Paradigm shift for Industrie4.0 by Software

Copyright © 2016 NTT DATA Corporation

NTT DATA Corporation, Public Sector 1Masao Furuta

Paradigm shift for Industrie4.0 by Software~Realization of sophisticated analysis functions through Fog computing

2Copyright © 2013NTT DATA WAVE CorporationCopyright © 2016 NTT DATA Corporation

Agenda

1. Introduction

2. Need for sophistication of mechanical processing

3. Difference in Japan’s and Germany’s efforts, and NTT DATA

Corporation’s ideas

4. Concept of fog computing

5. Use cases

6. Our company’s IoT platform and system concept

7. Example of advanced data analysis

8. Use case of system

3Copyright © 2013 NTT DATA WAVE CorporationCopyright © 2016 NTT DATA Corporation

New York, USA

California, USA

Washington, USA

Location

1

2

3

4

IBM

Hewlett-Packard

Microsoft

Accenture

5

6

7

8

Dell, Inc

Cisco Systems, Inc.

Oracle Corporation

EMC

Company Name

9

10

11

12

Intel

NTT DATA

Thomson Reuters

Fujitsu

13

14

15

CapGemini

CSC

SAP SE

Dublin, Ireland

Texas, USA

California, USA

California, USA

Massachusetts, USA

California, USA

Tokyo, Japan

Massachusetts, USA

Tokyo, Japan

Paris, France

Virginia, USA

Walldorf, Germany

Source:

IDC Financial Insights Fin Tech Rankings Top25

Enterprise （Published: 16 September 2015)

■ Business Areas:

- System integration

- Networking system services

- Other business activities related

to the above

1.1 About NTT DATA Corporation (1/2)


Bases： 79 cities

Employees： approx. 15,000

EMEA

Bases： 27 cities


APAC

Bases： 57 cities


AmericasBases： 13 cities


China

Approx. 76,000 persons deployed in 42 countries and regions around the world.

1.1 About NTT DATA Corporation (2/2)

NTT DATA Deutschland GmbHZentrale München Zamdorfer Straße 120

81677 München, Germany


Airport area 空港エリア

Airport traffic

control center Air traffic flow management

Location

information

Terminal radar

information

processing

system

Flight information management

Area control centerArea control center

Flight route radar information processing

system

Control desk

Domestic flight routes areas

Flight route radar information processing

system

Control desk

Terminal

radar

information

processing

system

Marine control data

display system

Position report

Marine areasDomestic flight routes areas

A system for providing control support information such as information on flight schedules, flight routes and advanced control instructions, the positioning of aircrafts from the ground and from satellites, and weather, etc., to air traffic controllers.

Airport area

Provided by our company

1.2 Recent Overview of Business Activities

Air traffic control system


Digitization and long-term preservation business of valuable documents at the Vatican Library ・Entrusted with the digitization of historic documents by the Vatican Library

Botticelli’s illustration found in Dante’s

“Divine Comedy”Dante’s “Divine Comedy” drawn by the famous painter

Sandro Botticelli (masterpiece “The Birth of Venus”) for

the head of a noble Medici family in the 15th century.

Digital archiving business in the Vatican Library1.2 Recent Overview of Business Activities



(1) Immediate judgement of work results in each process

⇒We want to find defects in each process and remove them on-the-spot

⇒Finding defects through the final inspection process is inefficient

Process

1Process

2

Process

3Inspection

It is inefficient to find defects through the final inspection process

●Current

●After improvement

Process

1Process

2

Process

3Inspection

CHECK CHECK CHECK

Realized

through IoT

Slip through Slip through Slip through

Defective

product

Defective

product

Defective

product

Slip through Slip through Slip through

Defects Defects Defects



(2) Realizing zero down time by detecting signs

⇒Great losses occur when breakdowns happen. (Toyota loses 3M JPY if operations stop for 1 minute)

⇒If replacement parts have long delivery dates, there is a concern that additional losses will occur.

Break

down

DOWN TIMEReplacement

●Corrective maintenance

Sign detectionReplacement

●Preventive maintenance

！

Loss

occurs

Prevention Realization of Zero Down Time


3. Difference in Japan’s and Germany’s efforts, and NTT DATA Corporation’s ideas

Devices

Controller(control)

MES(on-site mgmt.)

ERP(business mgmt.)

Germany’s efforts

Top

down-

type (importance

placed on

strategy)Standard

Standard

Standard

Devices

Controller(control)

MES(on-site mgmt.)

ERP(business mgmt.)

Japan’s efforts

Improve

& devise

Improve

& devise

Improve

& devise

Bottom

up-

style (importance

placed on

operations)

Straight data

integration

Optimization by

processOur company’s

ideas

Ｉ４.０

KAIZEN

To combine I4.0

with Japan’s

analysis technology


4. Concept of fog computing

Advanced processing can be realized in real-time by controlling devices through

cooperation between on-site computers (Fog) and computers (Cloud) on the center side.

