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METHOD AND SYSTEM FOR PREDICTING A PRODUCTION LINE STANDSTILL TIME FOR AN INDUSTRIAL AUTOMATION ARRANGEMENT

机译:用于预测工业自动化装置的生产线静止时间的方法和系统

摘要

The invention discloses a method and a system for providing a predicted time interval before the standstill of a production line. This task is solved by using a machine learning approach. The approach consists of several steps. In a first step, information on states or change of states of aggregates (Agg.1,..., Agg.N) of a production line is continuously collected. In a second step, the information on the aggregate (RC Agg.) which is the root cause for a bottleneck aggregate (BN Agg.) standstill is determined and the information on the time interval from the start of the root cause aggregate (RC Agg.) fault to the bottleneck aggregate (BN Agg.) standstill is obtained. In a third step, a dataset for the machine learning algorithm is prepared from this information. In a fourth step, the chosen machine learning algorithm is trained with the prepared datasets. Finally, in a fifth step the time to a bottleneck aggregate (BN Agg.) standstill with a trained machine learning algorithm utilizing newly collected data from the first step with a preprocessing described in the third step is predicted.
机译:本发明公开了一种在生产线停止前提供预测时间间隔的方法和系统。这项任务是通过使用机器学习方法来解决的。该方法由几个步骤组成。在第一步中,连续收集关于生产线骨料(Agg.1,…,Agg.N)状态或状态变化的信息。在第二步中,聚合上的信息(RC Agg.)这是瓶颈聚合(BN Agg)的根本原因确定停止状态,并显示从根本原因汇总(RC Agg)开始的时间间隔信息瓶颈聚合故障(BN Agg.)获得静止状态。在第三步中,根据该信息准备机器学习算法的数据集。在第四步中,使用准备好的数据集对所选择的机器学习算法进行训练。最后,在第五步中,是瓶颈聚合(BN Agg)的时间预测了利用第一步新收集的数据和第三步中描述的预处理的经过训练的机器学习算法的停滞。

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