Process parameter root cause location method and related device

Abstract

Embodiments of the present invention provide a process parameter root cause location method and related devices. The method includes: respectively dividing process parameters and sample output time into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals. interval; respectively for each first subinterval and each second subinterval, determine the median of each first subinterval, generate the first parameter data, determine the median of each second subinterval, and generate the first time data; after the first processing is performed on the sample label and the first parameter data, the correlation coefficient of the parameter trend fluctuation is obtained; the second processing is performed on the sample label, the process parameter data and the first time data, and the correlation coefficient of the parameter time series trend fluctuation is obtained; Based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation, the comprehensive index of the process parameter is calculated. Taking into account the correlation analysis of parameter trend fluctuations and parameter time series trend fluctuations and label fluctuations, so as to effectively identify suspicious parameters.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a process parameter root cause location method and a related device. Background technique [0002] In the industrial scene, automatic glass production has been realized. During the production process, the process equipment will automatically save the actual state value of the process parameters corresponding to the glass production process. For large batches of glass with the same process, the parameter settings of the process equipment remain consistent, but fluctuations to varying degrees may lead to poor glass output. [0003] Based on real-time state records of production parameters and well-designed algorithms, effectively mining the correlation between parameter fluctuations and failures is the key to locating the root cause of poor equipment parameters. [0004] At present, one way to determine the root cause is based on the classic Pearson\Kendall correlation coefficient f...

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Problems solved by technology

[0005] The above two methods consider the factors affecting the root cause incompletely, which leads to the inaccurate root cause of the final calculation

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