a modular and low-cost infrastructure for industrie 4.0 ... 4.0 creates what has been called the...

A Modular and Low-Cost Infrastructure for Industrie 4.0

Applications – Linking Real-Time Data to the Cloud

T. Ortmaier, I. Maurer, M. Riva, C. HansenInstitute of Mechatronic Systems (imes)Leibniz Universität Hannover

Appelstraße 11 A30167 Hannover

E-Mail: [email protected]: www.imes.uni-hannover.deTelefon: +49 511 762 4179

Seite 2

Structure A Modular and Low-Cost Infrastructure for Industrie 4.0 Applications

A Modular and Low-Cost Infrastructure for Industrie 4.0 Applications – Linking Real-Time Data to the Cloud

Introduction

Concept

Infrastructure Architecture

Model Factory

Exemplary Use-Cases

Summary

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… in the Context of Industry 4.0

Industry 4.0 creates what has been called the "smart factory“, containing:

data integration of all involved components (variety),

real-time data acquisition (velocity),

analysis of large volume data sets (volume).

and enabling several goals and methods:

real-time condition monitoring,

predictive maintenance,

energy management,

process optimization,

combining identification and parameterization methods,

and many more.

This requires an infrastructure to collect, store, and process (big) data in real-time:

under development in the new research group Integrated Systems & Machine Learning,

tested on the fully automated handling process in a laboratory model factory.

Institute of Mechatronic Systems (imes)


Big Data[1]

Apps

Infrastructure

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Automation PyramidConcept


MES

ERP

SPS

SCADA

Manufacturing / production process

Sensors / actuators

Enterprise levelProduction planning

Plant management levelProduction control,operational data management

(Process-) control levelHuman Ressource Interface

Operation levelMachine and plant control

Field levelIn- / output signals

Process level

Company Server

Operating- andMonitoringsystems

Programmable LogicController (PLC)

Servo drives,Field devices

Automatedproduction plant

Operation datamanagement

Main data flow(Process data)

Processed / evaluated data

Micro-controller

Private cloud

Own representation based on [2]

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Design Goals and AppsConcept


Transparency

Monitoring

Low-costsolution

Availabilityincrease

Predictivemaintenance

Modularity / Scalability

Data security

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Conceptual SettingInfrastructure Architecture


Plant / Process data Set points, Sensor values etc.

Embedded System Data Acquisition, Preprocessing, Filtering, Anonymisation etc.

Data Analytics Platform Storage, Management, Data Analysis, Modeling, Optimization etc.

Interactive Web Services Visualization (GUI) Parameterization

Wide range of possibleanalysis methods Robotics Energy, System Identification, Structural Health, Plant Performance, Economic Efficiency, etc.

Trigger alerts, optimize processes Save results, parameterization

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Server ArchitectureInfrastructure Architecture


Dashboard

Development

Controlling

Emb

edd

ed S

yste

m

HTTP(S)-Streaming

OPC UA

HTTP(S)

HTTP(S)

HiveSQL

HDFSStorage

HBaseNoSQL

Real Time AnalyticsStorm

HTTP(S)

HTTP(S) / OPC UA

„real-time“ publish–subscribe

messaging system[4]

Can handle terabytes of data without performance impact [5]

Save and backup of large volume of process data [3]

Low latency

High performance[3]

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Model Factory


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StructureModel factory


- PLC 3200C- Brake resistor- 4 servo axes

- PLC 3200C- Brake resistor- 4 + 2 servo axes

- PLC 3200C- Brake resistor- 6 + 1 servo axes

- PLC 3200C- Brake resistor- 3 servo axes

Stacker crane6-Axis-Robot + linear axisSCARA Delta Robot & Belts

Higher-level control / logic system: p500

Programminginterface

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Two Exemplary Use-CasesExemplary Use-Cases


„Energy Monitoring / Process optimization“

Application: model factory Delta robot (4 axis) Stacker crane (3 axis) 2 conveyor belts

Goals: Quantification of energy consumption

(on module / component basis) Identification of potentials to increase efficiency

and optimize processes

Challenge: Synchronize and analyze data of different sources

/ components Processing and storing large amount of „real-

time“ (1kHz) process data

„Condition Monitoring / Predictive Maintenance“

Application: stacker crane 3 axis

Goals: Diagnosis / system condition monitoring Anomaly detection and classification

