Maintenance Spare Parts Prediction Based on Multilevel Migration Learning CNN-ISE-Attention-BiLSTM

Scientific and accurate prediction of the consumption of equipment maintenance spare parts is crucial to improve the efficiency of equipment failure disposal and enhance the competitiveness of enterprises. However, the consumption of some maintenance spare parts is usually affected by a variety of factors, with strong randomness and uncertainty, and it is difficult to accurately predict them. In this paper, a multi-level migration learning CNN-ISE-Attention-BiLSTM prediction model for maintenance spare parts is designed through the study of neural networks, time series and attention mechanisms. Through several sets of experiments, it is proved that the model can better solve the problems of insufficient mining of key information due to insufficient data samples of maintenance spare parts consumption and insufficient mining of spare parts consumption law by ordinary neural networks, and better achieve the prediction of non-stationary maintenance spare parts, which is of great significance for the implementation of the scientific management of maintenance spare parts in enterprises.