ISSN   1004-0595

CN  62-1095/O4

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李克斯, 张尔卿, 傅攀, 林志斌. 不完备先验知识下的机械密封端面磨损状态评估研究[J]. 摩擦学学报, 2016, 36(6): 717-725. DOI: 10.16078/j.tribology.2016.06.008
引用本文: 李克斯, 张尔卿, 傅攀, 林志斌. 不完备先验知识下的机械密封端面磨损状态评估研究[J]. 摩擦学学报, 2016, 36(6): 717-725. DOI: 10.16078/j.tribology.2016.06.008
LI Kesi, ZHANG Erqing, FU Pan, LIN Zhibin. Condition Assessment on Mechanical Seal Face Wear Based on Incomplete Prior Knowledge[J]. TRIBOLOGY, 2016, 36(6): 717-725. DOI: 10.16078/j.tribology.2016.06.008
Citation: LI Kesi, ZHANG Erqing, FU Pan, LIN Zhibin. Condition Assessment on Mechanical Seal Face Wear Based on Incomplete Prior Knowledge[J]. TRIBOLOGY, 2016, 36(6): 717-725. DOI: 10.16078/j.tribology.2016.06.008

不完备先验知识下的机械密封端面磨损状态评估研究

Condition Assessment on Mechanical Seal Face Wear Based on Incomplete Prior Knowledge

  • 摘要: 针对机械密封端面全状态先验知识不易获取, 正常开启状态下和未打开状态下的先验知识可以获取的特点, 以声发射信号作为监测信号, 提出基于因子隐马尔可夫模型(FHMM)的机械密封端面磨损状态评估技术. 应用经验模态分解(EMD)方法对原始信号进行分离提取, 得到准确反映信号的特征信息, 将机械密封端面接触状态下的先验特征归一化, 并建立因子隐马尔可夫(FHMM)监测模型; 根据观察序列对数似然度得到评估密封端面磨损状态性能指标, 实现对端面磨损状态的评估. 机械密封端面磨损状态监测试验表明: 该方法能在仅具备端面接触状态先验知识的情况下, 实现对密封端面磨损状态的初步评估, 而且所需样本数较少、训练速度快、结果准确、具有较好扩展性.

     

    Abstract: The prior knowledge of the mechanical seal wear condition during the normal running process is very difficult to obtain, however, the opening and closing condition prior knowledge of mechanical seal is easy to obtain. According to these characteristics, taking the acoustic emission signal as the monitoring signal to detect and assess the mechanical seal face wear condition based on the method of Factor Hidden Markov Model (FHMM). Empirical mode decomposition (EMD) method was applied to extract the original signal feature. Using the contact condition prior normalized feature information of mechanical seal face to establish the FHMM. The logarithmic likelihood degree between the unknown condition feature vector and the monitoring model established can be calculated, then compared it with the seal face performance index to evaluate the wear conditions of a mechanical seal face. The experiment show that this method realized the wear condition assessment of mechanical seal using small number of samples.

     

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