(e.g. based on bearing damage or friction)

Challenge : Modelling based on process data (data science methods) Online anomaly detection and classification of „real-time“

(1kHz) process data-streams

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Energy-Monitoring / Process OptimizationExemplary Use-Cases


Analysis: Energy-Monitoring / -Management[6,7,8]

Quantification of energy consumption of plant and individual components

Detection of excessive energy losses and peak loads

Development of generic approaches to use for any type of robot

Goals: Energy minimization through process optimization

Identification of potentials to increase efficiency

Assessment of energy demands for alternative drive components or energy supply concepts (e.g. intermediate circuit)

Optimization:

Energy optimal motion planning / task synchronisation

Recommendation for action (DC networking, recovery, energy storage, component replacement, …)

power

time

Stacker crane

Delta robot

Belt 1

Belt 2

Resolution: up to 1kHz

timetimetime

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Condition Monitoring / Predictive MaintenanceExemplary Use-Cases


Condition Monitoring:

Real-time analysis and diagnosis

Monitoring as a decision-making basis for component replacement / error handling

Methods:

Principal component analysis (PCA)

k-Nearest-Neighbor (kNN)

Trainings-data sets without errors

with errors (only for classification)

Detection of anomalies versus training data

Classification of errors (e. g. belt slippage)

Optimization:

Switching of control trategies (e.g. emergency stop)

Predictive component replacement before damageoccurs

Controller needs >2s forbelt slippage detection New: belt slippage detection in 0,5s

Example 2: slippage (fast) Example 3: slippage (slow)Example 1: no slippage

belt slippage

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Summary

imes is researching the topic of Industrie 4.0 in industrial applications, including:

Development and implementation of a modern big data-infrastructure:

real-time data aggregation,

data preprocessing,

data handling for big data-applications, and

visualization / controlling methods.

Research of data science / machine learning methods for process / plant data analysis, e. g.

Energy monitoring,

Process optimization,

Condition monitoring etc.

Focus on online / real-time process and plant data-stream analysis.

Industrie 4.0 Applications – Linking Real-Time Data to the Cloud


Thank you for your attention.

T. Ortmaier, I. Maurer, M. Riva, C. HansenInstitut of Mechatronic Systems (imes)

Leibniz Universität Hannover

Appelstraße 11 A30167 Hannover, Germany

mail: [email protected]: www.imes.uni-hannover.de

phone: +49 (0)511 - 762 - 4179

Seite 15

References

Literature:

[1] Mayer-Schönberger V. and Cukier K. (2013), "Big data: A revolution that will transform how we live, work, and think" Houghton Mifflin Harcourt.

[2] VDI/VDE-Gesellschaft Mess und Automatisierungstechnik (GMA). "Cyber-Physical Systems: Chancen und Nutzen aus Sicht der Automation". Thesen und Handlungsfelder, April 2013.

[3] Shvachko K., Kuang H., Radia S. and Chansler R. (2010), "The Hadoop Distributed File System", In 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)., May, 2010. , pp. 1-10.

[4] Kreps, J., Narkhede, N., & Rao, J. (2011). "Kafka: A distributed messaging system for log processing". In Proceedings of the NetDB (pp. 1-7).

[5] Aydin G., Hallac I.R. and Karakus B. (2015), "Architecture and implementation of a scalable sensor data storage and analysis system using cloud computing and big data technologies", Journal of Sensors. Vol. 2015 Hindawi Publishing Corporation.

[6] Hansen C., Öltjen J., Meike D. and Ortmaier T. (2012), "Enhanced Approach for Energy-Efficient Trajectory Generation of Industrial Robots", Proceedings of the 2012 IEEE International Conference on Automation Science and Engineering.

[7] Hansen C., Kotlarski J. and Ortmaier T. (2014), "Optimal motion planning for energy efficient multiaxis applications", International Journal ofMechatronics and Automation.

[8] Hansen C., Eggers K., Kotlarski J. and Ortmaier T. (2015), "Concurrent Energy Efficiency Optimization of Multi-Axis Positioning Tasks", The 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015).

Figures:

KUKA AG, Lenze SE, Dell Technologies Inc., Apache Software Foundation, GINO AG

A Modular and Low-Cost Infrastructure for Industrie 4.0 Applications


a modular and low-cost infrastructure for industrie 4.0 ... 4.0 creates what has been called the...

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