evaluation for actuator systems based on least squares was proposed. Firstly,two kinds of attribute weights of actuators system mechanical properties were obtained with information entropy theory and analytic hierarchy process（ AHP） theory. Then the combination weights of actuators system were obtained with least squares method,and the comprehensive evaluation values of actuators system were calculated for selecting the most excellent solution. Finally the new evaluation method authentication was carried out with the specific examples. Results show that,as a case of pneumatic and electric actuators system under the specific conditions,the mechanical properties of pneumatic actuator system are better than that of electric actuator system with the combination weight mechanical properties evaluation method,and the result fits the on-site conditions. The standard deviation of the new method for actuators system is smaller than that of information entropy and AHP-fuzzy comprehensive evaluation（AHP-FCE）.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2014年第7期881-886,共6页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金资助项目(U1204107)
河南省高等学校精密制造技术与工程重点学科开放实验室开放基金资助项目(PMTE201318A)
河南理工大学博士基金资助项目(B2012-101)
河南省教育厅科学技术研究重点资助项目(14B460033)
关键词
执行器
机械属性
最小二乘法
信息熵
组合权重
actuators
mechanical properties
least squares
information entropy
combination weights
