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A Dynamic Process Monitoring Method Based on Structured Canonical Variable Analysis

A typical variable and dynamic process technology, applied in the field of data-driven process monitoring, can solve problems such as inability to reflect correlation

Active Publication Date: 2021-03-09
NINGBO UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this method fails to consider the dynamic relationship between past data and future data like the CVA algorithm, and cannot reflect the correlation from the time series

Method used

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  • A Dynamic Process Monitoring Method Based on Structured Canonical Variable Analysis
  • A Dynamic Process Monitoring Method Based on Structured Canonical Variable Analysis
  • A Dynamic Process Monitoring Method Based on Structured Canonical Variable Analysis

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Embodiment Construction

[0062] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0063] Such as figure 1 As shown, the present invention discloses a dynamic process monitoring method based on structured canonical variable analysis. The specific implementation process of the method of the present invention and its superiority over existing methods will be described below in conjunction with an example of a specific industrial process.

[0064] The application object is from Tennessee-Eastman (TE) chemical process experiment, and the prototype is an actual process flow of Eastman chemical production workshop. Currently, the TE process has been widely used in fault detection research as a standard experimental platform due to its complexity. The whole TE process includes 22 measured variables, 12 manipulated variables, and 19 component measured variables. The TE process object can simulate a v...

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Abstract

The invention discloses a dynamic process monitoring method based on structured typical variable analysis, aiming at inferring a structured typical variable analysis algorithm and implementing dynamic process monitoring based on the algorithm. Specifically, the present invention improves the optimization target of the typical variable analysis algorithm and takes the structural thinking into account, thereby inferring a new algorithm to mine autocorrelation features. In the process of extracting potential features, the method of the present invention simultaneously considers the correlation between future data and past data, that is, considers the autocorrelation feature on the time series. In addition, the monitoring of autocorrelation features is realized by constructing the past score vector and the future score vector respectively during monitoring. It can be said that the method of the present invention deduces a brand-new dynamic modeling algorithm: a structured typical variable analysis algorithm, on which the implementation of dynamic process monitoring should have more superior fault monitoring performance.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a dynamic process monitoring method based on structured typical variable analysis. Background technique [0002] Driven by the upsurge of industrial "big data", modern industrial processes are gradually moving towards digital management, and the utilization of data by industrial processes reflects the modern management level. This is mainly due to the rapid development and wide application of advanced instrument technology and computing technology. Production process objects can store and measure massive amounts of data offline and online. These data contain useful information that can reflect the operating status of the production process, and the use of sampling data to monitor the operating status of the process has been favored by many scholars. In the field of academic research and industrial practice, researchers and enterprise technicians have invested a lot of m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 来赟冬童楚东朱莹
Owner NINGBO UNIV
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