Minimax entropy-based co-training for fault diagnosis of blast furnace

Gao, Dali; Yang, Chunjie; Yang, Bo; Chen, Yu; Deng, Ruilong

doi:10.1016/j.cjche.2022.12.007

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Among them, the blast furnace hanging is one of the abnormal furnace conditions that occur frequently and are more hazardous. Judging the occurrence of hanging depends largely on the main factors such as the experience of experts and operators, which can lead to misjudgment [3][4][5] . If the prevention and control are not timely, it can cause significant losses.…”

Section: Introductionmentioning

confidence: 99%

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Li,

Meng,

Liu

et al. 2024

ISIJ Int.

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Blast furnace hanging is an abnormal furnace condition with the highest frequency.The judgment of hanging mainly relies on manual analysis, but this approach has strong subjectivity and time lag. In order to solve the above problems, this paper proposes a ReliefF -Decision Tree based anomaly diagnosis model to identify the hanging. Firstly, 10 relevant parameters are extracted based on expert experience, and each characteristic parameter is weighted using ReliefF algorithm after preprocessing. Secondly, the calculated weights of the characteristic parameters are sorted from the largest to the smallest, and the seven most effective characteristic parameter sets are selected as the decision nodes of the decision tree, which is constructed according to the expert rules of hanging diagnosis to complete the training and testing of the classifier. Simulation results show that the accuracy of the ReliefF -Decision Tree model reaches 96.5%, and the recognition rate of the two anomalies of "burden stop" and "sudden rise of differential pressure" exceeds 80%, which is 83.1% and 87.5%, respectively, indicating that the performance of the model is good. Finally, the expert diagnosis system is built, which effectively improves the diagnosis efficiency of hanging.

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Section: Introductionmentioning

confidence: 99%

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Li,

Meng,

Liu

et al. 2024

ISIJ Int.

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Fault diagnosis for blast furnace ironmaking process based on randomized local fisher discriminant analysis

Zhou,

Wu,

et al. 2024

Can J Chem Eng

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Fault diagnosis plays a vital role in ensuring the operation safety of blast furnaces and improving the quality of molten iron in the ironmaking and steelmaking industry. The blast furnace ironmaking process (BFIP) is intrinsically nonlinear. To address the nonlinearity issue of BFIP, a novel fault diagnosis approach that combines the randomized method, local structure information, and Fisher discriminant analysis is proposed in this paper. Using a randomized feature map, the process data is first mapped onto a randomized explicit low‐dimensional feature space. Compared to kernel methods, explicit low‐dimensional random Fourier features considerably reduce the computational cost, particularly for real‐time fault diagnosis for a large training dataset or a large‐scale process. Additionally, the local structure information contained in the randomized low‐dimensional feature space is extracted. The fault diagnosis performance is improved through the exploration of the local structure of random Fourier features. Finally, the blast furnace iron‐marking process state is determined using Bayesian inference. Case studies on a real‐world BFIP are carried out to demonstrate the superior performance of the proposed method in comparison with other related methods.

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A local space transfer learning-based parallel Bayesian optimization with its application

Yang,

Zhang,

et al. 2024

Chinese Journal of Chemical Engineering

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Minimax entropy-based co-training for fault diagnosis of blast furnace

Cited by 3 publications

References 20 publications

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Fault diagnosis for blast furnace ironmaking process based on randomized local fisher discriminant analysis

A local space transfer learning-based parallel Bayesian optimization with its application

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