In industrial production process, it is difficult to predict product quality in advance. Traditional prediction methods are mostly based on complex mechanism models, and the prediction accuracy is not high. This paper uses the historical data of industrial production to forecast, constructs the quality prediction model of neural network, and uses genetic algorithm to optimize the network parameters, so as to avoid the neural network falling into local optimum. This paper takes the data of hot rolling production line as an example and adopts the method to predict the quality index of steel plate. The simulation result shows that the quality prediction model of neural network based on genetic algorithm has better prediction ability. This method has important theoretical value and practical significance for production workers to improve product quality.
Due to the large amount of data in the actual fan equipment failure, the external noise is complicated, and there is a high degree of nonlinearity and complexity, which makes it difficult to extract the fault features. If the model is constructed by the traditional method, the accuracy of the fault prediction is poor. Therefore, considering the advantages of deep learning in data feature extraction, this paper proposes a wind fault prediction method based on deep belief network (DBN). The original raw data is firstly deleted and normalized, and then imported into the DBN for training. The internal parameters of the network are adjusted by reverse learning to improve the feature extraction accuracy. Finally, the BP neural network is used to predict the fault. Comparing the prediction results with the SVRM method, we can find that the method has certain advantages in the fault prediction for the data.
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