Proactive Fault Management by Big Data Usage Data Science: Theory and Application RWTH Aachen University - ICT cubes, October 26, 2015 Gregor Fuhs M.Sc

Proactive Fault Management by Big Data Usage

Data Science: Theory and ApplicationRWTH Aachen University - ICT cubes, October 26, 2015Gregor Fuhs M.Sc. RWTH© FIR 2015 Gregor Fuhs, FIR an der RWTH Aachen RWTH Aachen University

| 26.10.2015

© FIR 2015 Gregor Fuhs, FIR an der RWTH Aachen Bitkom AK Big Data | 25.09.2015

Motivation and Basics

© FIR 2015 Gregor Fuhs, FIR an der RWTH Aachen RWTH Aachen University | 26.10.2015

So, why do you do it with your data?

You never would do that!

© FIR 2015 Gregor Fuhs, FIR an der RWTH Aachen Bitkom AK Big Data | 25.09.2015


Traffic DataGPS-Data

Data is generated everywhere – Why shouldn’t we use it?

Smartphone Data

Camera Data

Customer Data


Big Data and Smart Data Ansatz


SizeVolume Velocity Variety Veracity

SpeedStructuredness

defectiveData

fastData Generation

Different Formats

Complexity

Machine Learning

Cyber Physical Systems

Cloud

Webonline

Sensor Data

Google

IBM

Facebook

Twitter

Smartphone

Un

stru

ctu

red

nes

s

GP

S

RF

ID

Hypervisor

Web Server

E-Mail

Messaging

Clickstreams

Databases

Sensory

Telematics

Security Devices

Map Reduce

Hadoop

Rea

l T

ime

An

alys

is

NSA

SE

TI

LH

C

NASA NCCS

DN

A

Am

azo

n

Internet of Things

Structure

Petabyte Zettabyte

Information

Search Science

Su

pp

ort

Governance

Management

Lo

gs

„640 kB ought to be enough for anybody. “ 1981, Bill Gates (disclaimed)

Bill Gates for GeoengineeringMicrosoftLet's talk about cloud ships

BIG DATA


What is Big Data?

Data GenerationWhere comes the data from?

Data StorageHow is Data stored?

Data AnalysisHow to analyze Big Data?


How differs Smart Data?

Data GenerationWhere comes the Data from?

Data StorageHow is Data stored?

Data AnalysisHow to analyze Big Data?

Data GenerationData Storage

Data is stored by Categories

Data AnalysisOnly relevant Data is analyzed

Data CategorizationData is categorized by certain

Criteria


Application of Big Data Methods in BigPro


Research Project BigPro accesses practical problems of Data- and Fault Management


11

The work packages are aligned with the target picture of the project

Datageneration

Data analysis/-monitoring

Pattern recognition/

prognosisCounter

measures

Real-time Big Data-Platform for failure managementD

ata

fro

m p

rod

uct

ion

, th

e en

viro

nm

ent

and

fr

om

sta

ff

User-o

riented

V

isualizatio

n

Mood-MonitoringHuman meta data

Machine-/ Process-Sensors Complex Online Optimization

Data linking

CEPMeasurementsgeneration

ScalableVisualization

Pattern-Management

Data generation

Data aggregation Information flow

material

Sentiment-Analysis

WP 1Requirements analyzis

WP 2Design / realization Data platform

WP 5Scalable visualization

WP 6Testing phaseDFA Aachen GmbH

WP 3Monitoring and prognosis

WP 4Failure management

WP 7Testing phaseC. Grossmann GmbH

WP 8Testing phaseRobert Bosch GmbH

Picture: own source

Reactive Pattern

Proactive Pattern

Event Producer

Event Consumer

Historical Data

Engine

Predictive Pattern Generator

Source: FZI Forschungszentrum Informatik

80% in next 10 Minutes

Pattern DB

Proactive Event-Driven Computing


RFID-based Tracing

6 weeks Lead Time

ERP: TimeLineMDE/BDE


Facility-Sensors with 10.000 Data Generators

MES / MDE / BDE

Several hundreds machines,

24/7 operation

process, context-, machine data, …


Assembly e.GO KART

Pick-by-Voicecommissioning

Process deviations


User specific Warning before Fault occurring

Pattern Detectionin BigPro Platform

Warnings will be directly given to the mechanic or production manager

Via „Thinking Aloud“ and RTLS Tracking process times will be recorded and submitted to the system, such that faults can be assigned to the respective process step.

Probability of Faults are calculated by the generated Data Patterns

Verbal Communication

Automatic Process time Tracing

Strain Monitoring


outlook


SkillsMachine Park and Production skillsExperience & Knowledge

Company

Customer ConfigurationMaintenanceChange/ExpansionCondition Monitoring

Product Live Cycle

PreproductionMachine configuration Product configurationSensor data

Serial productionSensor dataDefect analysisCIP

Production

Draft

Elaborate

Response Data:Improved Visualization and Detection of: Business skillsResponse/Problems in ProductionResponse from Customerby Digitalisation.

Live-Information-Provision:Direct Depiction of Impacts of Changes in Time, Quality and Costs.

Planning

Product Design

Impact Calculator

► Live-Description of Impacts of Decisions► Task-specific View

(reduction on relevant Information)

Improvement of Product Design by Big Data and Smart Data Methods

Design


Data Source 1

VISTEKProviding required data infrastructure and connecting datasets

DFA AachenProviding data from production process / environment for analysis

DataStorageLayer

DataAnalysisLayer

Production,Sensor andData ProvidingLayer

SabanciDeveloping problem-oriented data algorithms

Data Source 2

FIRProcess-based data requirements analysis and anonymization

Data filterCategorization/ Anonymization

Key Data flow

Material flow

Algorithm 1

Algorithm 2

Data Connection

Connected and analyzed Data flow

Production step 2

Production Step 1

ProMaQ – Data Anonymization for Outsourcing Data Analysis


SensorFusion – innovative Combination of measured Data and Sensors for a Predictive Maintenance Scenario

Sensor Fusion

[K]

[Hz]

Measurement a

Time

Measurement parameter c

Predictive Reactive

Maintenance

Machine Data

Extra Sensor

Easy SensorUpgrade

Plug&Play-Data combination and -aggregation

Predictive Maintenance by Pattern Recognition

Measurement b

Research Objective 1 Research Objective 2 Research Objective 3


Thank you for your attention!


Documents

Proactive Fault Management by Big Data Usage Data Science: Theory and Application RWTH Aachen University - ICT cubes, October 26, 2015 Gregor Fuhs M.Sc