The hot-rolled strip steel is a critical process in steel production, where equipment stability directly impacts product quality. Traditional evaluation methods struggle to balance uncertainties between influencing factors and expert judgments. This study proposes a novel fault evaluation framework: first, a cloud rough model is adopted to handle multi-source uncertainties in expert evaluations; second, game theory is used to optimize the combined weights of subjective and objective criteria; finally, an improved Grey Relational Analysis-Technique for Order Preference by Similarity to Ideal Solution (GRA-TOPSIS) method constructs a dual-scale measurement model by fusing grey relational degree and Euclidean distance, effectively reducing the influence of geometric sequence similarity and distance on fault prioritization and enhancing the robustness of ranking results under complex working conditions. A case study on hot rolling loopers demonstrates a diagnostic accuracy of 90%. Comparative analyses with seven typical multicriteria decision-making methods indicate the proposed method's feasibility and effectiveness in practical applications, offering a novel reference for equipment status evaluation.

In this paper, the parameter identification problem of FIR (Finite Impulse Response) systems with quantized inputs and binary outputs is studied when it is under data tamper attacks. Tampering attacks disrupt the integrity and accuracy of data, and the quantification of inputs and outputs further reduces the information of data. In order to make full use of the information in the data, this paper uses the type of persistent regressors to characterize the excitation degree of the input and designs different identification algorithms that remain consistent under attacks. When the excitation of the system by the input is strong, a three-step identification algorithm based on empirical measurement and least squares method is proposed, which can simultaneously conduct consistent identification of the attack strategy and the system parameter vector. When the excitation is weak, a compensation strategy is developed, which introduces a reprocessing module to sacrifice part of the data for consistency estimation of the attack strategy. Subsequently, the estimated attack strategy is used to identify the system parameter vector, and the asymptotic efficiency of the algorithm is given. Finally, simulation results are provided to illustrate the effectiveness of the algorithms designed in this paper.

Energy consumption accounting and prediction in the medium and thick plate rolling process are crucial for controlling costs, improving production efficiency, optimizing equipment management, and enhancing the market competitiveness of enterprises. Starting from the perspective of integrating process mechanism and industrial big data, we overcame the difficulties brought by complex and highly nonlinear coupling of process variables, proposed a rolling power consumption accounting algorithm based on time slicing method, and gave a calculation method for the additional power consumption of the main motor for rough rolling and finishing rolling (auxiliary system power consumption, power loss, main motor power consumption deviation); with the help of SIMS model, forward recursion, and reverse recursion pass rolling force estimation strategies are proposed, and the rated power consumption of the main motor was predicted. Furthermore, a random forest regression model of additional power consumption based on data was established, and then a prediction algorithm for the comprehensive power consumption of billet rolling was given. Experiments showed the effectiveness of the proposed method.

Named entity recognition is not only the first step of text information extraction, but also the key process of constructing domain knowledge graphs. In view of the large amount of text data, complex process flow and urgent application needs in the hot strip rolling process, a novel named entity recognition algorithm based on BERT-Imseq2seq-CRF model is proposed in this paper. Firstly, the algorithm uses the BERT preprocessing language model to mine the dependencies in the domain text and obtain the corresponding representation vector. Then, the representation vector is sent to the encoder layer, and the output vector is input to the decoder at the same time, on the premise that the original model only considers the semantic vector. The Teacher-Forcing mechanism is integrated into the decoder layer to randomly modify the labeling results, and error accumulation is avoided to guarantee the sequence recognition effect. Finally, the validity of the labeling results is checked according to the conditional random field constraints, and the overall labeling quality of the algorithm is improved. The experimental results show that this model can efficiently and accurately predict the physical label of hot strip rolling, and the model performance index is better than other models, with the F1-Score reaching 91.47%. This model further provides technical support for information extraction and domain knowledge graph construction of hot strip rolling.