Method for deriving fault diagnosis rules of blast furnace based on deep neural network
US12182700B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2021 |
| Grant date | Dec 31, 2024 |
| Priority date | — |
| Expiry date | Nov 2, 2043 |
Classification
- Technology area (CPC C)Chemistry; Metallurgy
- CPC primaryC21B2300/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure discloses a method for deriving fault diagnosis rules of a blast furnace based on a deep neural network, which relates to the field of industrial process monitoring, modeling and simulation. Firstly, a deep neural network is used to model historical fault data of the blast furnace. Then, for each kind of fault, the process starts from the output layer of the network, wherein sub-models of nodes in the adjacent layers in the deep neural network are established by using the decision tree in sequence, and the if-then rule is derived. Finally, the if-then rules are merged layer by layer, so as to finally obtain fault diagnosis rules of the blast furnace with blast furnace process variables being the rule antecedents and with fault categories being the rule consequents.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.