摘要随着生产线数字化、自动化水平的提高，产生了大量的反映运行过程机理及运行状态的数据, 这些数据为生产线性能分析和预测提供了可能。针对生产线的在制品数量、生产周期和生产效率性能指标，构建了一种面向时序数据的循环深度信念网络的生产线预测模型（Cycle – deep belife network，C–DBN）；针对传统基于SGD（Stochastic gradient descent）的训练方法存在的预测模型收敛速度慢以及精度低等问题，提出了基于AMM（Adam with momentum）算法的生产线性能预测模型训练方法；确定了面向时序数据的生产线性能预测模型训练流程。最终通过实例验证了生产线性能预测模型和训练方法的有效性。
Abstract：With the improvement of the digitalization and automation level of production line, a large number of data reflecting the operation process mechanism and operation state are produced, which provides the possibility for the analysis and prediction of production line performance. In this paper, a cycle–deep belife network (C–DBN) model is proposed for production line prediction based on the performance indexes of work in process, cycle time and throughput. Aiming at the problems of slow convergence and low accuracy of the prediction model in the traditional training method based on SGD (Stochastic gradient descent), a production line performance prediction model training method based on AMM (Adam with momentum) algorithm was proposed. The training flow of production line performance prediction model for time series data is determined. Finally, the effectiveness of the production line performance prediction model and training method is verified by an example.
张维，张少勋，吴燕，时思远. 一种面向时序数据的多品种小批量生产线性能预测模型研究[J]. 航空制造技术, 2022, 65(19): 30-36.
ZHANG Wei, ZHANG Shaoxun, WU Yan, SHI Siyuan. A Prediction Model of Multi-Variety and Small Batch Production Line Performance Based on Time Series Data[J]. Aeronautical Manufacturing Technology, 2022, 65(19): 30-36.
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