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A Dynamic Process Monitoring Method Based on Latent Variable Autoregressive Model

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

Active Publication Date: 2021-05-04
NINGBO UNIV
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  • 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

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  • A Dynamic Process Monitoring Method Based on Latent Variable Autoregressive Model
  • A Dynamic Process Monitoring Method Based on Latent Variable Autoregressive Model
  • A Dynamic Process Monitoring Method Based on Latent Variable Autoregressive Model

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Embodiment Construction

[0072] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0073] 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.

[0074] Table 1: TE process monitoring variables.

[0075] 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 Compressor cycle valve position 6 Reactor feed 17 Stripper bot...

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Abstract

The invention discloses a dynamic process monitoring method based on a latent variable auto-regression model, which aims to establish a latent variable auto-regression model and implement dynamic process monitoring based on the latent variable auto-regression model. Specifically, the method of the present invention defines the least squares objective function of the autoregressive model of latent variables, deduces a corresponding feature mining algorithm, and then establishes a fault monitoring model to implement online fault monitoring. Since the method of the present invention aims at establishing the latent variable autoregressive model, the latent variables of dynamic autocorrelation are excavated, and a corresponding autoregressive model satisfying the least square condition is provided. Through the latent variable autoregressive model, not only the autocorrelation features in the original training data can be mined, but also the influence of latent variable autocorrelation can be eliminated. Therefore, the method of the present invention is significantly different from the traditional dynamic process monitoring method, and the interpretability of the model is stronger. It can be said that the method of the present invention is a more preferred 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 Patents(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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