Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism

Ding, Shenyi; Wang, Zhijie; Kong, Weijian; Yang, Honghai; Song, Guangxiao

doi:10.1016/j.neucom.2019.05.060

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…With the development of industrial control systems [ 1 ] and digital communication technology, the industrial control network needs to face more and more external network access attacks [ 2 – 3 ]. Therefore, more research has been carried out on anomaly detection algorithm [ 4 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

Liu

Kong

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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The industrial control data set has many features and large redundancy, which has a certain impact on the training speed and classification results of the neural network anomaly detection algorithm. However, features are independent of each other, and dimension reduction often increases the false positive rate and false negative rate. The feature sequencing algorithm can reduce this effect. In order to select the appropriate feature sequencing algorithm for different data sets, this paper proposes an adaptive feature sequencing method based on data set evaluation index parameters. Firstly, the evaluation index system is constructed by the basic information of the data set, the mathematical characteristics of the data set, and the association degree of the data set. Then, the selection model is obtained by the decision tree training with the data label and the evaluation index, and the suitable feature sequencing algorithm is selected. Experiments were conducted on 11 data sets, including Batadal data set, CICIDS 2017, and Mississippi data set. The sequenced data sets are classified by ResNet. The accuracy of the sequenced data sets increases by 2.568% on average in 30 generations, and the average time reduction per epoch is 24.143%. Experiments show that this method can effectively select the feature sequencing algorithm with the best comprehensive performance.

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Section: Introductionmentioning

confidence: 99%

Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

Liu

Kong

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…Hence, an improved analysis algorithm-dynamic PCA (DPCA) is proposed, which combines dynamic time series with PCA algorithm. Through time window removal, the dynamic time series group is promoted to the analytical range of PCA, so that the original static data is transformed into real-time dynamic data, and the PCA algorithm can analyse the system operation state accurately [18].…”

Section: Dynamic Pcamentioning

confidence: 99%

Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

2021

IJOMAM

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The sensor faults in electromechanical equipment are diagnosed and predicted to accelerate the industrial automation and stable operation of electromechanical equipment. Firstly, several basic fault prediction methods are introduced, and the data of electromechanical equipment during production can be well diagnosed and processed through data-driven technology. Secondly, the principal component analysis (PCA) method is introduced, and accordingly, the optimized dynamic principal component analysis (DPCA) method and several common sensor faults are proposed. Thirdly, the specific steps of the PCA and DPCA methods are discussed in sensor faults diagnosis, respectively. The PCA method can study the multivariate nonlinear data concurrently. On this basis, the optimized DPCA method can analyse the dynamic features of the real-time data collected by sensors. Finally, the fault type that produces a fixed deviation is studied. Then, the experiment of the draw-wire displacement sensor is simulated through the optimized PCA and DPCA. The square prediction error (SPE) and prediction-error rate are compared with the error. The results show that both methods can predict the fault of the sensor. The results of the T 2 statistics obtained through the PCA show that the sensor fault can be effectively predicted, but the prediction-error rate and fault-omission rate are also high. In the DPCA algorithm, the SPE statistics show that the DPCA algorithm can predict the fault with a small prediction-error rate and faultomission rate, which is far lower than the PCA algorithm. Therefore, dynamic factors analysis can improve sensor fault prediction, which is of great significance to the acceleration of industrial automation and equipment intelligence.

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Modeling of dynamic data-driven approach for the distributed steel rolling heating furnace temperature field

Bao

Zhang

Guo

et al. 2022

Neural Comput & Applic

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Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism

Cited by 5 publications

References 12 publications

Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

Construction of an Electromechanical Automation and Fault Diagnosis System Using Data-Driven Technology in the Context of Big Data

Modeling of dynamic data-driven approach for the distributed steel rolling heating furnace temperature field

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