Transactions of Nanjing University of Aeronautics & Astronautics
Lifelong Learning Based Material Delivery Time Prediction for Helicopter Assembly
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Abstract:
The lack of key materials has emerged as one of crucial factors affecting the execution of helicopter assembly production plans. Accurate material delivery time prediction can guide assembly production planning and reduce frequent changes caused by material shortages. A lifelong learning-based model for predicting delivery time of materials is proposed on the basis of internal data sharing within the helicopter factory. During real-time prediction， the model can store new memories quickly and not forget old ones， which is constructed by gated recurrent unit （GRU） network layer， ReLU activation layer， and fully connected layers. To prevent significant precision degradation in real-time prediction， a regularization parameter constraint method is proposed to adjust model parameters. By using this method， the root mean square error （RMSE） in the model’s prediction on the target domain data is reduced from 0.032 9 to 0.013 4. The accuracy and applicability of the model for real-time prediction in helicopter assembly is validated by comparing it with methods such as L2 regularization and EWC regularization， using 25 material orders.
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TP391
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