Sensor network Fog Cloud

Analysis and

judgement based on

rules

Data with volume

Diversified data

Highly frequent data Decisionon analysis

andthreshold value

Cooperation

Data and status of

machines and sensors

Threshold value, and

setting changes

Can immediately make analyses and judgements

since they are close to machines

Can make exact analyses in large

quantities since the computer’s

power resource is large


Sensor network(1) Decision

on normal &

abnormal

threshold

values

5. Use cases

(2) Decision

on

abnormality

Raw data

Feedback on threshold values

[Preparation phase]

[Operating phase]

Raw data

Abnormality?

Surveillance

through

monitoring data

Ｎo

ＹES

Failure prediction of production equipment are:(1) Analyzed in the cloud where threshold values are decided(2) Abnormalities are determined in the fog using threshold values(3) Machine operations are stopped when an abnormality is detected

Large amount

of data

Large amount

of data

Large amount

of data

Transmission of related data before

and after occurrence of abnormality

Fog Cloud

(3) Actions such as process

cancellation, etc. (immediate)


PLC

Device

PLC

Device

PLC

Device

Control LAN(PROFINET etc)

6. Our company’s IoT platform and system concept

Fog node

OPC

serverSCADA MES

Software for

KAIZEN

OPC-UA MQTT

Low frequency data for use in

system monitoring is handled on the

OPC side.

I4.0KAIZEN

IoT Platform

（ANYSENSE）

Software for

KAIZEN

IoT Platform

（ANYSENSE）

Data for monitoring (I4.0) and data for KAIZEN (analysis-use) are separated in the fog node.

Highly frequent data is analyzed through fog/cloud cooperation, and on-site processes become more sophisticated.

Highly frequent data for use in analyses is handled in the

fog/cloud.



Example of applying abnormal sound detection for diesel engines・Use sensors to collect sounds from diesel engines

・Use machine learning to automatically determine the range of normal sounds from collected data

・Detects abnormal sounds that cannot be detected under normal circumstances

→

分析

*The picture is an illustrated image

Degree of

abnormality

determination

Strength of noise

It’s possible to classify normal and abnormal sounds even when the

strength of noise is the same



Example of applying failure prediction for production equipment・Apply machine learning to the normal correlation pattern of multiple sensor data

・Display a list of candidate factors deemed as abnormalities

⇒Possible to quickly identify factors using the factor candidate list

⇒Possible to take countermeasures before production equipment breakdowns occur

！

Possible to detect signs

from correlate multiple senor data*The picture is an illustrated image



Upper limit

Lower limit

Water level

Pump OFF

Pump ON

Alarm

Example of application in overseas (Israel) water purification plant・Collect data operating conditions and the status of operations

・Apply machine learning to the normal correlation pattern of operations and operating conditions

・Detect hacking attempts from outside of the system, operational errors, equipment failures, etc.

Alarm automatically goes off when the

operating conditions of pumps and water level

changes differ from normal patterns


Maximum value

Ele

ctric curre

nt

valu

e

Sampling period


Example of application in automobile factory

Measures the electric current value of the machine tools’ main shafts in automobile factories and

implements a total inspection of defects in work processes.

←Sampling period→

Load

curre

nt

1st process

Average value－σ

Minimum wave pattern

Average value

Average value＋σ


Limit

curves

(2) Abnormalities were recorded

just before the drill bit broke

(3) 2 out of 4 bits

were broken


(sign detection through the electric current value of 4-axis drilling machines)

・Detects behavior that exceeds limit (line(2)) before tools are damaged ((3))

・Can prevent down time caused by damage if parts are replaced after limit values are exceeded


(1) Within the limit curves

during normal times


Real-life application of three whetstones on the same machine

1. Maximum, average, and minimum waveforms

2. Standard deviation σ + average, average, and average – σ waveforms

Whetstone A Whetstone B

（half-price of A)


(performance comparison of whetstones in the polishing process)・Verify the performance of whetstones through electric current values during the polishing process

・Greatly reduce costs by switching products to lower-cost and high quality product C.


Whetstone C

（half-price of A)

Current performance conditions No change from current

performanceBetter performance than

current performance


In the production equipment aftermarket field, the system coordinates with the cloud and fog to detect beforehand when parts need to be replaced, and allows for parts and workers to be arranged.

Parts supply

Distribution warehouseManagement center

8. Use case of system (example 1)

Analysis

system

Raw data

Threshold value

Fog

Parts replacement timing

replacement

operations

Analysis center

Production equipment


8. Use case of system (example 2)

Analysis

system

Integrated IoT DB

in factoryRaw

data

Standard value

Monitoring

data

Failure prediction

information

Quality

assessment

Quality

assessment

Quality

assessment

Failure

prediction

ERPQuality assessmentstandard calculation

PROFINET etc

Calculate quality assessment standards and uses that data for quality assessment.

In addition, the system also notifies MES of failure prediction information and enables the review, etc.

of production plans.

MES

Standard value Standard value

Copyright © 2016 NTT DATA CorporationThe names of companies, products, or services listed in this document are trademarks or registered trademarks of each relevant company.

Please direct inquiries to: ANYSENSESALES

[email protected]

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Paradigm shift for Industrie4.0 by Software