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Data Mining of Historic Data for Process Identification

机译：流程识别历史数据的数据挖掘

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Performing experiments for system identification is often a time - consuming task which may also interfere with the process operation. With memory prices going down, it is more and more common that years of process data - are stored (without compression) in a history database. The rationale for this work is that in such stored data there must already be intervals informative enough for system identification. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identification (rather than performing completely automatic system identification). For each loop, 4 stored variables are required; setpoint, manipulated variable, process output and mode of the controller. The proposed method requires a minimum of knowledge of the process and is implemented in a simple and efficient. recursive algorithm. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a chi-square test to check that at least one estimated parameter,is statistically significant. The use-of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from more than 200 control loops. It was able to find all intervals in which known identification experiments were performed as well as many other useful intervals in closed/open loop operation.
机译：对系统识别执行实验通常是耗时的任务，其也可能干扰处理操作。随着内存价格下降，它越来越普遍，多年的流程数据 - 在历史数据库中存储（没有压缩）。这项工作的基本原理是，在这样的存储数据中，必须已经有足够的间隔信息​​，以便系统识别。因此，该项目的目标是找到一种搜索和标记适合于过程识别的间隔的算法（而不是执行完全自动系统识别）。对于每个循环，需要4个存储的变量;设定值，操纵变量，控制器的过程输出和模式。该方法需要最少的过程知识，并在简单有效地实现。递归算法。该方法的基本特征是搜索输入和输出的激励，然后估计Laguerre模型与Chi-Square测试结合检查至少一个估计参数，是统计上的重要性。 Laguerre模型的使用对于处理具有死区时间而无明确延迟估计的流程至关重要。该方法在来自200多个控制循环的三年数据上进行了测试。它能够找到所有的间隔，其中在闭合/开环操作中进行已知的识别实验以及许多其他有用的间隔。

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