关键词: 织机了机, LSTM循环神经网络, 时间序列, 经纬向停车, 织轴

Abstract: Due to the different production conditions and impact parameters, looms have great differences in the actual weaving efficiency and loom warp-out times from each other. Aiming to solve the problem that the deviation between the theoretical calculation value and the actual value of loom warp-out time is too large when calculating it by using the pre-set static parameters of planned production, a loom warp-out time prediction method based on LSTM (Long Short Term Memory)  recurrent neural network is proposed. Based on the analysis of the factors affecting the loom warp-out time from three aspects: the warp and weft stop of the loom, the working efficiency of personnel and the variety of cloth processed, a data set of loom production with time series characteristics is constructed. The prediction of the model in the whole life cycle of the weaving axis is dynamically adjusted by setting the time schedule coefficient, and the performance of the model is optimized from the two aspects of loss degree and training time. Finally, eight groups of experimental data are used to verify the reliability of the model. The experimental results show that when it is 30 hours to 6 hours before the warp-out time of machine prediction, the average error range between the predicted value of the model and the actual value is 0.84 h to 1.52 h, which meets the requirements of actual production.

Key words:  , loom warp-out time, LSTM recurrent neural network, time series, warp and weft stop, weaving axis