Complex industrial process fault monitoring method based on OICA

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,...

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

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