数据驱动技术对间歇生产过程实时状态监测的研究

【作者】 曲爱妍；

【导师】 陈前；

【作者基本信息】 南京航空航天大学 ， 精密仪器及机械， 2004， 硕士

【摘要】 本文主要研究了运用多向主元分析法和核函数法概率密度估计相结合的多元统计过程监控技术对间歇生产过程进行实时的状态监测与故障诊断。用核函数法概率密度估计对间歇生产过程进行实时状态监测的主要优点是它属于非参数法概率密度估计的一种，不需要数据总体的任何先验知识或是假设而直接基于实测数据样本求出总体的概率分布密度函数，摆脱了对不可靠的先验知识的依赖。本文从这一背景出发，重点是对用核函数法概率估计对间歇生产过程实时状态监测的方法进行较广泛、深入的研究。 在传统的实时监测方法中缺乏合适的统计量能够反映间歇生产过程中主元空间的实时变化。针对这一缺点，本文首次提出用KDE提取主元的概率密度函数用作为实时状态监测图，应用于对间歇生产过程主元空间实时状态监测的问题上。 另外，本文也尝试用主元PDF对间歇生产过程的故障批次进行检测，取得了较好的效果，从而为今后间歇生产过程批次检测的问题提供了一个新的选择。更多还原

【Abstract】 This paper investigates the application of the multivariate statistical process monitoring and control technology, which employs both Multiway Principal Component Analysis (MPCA) and Kernel Density Estimation (KDE), to real time status monitoring and fault diagnosis of batch production processes. KDE is a non-parametric method which is capable of extracting the population’s Probability Density Function (PDF) based on data sample only without any a prior knowledge about the statistic properties of the data regime. In this thesis, it is conducted the implementation of the KDE for monitoring the performance of batch production processes.The conventional real-time monitoring method does not use the non-parametrical PDF of the principal components, which are capable of indicating the real-time changes of batch production processes. To improve the monitoring sensitivity, it is for the first time to propose the utilization of the PDF of the principal components in real-time to monitor batch production process.Besides the above-mentioned efforts, this thesis also applies the PDF of the principal components to detect the faulty batches of the batch process and the result is encouraging, which provides a new option for process inspection.更多还原