A blast furnace fault diagnosis system and method

Abstract

The present invention provides a system and a method for the fault diagnosis of a blast furnace. The system comprises a historical data acquisition module, an actual data acquisition module, a feature weight matrix construction module, a model building module and a blast furnace fault diagnosis module. The method includes the steps of collecting the actual attribute data, the historical attribute data and the corresponding fault state types of the production condition of a blast furnace; according to the importance degree of each attribute for the fault diagnosis, determining the feature weight of the attribute and constructing a feature weight matrix; establishing a twin hyper-sphere support vector machine model for the feature weighting of the fault diagnosis of the blast furnace; taking the actual attribute data of the production condition of the blast furnace into the above established twin hyper-sphere support vector machine model to obtain the operation fault state type of the blast furnace that corresponds to the actual attribute data of the production condition of the blast furnace; and completing the fault diagnosis of the blast furnace. According to the technical scheme of the invention, the importance degree of each attribute for the fault diagnosis of the blast furnace is quantized. Meanwhile, the importance degree of each attribute is integrated into the constructing process of a learning machine. Therefore, the accuracy of the fault diagnosis is improved.

Description

Technical field [0001] The invention belongs to the technical field of blast furnace fault diagnosis, and specifically relates to a blast furnace fault diagnosis system and method. Background technique [0002] The fault diagnosis of blast furnace is of great significance to reduce accidents and economic losses. Blast furnace smelting is the reduction of pig iron ore into iron. It is a continuous and complex production process; in order to prevent abnormal situations, a large number of parameters in the production process need to be monitored, such as hot air temperature, hot air volume, hot air pressure, furnace Top pressure, total pressure difference, upper pressure, lower pressure, oxygen enrichment, permeability index, cross temperature measurement, material speed, physical heat, [Si] content, etc.; therefore, the characteristics of the fault state are high-dimensional and multi-featured signals Therefore, the traditional fault diagnosis method applied to blast furnace fault...

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Problems solved by technology

Blast furnace smelting is to reduce pig iron ore to iron, which is a continuous and complicated production process; in order to prevent abnormal situations, a large number of parameters in the production process need to be monitored, for example, hot air temperature, hot air volume, hot air pressure, furnace Top pressure, total pressure difference, upper pressure, lower pressure, oxygen enrichment, air permeability index, cross temperature measurement, material speed, physical heat, [Si] content, etc.; therefore, the characteristics of the fault state are high-dimensional and multi-featured signals comprehensive embodiment; therefore, the traditional fault diagnosis method applied to the blast furnace fault diagnosis effect is not very ideal

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