Blast furnace multiple working condition fault separation method and system based on sparse contribution plot

Abstract

The invention discloses a blast furnace multiple working condition fault separation method and a blast furnace multiple working condition fault separation system based on a sparse contribution plot. The blast furnace multiple working condition fault separation method based on the sparse contribution plot includes the steps of a data collection step: collecting normal data corresponding to each detection variable under different working conditions, and using the normal data as a training sample set; a dictionary augmentation step: obtaining a dictionary based on the training sample set, and obtaining an augmentation dictionary by performing augmentation processing on the dictionary; a sparse coding step: using the augmentation dictionary to achieve sparse coding of online data; a fault detection step: calculating a dictionary reconstitution residual error of the online data based on the sparse coding, comparing the dictionary reconstitution residual error with a control limit of the dictionary reconstitution residual error, and if the dictionary reconstitution residual error is larger than the control limit, judging that a fault occurs and executing a fault separation step; the fault separation step: calculating a sparse contribution value of each detection variable, and drawing the sparse contribution plot according to the sparse contribution values so as to perform fault separation. The blast furnace multiple working condition fault separation method based on the sparse contribution plot has a sparse characteristic, and facilitates rapid and accurate separation for the fault.

Description

technical field [0001] The invention belongs to the field of flow industry process monitoring and fault diagnosis, and in particular relates to a blast furnace multi-working-condition fault separation method and system based on a sparse contribution graph. Background technique [0002] For process monitoring and fault isolation problems, traditional process monitoring methods mostly use Multivariable Statistical Process Control (MSPC), in which Principal Component Analysis (PCA) and Partial Least Squares (Partial Least Squares) , PLS) as representatives and other methods have been successfully applied in industrial process monitoring. [0003] Traditional fault isolation methods, such as contribution graphs and refactoring-based contribution graphs, also achieve good results in some applications. Both the traditional MSPC method and the fault isolation method assume that the process runs under a single operating condition, but in fact, due to changes in raw materials and fu...

AI Technical Summary

Problems solved by technology

[0005] One of the technical problems to be solved by the present invention is to provide a blast furnace multi-working-condition fault separation method based on a sparse contribution graph, which can solve the problem of big data in the blast furnace multi-working Fast and accurate isolation of faults

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