Data-Driven Production Line Cycle Time Prediction Study
In order to solve the problems of uncontrollable time in the cycle time,complex data composi-tion and change of production line composition,a production line cycle time prediction method driven by heterogeneous data is proposed.Based on the membership function and the dynamic time warping,the het-erogeneous data fusion algorithm is established under the triangular characteristic configuration,in this way unify heterogeneous data under the same structure.The deep belief networks is used to establish the cycle time prediction model,solve the uncontrollable time composition of the cycle time by certain factors,com-municate the particle swarm optimization algorithm and the neural network through the improved equation,which could get neural network optimized iteratively every time and use the feedback to improve the parti-cle swarm optimization algorithm.Taking an aviation processing production line as an example,the opti-mized neural network accuracy increases by 9%,the particle swarm training time decreases by 5%,and the prediction model can predict the cycle time,also adapt to the changes of production line composition.
feature fusionparticle swarm optimizationdeep belief networkscycle time prediction
NETL
NSTL
万方数据
