Industrial process monitoring method based on missing variable PCA model

An industrial process and variable technology, applied in the direction of program control, comprehensive factory control, comprehensive factory control, etc., to achieve the effect of strong generalization ability and expansion of the scope of application

Active Publication Date: 2017-08-25
江天科技有限公司
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  • Application Information

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Problems solved by technology

That is to say, at present, in the process monitoring model based on PCA, the missing variable processing method is usually only regarded as an

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  • Industrial process monitoring method based on missing variable PCA model
  • Industrial process monitoring method based on missing variable PCA model
  • Industrial process monitoring method based on missing variable PCA model

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Embodiment Construction

[0030] The method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0031] Such as figure 1 As shown, the present invention relates to a kind of industrial process monitoring method based on missing variable PCA model, and the concrete implementation steps of this method are as follows:

[0032] Step 1: Under the normal operation state of the production process, use the sampling system to collect samples to form the training data set X∈R n×m, standardize each variable in the matrix X to get a new matrix with a mean of 0 and a standard deviation of 1 Among them, n is the number of training samples, m is the number of process measurement variables, R is the set of real numbers, and R n×m Represents an n×m-dimensional real number matrix, It is a column vector composed of n measured values ​​of the kth variable, subscript k=1, 2, ..., m.

[0033] Step 2: Use the PCA algorithm to solve the matrix The PCA model of A...

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Abstract

The invention discloses an industrial process monitoring method based on a missing variable PCA model, so as to use a missing variable processing method to online estimate principal component information and realize the purpose of executing monitoring on estimation errors. Firstly, the principal component is estimated by assuming that each measurement variable data is missing one by one; and then, with the estimation error of the principal component and the error estimation value of the PCA model as monitored objects, online process monitoring is executed. Although sampling data under a normal working condition do not necessarily satisfy the Gauss distribution hypothesis, the estimation errors generally obey the Gauss distribution. From the point of view, although the method is based on the PCA algorithm, the method does not require hypothetical training data to obey or approximately obey the Gauss distribution, and the application range of the traditional PCA-based process monitoring method is extended to a certain extent. Besides, the method also has the advantage of a high multi-model generalization capability as multiple fault detection models are adopted.

Description

technical field [0001] The invention relates to an industrial process monitoring method, in particular to an industrial process monitoring method based on missing variable PCA model. Background technique [0002] With the wide application of computer technology in the production of industrial processes, industrial objects can measure more and more indicators, and can also store massive process data information. The rich sampling data provides a solid data foundation for the modern industrial process to move towards the "big data" era, and has spawned many data-driven industrial informatization applications and research fields. Data-driven industrial process monitoring is one of the very important branches, which aims to realize the purpose of real-time monitoring of production process failure by mining useful information hidden in process data. In recent years, the research on data-driven process monitoring, especially the process monitoring based on multivariate statistica...

Claims

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Application Information

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IPC IPC(8): G05B19/418
CPCY02P90/02G05B19/41885G05B2219/32339
Inventor 石立康朱莹童楚东
Owner 江天科技有限公司
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