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模糊聚类下气动阀门粘滞检测方法的改进

郑丽丽 王志国 刘飞

郑丽丽, 王志国, 刘飞. 模糊聚类下气动阀门粘滞检测方法的改进[J]. 机械科学与技术, 2018, 37(2): 300-305. doi: 10.13433/j.cnki.1003-8728.2018.0222
引用本文: 郑丽丽, 王志国, 刘飞. 模糊聚类下气动阀门粘滞检测方法的改进[J]. 机械科学与技术, 2018, 37(2): 300-305. doi: 10.13433/j.cnki.1003-8728.2018.0222
Zheng Lili, Wang Zhiguo, Liu Fei. Improved Detection of Pneumatic Control Valve Stiction using Fuzzy Clustering[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 300-305. doi: 10.13433/j.cnki.1003-8728.2018.0222
Citation: Zheng Lili, Wang Zhiguo, Liu Fei. Improved Detection of Pneumatic Control Valve Stiction using Fuzzy Clustering[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 300-305. doi: 10.13433/j.cnki.1003-8728.2018.0222

模糊聚类下气动阀门粘滞检测方法的改进

doi: 10.13433/j.cnki.1003-8728.2018.0222
基金项目: 

国家自然科学基金项目(61134007)资助

详细信息
    作者简介:

    郑丽丽(1988-),硕士研究生,研究方向为阀门粘滞故障的检测与补偿,953913272@qq.com

    通讯作者:

    刘飞,教授,博士生导师,fliu@jiangnan.edu.cn

Improved Detection of Pneumatic Control Valve Stiction using Fuzzy Clustering

  • 摘要: 在工业过程控制中,气动阀门的粘滞非线性特性会导致控制回路性能下降甚至振荡。针对已有基于模糊聚类的阀门粘滞检测方法在故障检测过程中容易出现错误诊断问题,提出了改进方法,能有效识别外部干扰和阀门粘滞。首先利用模糊聚类算法对回路日常运行数据进行聚类分析得到聚类中心,根据粘滞阀门输入输出数据的分布特性,评价聚类中心的线性拟合度。然后对聚类中心所构成四边形进行凹凸性识别,再根据聚类中心的分布特征定义了一种新粘滞指标。通过给出的仿真实验,以及化工厂的两个流量控制回路故障检测实验,验证了所提方法的有效性和准确性。
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出版历程
  • 收稿日期:  2016-12-22
  • 刊出日期:  2018-02-25

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