The existing deep learning models can achieve a high level of fault diagnosis accuracy in the case of a large number of samples. However, in actual production, data is often limited due to the difficulty of data collection and labeling. For small sample fault diagnosis, a fault diagnosis method called diffusion model-overlapping-patch vision transformer (DM-OVT) is proposed in this paper. The method adds coordinate attention to the DM, so that it can consider both channel information and spatial information. In the patch embedding part of Vision Transformer, features are first extracted using convolutional layers, and then overlapping patch divisions are used to improve the correlation between each patch. To be specific, DM-OVT first uses short-time Fourier transform to convert the one-dimensional signals into the time–frequency maps. And then inputs them into the DM to generate different classes of fault data according to labels. Finally, OVT is used to classify the expanded data. The effectiveness of the proposed method was tested on data sets from laboratory multistage centrifugal fans and Case Western Reserve University, and the highest accuracy was achieved in the comparison experiments.

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

When wind turbines operate in complex environments, planetary gearboxes easily generate faults that will lead to increased wreck of the equipment or transmission failures. In this paper, a CNN model with adaptive parameters is proposed to realize the identification of planetary gearbox faults and improve the real-time performance and accuracy of fault diagnosis. Firstly, Ensemble Empirical Mode Decomposition (EEMD) is applied to scale the one-dimensional signal to solve the modal mixing problem of input data. Gramian Angular Difference Fields (GADF) is used to convert the processed data into images as the input of CNN model. Secondly, to capture more information and reduce the risk of overfitting, two convolutional neural networks incorporating different activation functions are connected in parallel to propose a multilayer CNN model structure. Additionally, Pied Kingfisher Optimization algorithm (PKO) is improved by integrating Tent chaotic mapping, second-order optimization and simulated annealing algorithm to optimize the multilayer CNN model automatically. Finally, the experimental results show that this improved model achieves real-time diagnosis due to the adaptive parameters, the diagnosis accuracy exceeds 95% under the same proportion of samples, and over 85% under the different proportions of samples. This approach significantly enhances planetary gearbox fault identification reliability.

It is challenging to control and optimize the aluminum electrolysis process due to its non-linearity and high energy consumption. Reducing the cell voltage is crucial for energy consumption reduction. This paper presents an intelligent method of predicting and optimizing cell voltage based on the evaluation of modeling the comprehensive cell state. Firstly, the Savitzky–Golay filtering algorithm(SGFA) is adopted to denoise the sample data to improve the accuracy of the experimental model. Due to the influencing factors of the cell state, a comprehensive evaluation model of the cell state is established. Secondly, the model of the least squares supports vector machine (LSSVM) is proposed to predict the cell voltage. In order to improve the accuracy of the model, the state transition algorithm (STA) is employed to optimize the structure parameters of the model. Thirdly, the optimization and control model of the cell voltage is developed by an analysis of the technical conditions. Then, the STA is used to realize the optimization of the front model. Finally, the actual data were applied to the experiments of the above method, and the proposed STA was compared with other methods. The results of experiments show that this method is efficient and satisfactory. The optimization value of average cell voltage based on the STA-LSSVM is 3.8165v, and it can be used to guide process operation. The DC power consumption is 11,971 KW·h per tonne of aluminum, with a reduction in power consumption of 373 KW·h. This result guarantees the reduction of aluminum electrolysis energy consumption.

This paper describes an intelligent integrated state optimization technique for the lead-zinc imperial smelting process, which features strong nonlinearity, strong coupling, a large time delay, time-varying parameters, and multiple constraints. First, the concept of the exponent of synthetic permeability (ESP) is explained. It reflects the permeability state of the process. Next, predictive models of ESP based on time-sequence and technological parameters are established using neural networks, and an intelligent integrated predictive model of the ESP is constructed by combining these two models with a fuzzy classifier. Then, a fuzzy expert predictor is built to predict the location of the burn-through point. Finally, the optimization of the permeability and heat states is implemented using a fuzzy expert controller with a self-study mechanism for those models. Actual runs show this method to be efficient and practical

In this paper, based on the model of data augmentation and Vision Transformer 16 (ViT16), a method of assessment for electrolysis cell state is presented to get the real-time information of the current cell state, so as to improve current efficiency of process. Firstly, in order to solve the issue of the small sample data and improve classification accuracy, the method of data augmentation is performed on the flame hole images by using convolutional block attention module to improve auxiliary classifier generativhyhee adversarial network. Secondly, the deep feature data of the flame hole images is extracted by the method of ViT16, and the genetic algorithm is applied to eliminate the redundant feature data to improve the accuracy. Thirdly, the support vector machines model is employed to classify the feature data, and the aluminum cells are classified into cold, hot, and normal. Finally, the actual data are applied to the experiments of the above method, the results of experiments show that this method is better than other methods, and the accuracy of classifying the cell state is as high as 98.677%. This is of great significance for the guidance of aluminum electrolysis production process.