Zhejiang University

Chunjie Yang

Xin Wang

Xiaoyu Tang

Xiaoke Huang

Wenhai Wang

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.

Method for deriving fault diagnosis rules of blast furnace based on deep neural network

A method and device for blast furnace state monitoring based on multi-modes fusion. In the process of blast furnace state monitoring, the concept of sub-mode is introduced, and the method for pre-acquisition and multi-modes weighted fusion by sub-mode is proposed to monitor blast furnace state. After preprocessing the main parameter data of the blast furnace, several sub-modes of the blast furnace and their corresponding blast furnace state indication variable data are obtained by means of a mean shift clustering algorithm. Taking the real-time parameter data of the blast furnace as inputs, the Euclidean distance between the real-time parameter data of the blast furnace and each sub-mode is calculated, and the sub-modes are weighted and fused according to the distance, and the fusion result is the real-time blast furnace state, so as to realize the real-time state monitoring of the blast furnace in a smooth operation.

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Hangzhou, China

Chunjie Yang