Cloud-based Control Approach in Discrete Manufacturing Using a Self-Learning Architecture

被引：12

作者：

Lindemann, Benjamin ^{[1
]}

Karadogan, Celalettin ^{[2
]}

Jazdi, Nasser ^{[1
]}

Liewald, Mathias ^{[2
]}

Weyrich, Michael ^{[1
]}

机构：

[1] Univ Stuttgart, Inst Ind Automat & Software Engn, D-70569 Stuttgart, Germany

[2] Univ Stuttgart, Inst Met Forming Technol, D-70174 Stuttgart, Germany

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 10期

关键词：

Data fusion and data mining; Process control; manufacturing; Intelligent systems and instrumentation (smart systems; sensors; actuators and distributed systems); MODEL;

D O I：

10.1016/j.ifacol.2018.06.255

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Process anomalies and fluctuations in product quality are widespread problems in discrete manufacturing. There have been various control approaches to tackle the challenge. This paper presents a cross-process control approach that combines engineering knowledge and data analytics techniques. An initial rule basis is generated by experts using simulation models. To achieve a data driven enhancement concerning process and product quality, a PLC-based connector is developed to record and unify real process data from heterogeneous data sources. The data is processed in the cloud and inferred using online modeling techniques. Neural networks with autoencoder structure are applied to extract unknown features, to iteratively refine the knowledge base and thus to optimize quality control. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：163 / 168

页数：6

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