Intermittent process monitoring data processing method

A data processing and process monitoring technology, applied in the field of control science and engineering, can solve the problems of large randomness, accurate estimation of cluster centers, and limited cluster quality, etc., to achieve easy engineering implementation, small algorithm scale, and improved accuracy Effect

Active Publication Date: 2018-03-13
CIVIL AVIATION UNIV OF CHINA
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Problems solved by technology

However, these two methods have different degrees of limitations in the stage division
K-means clustering is relatively random when selecting the initial cluster center, and is sensitive to noise and outliers, which will lead to unstable clustering results and limited clustering quality
Fuzzy C-means clustering is a local search algorithm, which needs to manually determine ...

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[0058] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0059] Such as figure 1 As shown, the batch process monitoring data division and statistical modeling method provided by the present invention include the following steps carried out in order:

[0060] (1) Using the improved affine propagation clustering algorithm for data pre-division;

[0061] The data pre-division first needs to do the following to the original sampling data: figure 2 The preprocessing shown is to expand the original three-dimensional monitoring data X (I×J×K) along the time axis to obtain K time slice matrices X k (I×J),k=1,2,…,K, then X k (I×J) standardization, centralization, and sequentially find the covariance matrix C corresponding to each time slice k (J×J).

[0062] Among them, I represe...

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Abstract

The invention relates to the field of control science and engineering, especially an intermittent process monitoring data processing method. The method comprises the following steps: a, employing an improved affine propagation clustering algorithm; b, designing a similarity decreasing scanning algorithm; c, determining outliers and wrong classification points, and respectively designing solutions.The improved affine propagation clustering algorithm can reveals a mode switching process between all subperiods of an intermittent process more accurately, and the similarity decreasing scanning algorithm can further reflect the change trend of the internal mode of each subperiod with time. The method for determining the outliers and wrong classification points and respectively designing solutions improves the statistical modeling accuracy, is small in algorithm scale, is simple in calculation, and is easy for engineering implementation.

Description

technical field [0001] The invention relates to the field of control science and engineering, in particular to a batch process monitoring data processing method. Background technique [0002] For a long time, with the market's urgent demand for multi-category, small-batch and high-value-added products, batch production has become the main production method in many industrial fields. However, the complexity of intermittent production will inevitably lead to production reliability and safety issues. In order to capture the mechanism of batch process more accurately to improve monitoring performance and monitor potential safety issues in a timely manner, the academic field has conducted in-depth research on batch processes and proposed many data-driven multivariate statistical analysis methods. For example, the typical principal component analysis method and least squares method and their extended forms have been widely used. However, these traditional methods basically assum...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/2433
Inventor 郭润夏张娜王佳琦
Owner CIVIL AVIATION UNIV OF CHINA
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