Reale Daten für Simulationen im digitalen Zwilling*/Real data for simulation in the digital twin -Study on how to record Profinet data and reproduce them in complex simulation environments

2019 ◽  
Vol 109 (09) ◽  
pp. 662-666
Author(s):  
M. Chemnitz ◽  
O. Heimann ◽  
A. Vick
Keyword(s):  
Twin Study ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Real Data ◽  
Control Data ◽  
Cause Analysis ◽  
Digital Twin ◽  
Root Cause ◽  
Complex Simulation ◽  
Complex Manufacturing Systems

Die hohen Anforderungen an moderne Fertigungssysteme erfordern leistungsfähige Engineering-Lösungen. Wie man die Identifikation von Fehlerursachen in komplexen Anlagen erleichtert, wurde in einer Machbarkeitsstudie des Fraunhofer IPK im Auftrag von Siemens DI FA untersucht. In der vorgestellten Lösung werden die Daten der Anlage auf Feldbusebene erfasst und in den digitalen Zwilling eingespeist. So kann das Verhalten der Komponenten taktgenau nachvollzogen werden. Dies elaubt einen tiefen Einblick in das System und unterstützt so bei der Fehlerbehebung.   Powerful engineering tools are required to keep modern production systems manageable. Siemens DI FA and the Fraunhofer IPK present a novel tool for root cause analysis within complex manufacturing systems. The solution combines a CAx plant model with control data recorded from the field bus. This creates a comprehensive digital twin, allowing to analyse past machine behavior with bus clock resolution.

scholarly journals Digital Twins for Simulation Systems

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2276-2279
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Operating Efficiency ◽  
Time Data ◽  
Actual System ◽  
Digital Twin ◽  
Simulation Techniques ◽  
Real Time Data ◽  
Physical Assets ◽  
Complex Manufacturing Systems

Complex Manufacturing Systems can be better engineered with simulation techniques Relaying only on physical system to collect real world and capturing models is out of date. Moreover, a complex system has recursive model design, which leads to consume more time and more maintenance, unplanned down times and poor operating efficiency. The New Industry 4.0, digital twin creates virtual mirror of actual system. Here we have demonstrated digital twinning of UAV(Parrot ARDrone 2.0). Digital twin is virtual replica of physical assets and it can be simulated with real time data using industrial Internet of Things. Simulation with real time data improves operating efficiency, reduces unplanned down time hence increased revenue to manufactures

scholarly journals Approaches for the Simulation of Deformable Objects in Manufacturing Systems

2013 ◽  
Vol 1 (1) ◽  
pp. 68-74
Author(s):  
Gunther Reinhart ◽  
Peter Stich
Keyword(s):  
Manufacturing Systems ◽  
Production Systems ◽  
Simulation Models ◽  
Deformable Objects ◽  
Control Software ◽  
Complex Simulation ◽  
Virtual Commissioning ◽  
Physically Based ◽  
Rigid Body Simulation ◽  
Multi Body

The validation of control software using methods of Virtual Commissioning (VC), with its origin in the field of machine tools, gains more and more importance in other application areas like process engineering or material-flow-intensive production systems. Especially because of the increasing complexity of technical systems the validation of the control software quality is a major challenge in production technology. To reduce the efforts of modeling and to increase the value of simulation results, a so-called physically model is integrated in the VC. Currently the physically based Virtual Commissioning is restricted to rigid body simulation objects. In this publication new methods for the simulation of deformable objects are shown and validated in an industrial context. Therefore the hybridization of existing simulation methods from computer science using so called physic engines is introduced as a method that simplifies the description of complex simulation objects by adapting well known simulation models. The new approach is comparable to a mixture of a multi body simulation and a real-time finite element simulation.

A MANUFACTURING FAILURE ROOT CAUSE ANALYSIS IN IMBALANCE DATA SET USING PCA WEIGHTED ASSOCIATION RULE MINING

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Phaik-Ling Ong ◽  
Yun-Huoy Choo ◽  
Azah Kamilah Muda
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Manufacturing Systems ◽  
Principal Component ◽  
Root Cause Analysis ◽  
Rule Mining ◽  
Data Set ◽  
Cause Analysis ◽  
Root Cause ◽  
Selection Of

Root cause analysis is key issue for manufacturing processes. It has been a very challenging problem due to the increasing level of complexity and huge number of operational aspects in manufacturing systems. Association rule mining (ARM) which aids in root cause analysis was introduced to extract interesting correlations, frequent patterns, associations or casual structures among items in the transactional database. Although ARM was proven outstanding in many application domains, not many researches were focusing on solving rare items problem in imbalance dataset. The existence of imbalanced dataset in manufacturing environment make the classical ARM fails to extract interesting pattern in an efficient way. Weighted association rule mining (WARM) overcomes the rare items problem by assigning weights to items. The goal of using weighted support is to make use of the weight in the mining process and priorities the selection of the selection of targeted itemsets according to their significance, rather than frequency alone. However, the development of a suitable weight assignment scheme has been an important issue. In this research, we proposed principal component analysis (PCA) to automate the weight in WARM. The result shows that PCA-WARM is capable in capturing pattern from the data of industrial process. These patterns are proven able to explain industrial failure.

Root Cause analysis of SG tube ODSCC indications within the tube sheets of NPP biblis unit A

2011 ◽  
pp. 78-86
Author(s):  
R. Kilian ◽  
J. Beck ◽  
H. Lang ◽  
V. Schneider ◽  
T. Schönherr ◽  
...  
Keyword(s):  
Root Cause Analysis ◽  
Cause Analysis ◽  
Root Cause
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