Learning Automata of PLCs in Production Lines Using LSTM

Abstract

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.