Product quality monitoring method based on autoregression total projection to latent structures (T-PLS)

A product quality and autoregressive technology, applied in measuring devices, special data processing applications, instruments, etc., can solve problems such as long delays and quality variables of continuous chemical processes that cannot be measured online

Inactive Publication Date: 2013-08-14
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0003] The present invention proposes a new quality monitoring method of the autoregressive full latent structural projection model (AR-TPLS) aiming at the situation that the quality variable of the continuous chemical process cannot be measured online or has a long time delay

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  • Product quality monitoring method based on autoregression total projection to latent structures (T-PLS)
  • Product quality monitoring method based on autoregression total projection to latent structures (T-PLS)
  • Product quality monitoring method based on autoregression total projection to latent structures (T-PLS)

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Embodiment Construction

[0041] The implementation flow chart of the present invention is as figure 1 As shown, the specific implementation is as follows:

[0042] Let the input matrix , consisting of N samples, each containing n process variables; the output matrix It is also composed of N samples, and each sample is composed of m quality variables. Since changes in quality variables are usually caused by process variables, there is a certain correlation between X and Y, and X and Y can be described as follows

[0043] (1)

[0044] in Regression coefficient matrix representing information about X and Y, represents the change in mass variable that can be explained by X, Represents the part that cannot be explained by X, and satisfies

[0045] (2)

[0046]here and are X and row vector of .

[0047] because

[0048] (3)

[0049] Therefore, one can directly find

[0050] (4)

[0051] I...

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Abstract

The invention belongs to the field of quality monitoring, and mainly relates to a product quality monitoring method based on an autoregression total projection to latent structures (T-PLS). The method comprises the following steps of: projecting input and output data into four sub spaces, wherein the four sub spaces comprise a principal element sub space and a residual sub space generated by a quality variable predicted value, as well as a principal element sub space and a residual sub space generated by process variable residual; and establishing corresponding statistical magnitudes for performing fault detection on parts irrelevant with quality variables in the quality variables and process variables. With the adoption of the method, the complicated solving process of the nonlinear iteration partial least squares (NIPALS) in the traditional PLS (partial least-squares)-based monitoring method and in the improved monitoring method based on the total projection to latent structures (T-PLS) is avoided, and the variation problem that the residual in the later process still contains a large variance which is not suitable for being monitored by Q statistical magnitude is overcome.

Description

technical field [0001] The invention belongs to the field of quality monitoring, and mainly relates to a product quality monitoring method based on an autoregressive full latent structure projection model. Background technique [0002] It is very important for a factory to maintain a stable high-quality product. A common approach is to monitor and control key quality variables to ensure product quality. However, the most difficult problem in the quality monitoring process is the difficulty of real-time online measurement of quality variables, such as components in petroleum products, reactant concentrations in chemical reactors, and molecular weight in polymerization reactants, etc. Due to technical or economical reasons, conventional sensors cannot be used for direct measurement, and methods such as manual timing sampling and laboratory testing are usually used, which are time-consuming and have a certain delay. Using historical data to establish a soft sensor model is on...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/00G06F19/00
Inventor 文成林苑天琪
Owner HANGZHOU DIANZI UNIV
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