用LSTM从传感器数据学习生产线传送系统的自动机模型。
Learning Automata of PLCs in Production Lines Using LSTM
- 用LSTM捕捉传送系统的时间依赖关系，生成状态自动机。
- 相比OTALA方法，LSTM生成的自动机更准确地反映实际系统行为。
- 适合对制造系统建模与自动化诊断感兴趣的工程师和研究者。
现代制造业中的生产线与传送系统是核心环节，其效率直接影响生产效能。由于现代制造标准的复杂性，工业场景建模始终面临挑战。本文针对一个简单的气动传送系统（运输木块）应用长短期记忆网络（LSTM），该网络通过门控机制有效学习长期时间依赖关系。利用传感器采集的数据训练LSTM，输出一个能描述传送系统行为的自动机模型。将该模型与基于OTALA方法得到的自动机进行比较，结果表明，本方法生成的自动机在准确性上优于OTALA，能够更真实地刻画实际传送系统的动态行为，适用于大规模生产线的简化建模。
原文摘要 · Abstract (English)
Production Lines and Conveying Systems are the staple of modern manufacturing processes. Manufacturing efficiency is directly related to the efficiency of the means of production and conveying. Modelling in the industrial context has always been a challenge due to the complexity that comes along with modern manufacturing standards. Long Short-Term Memory is a pattern recognition Recurrent Neural Network, that is utilised on a simple pneumatic conveying system which transports a wooden block around the system. Recurrent Neural Networks (RNNs) capture temporal dependencies through feedback loops, while Long Short-Term Memory (LSTM) networks enhance this capability by using gated mechanisms to effectively learn long-term dependencies. Conveying systems, representing a major component of production lines, are chosen as the target to model to present an approach applicable in large scale production lines in a simpler format. In this paper data from sensors are used to train the LSTM in order to output an Automaton that models the conveying system. The automaton obtained from the proposed LSTM approach is compared with the automaton obtained from OTALA. The resultant LSTM automaton proves to be a more accurate representation of the conveying system, unlike the one obtained from OTALA.
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