Dynamic process monitoring method based on a latent variable autoregression model

An autoregressive model and dynamic process technology, applied in character and pattern recognition, complex mathematical operations, instruments, etc., can solve problems that do not involve research and application

Active Publication Date: 2019-03-26
NINGBO UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, looking at the existing scientific research literature and patent materials, there is no research and application in this area

Method used

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  • Dynamic process monitoring method based on a latent variable autoregression model
  • Dynamic process monitoring method based on a latent variable autoregression model
  • Dynamic process monitoring method based on a latent variable autoregression model

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Embodiment Construction

[0069] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0070] Such as figure 1 As shown, the present invention discloses a dynamic process monitoring method based on latent variable autoregressive model, and the specific implementation method is as follows.

[0071] Table 1: TE process monitoring variables.

[0072] serial number

variable description

serial number

variable description

serial number

variable description

1

Material A flow

12

separator level

23

D feed valve position

2

Material D flow

13

separator pressure

24

E feed valve position

3

Material E flow

14

Separator bottom flow

25

A feed valve position

4

total feed flow

15

Stripper grade

26

A and C feed valve positions

5

circulation flow

16

Stripper pressure

27

Co...

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Abstract

The invention discloses a dynamic process monitoring method based on a latent variable autoregression model, and aims to establish the latent variable autoregression model and implement dynamic process monitoring on the basis of the latent variable autoregression model. Specifically, the method comprises the steps of defining a least square objective function of an autoregression model of a latentvariable, inferring a corresponding feature mining algorithm, and then establishing a fault monitoring model so as to implement online fault monitoring. According to the method disclosed by the invention, the dynamic autocorrelation latent variable is mined by establishing the target of the latent variable autoregression model, and the autoregression model meeting the least square condition is given correspondingly. Through the latent variable autoregression model, autocorrelation characteristics in original training data can be mined, and the influence of latent variable autocorrelation canbe eliminated. Therefore, the method provided by the invention is obviously different from the traditional dynamic process monitoring method, and the interpretability of the model is stronger. In other words, the method provided by the invention is a more preferable dynamic process monitoring method.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a dynamic process monitoring method based on latent variable auto-regression model. Background technique [0002] In recent years, there has been an upsurge in the research and application of "big data" in all walks of life. At the same time, the degree of utilization of data reflects the degree of modernization of industrial process objects. In the industry, especially in process industry production workshops, advanced instrumentation technology and computing technology have been widely used, and production process objects can be stored offline and measure massive amounts of data online. These data provide a solid data foundation for industrial process research and application of "big data" methods. Taking production safety as an example, these sampling data hide information that can reflect the operating status of the production process, and the monitoring of the ope...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/2135
Inventor 吴华史旭华童楚东
Owner NINGBO UNIV
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