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Complex industrial process fault monitoring method based on OICA

An industrial process and fault monitoring technology, applied in the field of data-driven fault monitoring, can solve problems such as information loss, high time cost, and lack of unified standards, and achieve the goals of reducing false positives, reducing computational complexity, and improving accuracy Effect

Active Publication Date: 2019-08-13
BEIJING UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In order to ensure the convergence, the traditional independent principal component analysis algorithm requires the observation signals to be independent of each other. Therefore, the data needs to be preliminarily whitened before the operation. However, the whitened data will inevitably lead to information loss.
Moreover, the fast ICA algorithm used in calculating the unmixing matrix requires a large number of iterative operations, and the time cost is high
In addition, there is a lack of uniform standards in the selection of independent principal components, resulting in the inability to produce stable monitoring results

Method used

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  • Complex industrial process fault monitoring method based on OICA
  • Complex industrial process fault monitoring method based on OICA
  • Complex industrial process fault monitoring method based on OICA

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Embodiment Construction

[0053] Fed-batch fermentation of penicillin is a typical biochemical reaction batch process with many inter-coupling variables. Based on the improved Birol model of the Bajpai mechanism model, Professor Cinar of the Illinois Institute of Technology led the process modeling and control research group to create and develop Pensim2.0 penicillin production simulation software. This simulation platform is specially designed for the penicillin fermentation process. A series of simulations of the penicillin fermentation process can be easily realized on this platform. The process variables sampled by the Pensim2.0 simulation platform are shown in Table 1

[0054] Table 1 Process variables

[0055]

[0056]

[0057] In order to verify the fault monitoring performance of the present invention, the following types of fault data are constructed, which are specifically described in Table 2.

[0058] Table 2 Fault data

[0059]

[0060] The application process of the present inven...

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Abstract

The invention discloses a complex industrial process fault monitoring method based on OICA, The invention is used for improving the accuracy of industrial process fault monitoring and has important significance in improving the safety of a production process to ensure the safety of production equipment and production personnel and improving the product quality. The method comprises two stages of off-line modeling and on-line monitoring. The step of off-line modeling comprises the following steps: preprocessing original data; extracting independent main components and residual errors of the data by adopting OICA; and calculating a control limit through kernel density estimation in the independent component space and the residual space respectively. The step of on-line monitoring comprises the following steps: preprocessing data at the current sampling moment; and calculating the statistics of the data at the current moment, and comparing the statistics with the control limit to judge whether the fermentation process runs normally. The method does not need to assume that data obeys Gaussian distribution, is low in calculation complexity, and is not limited by a hybrid matrix form, sothat the fault monitoring effect is better.

Description

technical field [0001] The invention relates to the field of data-driven fault monitoring technology, in particular to a fault monitoring technology for complex industrial processes. The data-driven method of the present invention is a specific application in the fault monitoring of a typical complex industrial process-penicillin fermentation process. Background technique [0002] Industrial process safety is one of the important guarantees for the new generation of industrial revolution. Process monitoring technology is an important means to achieve safe production in industrial processes, and one of the basic technologies to ensure the transformation and upgrading of manufacturing industry and realize intelligent manufacturing. With the development of modern sensor technology and the advancement of data storage technology, a large amount of industrial process data is preserved, which promotes the application of data-driven process monitoring methods. [0003] At present,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06F17/18G06Q50/04C12P37/00
CPCG06F17/18G06Q50/04C12P37/00G06F2111/20G06F30/20Y02P90/30Y02P90/02
Inventor 常鹏张祥宇卢瑞炜王普
Owner BEIJING UNIV OF TECH
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