Multi-scale binary tree blast furnace fault diagnosis method based on sample segmentation

Abstract

The invention discloses a multi-scale binary tree blast furnace fault diagnosis method based on sample segmentation, belonging to the technical field of blast furnace fault diagnosis. The method comprises the following steps of: firstly, acquiring blast furnace production condition and equipment operation state data, detecting the data and performing normalization for the extracted data through a mean-variance normalization method; converting a blast furnace fault diagnosis problem into a dichotomy problem to perform multi-classifier design; finding a segmentation plane through an improved generalized eigenvalue support vector machine, converting into two dichotomy problems, respectively finding a distance measuring matrix having local properties and being adaptive for each type of fault data itself, and designing two classification hyperplanes based on different scales through the support vector machine. The method provided by the invention is suitable for identification of high-dimensional nonlinear fault data; and by means of segmenting sample data and measuring similarity among the samples with a multi-scale standard, the method gives consideration to global and local logic structures of the identified data, reduces complexity of the identified fault problem and improves precision of fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of blast furnace fault diagnosis, and in particular relates to a multi-scale binary tree blast furnace fault diagnosis method based on sample segmentation. Background technique [0002] The blast furnace is the throat of the iron and steel enterprise. The energy consumption of the blast furnace in the entire iron and steel enterprise accounts for about 60%. Once the abnormal operation of the equipment develops into a production failure, it will bring huge economic losses to the enterprise. Therefore, its effective operation It is very important; the study of intelligent fault diagnosis technology for blast furnace conditions, and timely and accurate diagnosis and monitoring of furnace condition faults are of great significance for improving the economic benefits of enterprises, reducing production costs, and reducing production energy consumption. [0003] Blast furnace smelting is to reduce pig iron ore to ...

AI Technical Summary

Problems solved by technology

[0005] In blast furnace fault diagnosis, due to the difficulty of obtaining fault samples, the number of fault samples between different categories in the training set is difficult to achieve consistency, and there is a large amount of unbalanced data; the prediction process of support vector machine (SVM) using unbalanced data is skewed, As a result, errors are generated; methods for dealing with unbalanced data classification can be roughly divided into two categories: methods based on the algorithm level and methods based on the data level, or a combination of the two; the improvement of the former algorithm requires the introduction of penalty factors or cost functions Increases the complexity of the classifier and makes it difficult to set parameters; the latter method of reconstructing the dataset is prone to over-learning or deletion of meaningful samples

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