A Blast Furnace Fault Detection Method Based on Optimal Extreme Learning Machine

An extreme learning machine and fault detection technology, applied in biological models, computer parts, instruments, etc., can solve the problems of weak local optimization ability and slow convergence speed, so as to improve local search ability, reduce trouble, and improve classification The effect of precision

Active Publication Date: 2019-08-06
NORTHEASTERN UNIV LIAONING
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AI Technical Summary

Problems solved by technology

The ABC algorithm has a strong global search ability, but the local optimization ability is weak, and the convergence speed is slow in the later stage of evolution.

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  • A Blast Furnace Fault Detection Method Based on Optimal Extreme Learning Machine
  • A Blast Furnace Fault Detection Method Based on Optimal Extreme Learning Machine
  • A Blast Furnace Fault Detection Method Based on Optimal Extreme Learning Machine

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Embodiment Construction

[0062] An embodiment of the present invention will be further described below in conjunction with the accompanying drawings.

[0063] In the embodiment of the present invention, such as figure 1 As shown, the blast furnace fault detection system based on optimized extreme learning machine includes data acquisition module, database storage module, extreme learning machine training module and fault detection module;

[0064] In the embodiment of the present invention, the data acquisition module: when training, it is used to collect historical fault types of blast furnace production status data and equipment operation status, and sends them to the database storage module; when actually detecting, it is used to collect blast furnace production status data, and sent to the fault detection module;

[0065] In the embodiment of the present invention, the database storage module is used to store historical data of blast furnace production status and historical fault types of equipme...

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Abstract

The invention proposes a blast furnace fault detection method based on an optimized extreme learning machine, which belongs to the technical field of blast furnace fault detection. The invention optimizes the input weight and hidden layer threshold in the ELM through the ABC algorithm to establish a reasonable and effective classification model; based on Tent The mapping uses a chaotic reverse learning strategy to generate a uniformly distributed initial group to improve the quality of the initial solution and increase the stability of the method; an adaptive search strategy is used to achieve the best balance between global search and local search; through Tent Chaos The local search strategy jumps out of the local optimal solution; the invention improves the convergence speed and optimization accuracy of the ABC algorithm, improves the classification accuracy of the blast furnace fault detection, increases the probability of generating new solutions, avoids falling into the local optimal solution, and ensures the accuracy of the algorithm The direction of rapid evolution improves the local search ability of the algorithm to a certain extent while ensuring the global search ability.

Description

technical field [0001] The invention belongs to the technical field of blast furnace fault detection, and in particular relates to a blast furnace fault detection method based on an optimized extreme learning machine. Background technique [0002] In iron and steel enterprises, the blast furnace ironmaking production is in the leading and important position, and the energy consumption accounts for about 60%. Once a failure occurs, it will cause huge losses of personnel and property; therefore, realize the intelligent, automatic, timely and accurate fault detection process Accurately detect and predict the abnormal state of the blast furnace ironmaking production process, which can not only reduce accidents, ensure stable and reliable production operation, but also reduce production management costs and improve product quality; Enterprise competitiveness is of great significance; blast furnace smelting is a complex continuous production process. Parameters such as , oxygen e...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/00G06N3/06
CPCG06N3/006G06N3/061G06F18/214G06F18/24
Inventor 王安娜王杨孙海静艾青
Owner NORTHEASTERN UNIV LIAONING
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