Invention Application
- Patent Title: METHOD FOR DERIVING FAULT DIAGNOSIS RULES OF BLAST FURNACE BASED ON DEEP NEURAL NETWORK
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Application No.: US17324410Application Date: 2021-05-19
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Publication No.: US20210365784A1Publication Date: 2021-11-25
- Inventor: Xiaoke HUANG , Chunjie YANG
- Applicant: Zhejiang University
- Applicant Address: CN Hangzhou
- Assignee: Zhejiang University
- Current Assignee: Zhejiang University
- Current Assignee Address: CN Hangzhou
- Priority: CN202010422427.6 20200519
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N5/00 ; G06N3/063 ; G06N3/04 ; G06N5/02 ; C21B7/24

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.
Public/Granted literature
- US12182700B2 Method for deriving fault diagnosis rules of blast furnace based on deep neural network Public/Granted day:2024-12-31
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