基于SOM复杂机电系统服役过程动态标记方法  

SOM-Based Dynamic Tagging Method of Operation Process for Complex Electromechanical System

作　　者：谢军太 吕晓喆[1,2] 高建民 高智勇[1,2] 王伟[1,2] XIE Juntai;Lü Xiaozhe;GAO Jianmin;GAO Zhiyong;Wang Wei(Western China Institute of Quality Science and Technology,Xi’an Jiaotong University Xi’an,710049,China;State Key Laboratory of Manufacturing Systems Engineering,Xi’an Jiaotong University Xian,710049,China)

机构地区：[1]西安交通大学中国西部质量科学与技术研究院,西安710049 [2]西安交通大学机械制造系统工程国家重点实验室,西安710049

出　　处：《振动．测试与诊断》2020年第2期341-347,421,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：国家重点研发计划资助项目(2017YFF0210500);国家质量监督检验检疫总局质量基础设施效能研究重点实验室开放研究课题基金资助项目(KF20180301)。

摘　　要：针对流程工业复杂机电系统状态不断更迭、性能出现漂移等问题,提出了一种基于自组织特征映射网络的系统服役过程动态标记方法。首先,构建多变量间耦合关系网络,并在此基础上提取网络特征;其次,将动态标记过程分为状态主动更新过程和状态主动更新过程两个阶段,状态被动更新过程通过不断训练自组织特征映射网络来适应系统新状态出现及性能漂移等情况,状态主动更新过程可用于消除系统已消亡状态对网络模型产生的影响;最后,通过分析实际化工生产系统监测数据对所提方法进行有效性验证。实验结果表明,该方法可有效标记复杂机电系统服役过程中不断变化的多种状态,并建立符合系统动态演化过程的状态标记知识库,从而为系统状态辨识和预测提供可靠依据。In light of the continuous change of the state and the drift of performance of complex electromechanical systems in process industry, a dynamic state tagging method based on self-organizing feature map network is proposed. First, a multi-variable coupling relationship network is construct and the characteristics are extracted.Second, the dynamic tagging process is divided into two stages where states are updated passively and actively respectively. During the former process, the self-organizing feature map network is constantly trained to adapt to new situations and drift of performance. During the later process, the influence of the system's historical states on the network model is eliminated. Finally, the proposed method is verified effective byanalyzing the data of the real chemical production system. Experimental results show that this method is able to mark the constantly changing multiple states during the operation of complex electromechanical systems. It establishes a state marking knowledge base in agreement with the dynamic evolution process of the system, providing a basis for the identification and prediction of system state.

关 键 词：复杂机电系统 自组织特征映射 网络结构熵 网络效率 状态动态标记 

分 类 号：TH17[机械工程—机械制造及自动化]