Industrial production process fault monitoring method based on hierarchical non-Gaussian monitoring algorithm

Abstract

The invention discloses an industrial production process fault monitoring method based on a hierarchical non-Gaussian monitoring algorithm. The industrial production process fault monitoring method comprises the steps of collecting train data and to-be-detected data, calculating the cross-entropy between every two input variables, according to the cross-entropy, dividing all the input variables into various subblocks, building a non-Gaussian monitoring model in each subblock by utilizing a two-layer non-Gaussian monitoring algorithm to extract data of the non-Gaussian part in each subblock, and calculating control limits and a statistic amount of the data of the non-Gaussian parts; in each subblock, calculating data of the remaining Gaussian part to obtain the control limits and statisticamount of the Gaussian parts; conducting fault detection through the control limits and the statistic amount. The industrial production process fault monitoring method based on the hierarchical non-Gaussian monitoring algorithm is better than other traditional methods in fault detection of the non-Gaussian process, not only can the highly complex coupling relationship among variables be sufficiently considered, but also the non-Gaussian part of the data with unknown distribution characteristics can be extracted, and thus the fault detection in the chemical engineering process is more efficientand more accurate.

Description

technical field [0001] The invention belongs to the field of industrial process system engineering and relates to a fault monitoring method applied to industrial processes such as chemical production. Background technique [0002] The modern industrial process has the characteristics of large scale, multiple levels, complex structure, and high safety requirements. Therefore, process monitoring technology plays a pivotal role in modern industry, and the process of industrial production will generate a large amount of data. These process data usually have different process characteristics, and the distribution characteristics are unknown, and some data also have high-dimensional characteristics, which leads to the traditional global fault monitoring that cannot be applied to complex industrial production processes. In order to obtain the relationship between complex process variables and through fault monitoring To reflect the local behavior of the process, a series of hierarc...

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Problems solved by technology

[0005] In order to overcome the problem that there are many process variables in the fault monitoring process, it is necessary to divide the variables into multiple sub-blocks to facilitate local fault monitoring. Problem, the purpose of this invention is to propose a kind of industrial production process fault monitoring method based on hierarchical non-G

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