Denno Peter, Dickerson Charles, Harding Jennifer Anne
National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Loughborough University, Loughborough, UK.
J Manuf Syst. 2018;48. doi: 10.1016/j.jmsy.2018.04.006.
This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system.
本文提出了一种名为生产系统识别的方法，用于从制造系统的运行日志中生成该系统的模型。所生成的模型旨在辅助制定生产调度决策。生产系统识别与过程挖掘的机器学习方法类似，因为它们都使用操作日志。然而，过程挖掘未能满足一些重要要求；过程挖掘无法（1）考虑到可能有助于洞察系统能力和可靠性的罕见异常事件，（2）提供相对于对原因的理解来验证模型的方法，以及（3）随着生产车间情况的变化更新模型。本文描述了一种遗传编程（GP）方法，该方法使用Petri网、概率神经网络以及生产系统动态的因果模型来解决这些缺点。开发了一种适用于GP的有色Petri网形式化方法，并用于解释日志。解释后的日志提供了Petri网状态与异常系统状态之间的关系，这种关系可以通过概率神经网络（PNN）的新颖公式来学习。一个广义随机Petri网和PNN用于验证GP生成的解决方案。通过一个基于汽车装配系统的示例对该方法进行了评估。
