Feature selection method based on feature reconstruction and heterogeneous data joint modeling

Abstract

The invention belongs to the technical field of fault monitoring and diagnosis, and provides a feature selection method based on feature reconstruction and heterogeneous data joint modeling, which comprises the following steps: 1) collecting image data in an industrial production process; 2) performing feature extraction on the picture in the original data to form an image data vector; 3) on the basis of feature reconstruction and heterogeneous data joint modeling, adding sparse items according to the sparse requirement on the selected feature, and constructing an objective function; and 4) carrying out optimization solution on the objective function obtained in the previous step, carrying out simulation verification on the method, carrying out fault diagnosis on the data of feature selection by using a classification method, and determining the calculation time and the classification accuracy. According to the method, the working condition can be described in a full-process full-viewfull-period mode, the advantages of combined big data are fully exerted, the purpose of reducing the data dimension is achieved, then the calculation amount can be reduced, the calculation time can beshortened, and online fault detection can be achieved.

Description

technical field [0001] The invention belongs to the technical field of fault monitoring and diagnosis, and in particular relates to a feature selection method based on feature reconstruction and joint modeling of heterogeneous data. Background technique [0002] With the rapid development of modern industry, the production equipment in modern enterprises is increasingly large-scale, continuous, high-speed and automatic. The structure and composition of equipment are very complicated, the production scale is very large, and the connection between various departments is also particularly close. The actual production process not only has a large number of physical and chemical variables and collected images and sounds, but also different fault monitoring methods should be selected for the characteristics of different production processes, so as to effectively detect faults. The process of fused magnesium furnace is a complex process that includes a large number of physical and ...

AI Technical Summary

Problems solved by technology

[0003] Existing research results are limited to separate modeling and diagnosis of variable data and multimedia heterogeneous data, which ignores the inevitable connection between big data and loses the Inherent advantages of big data

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