Innovative Applications of

Industrial Big Data

K2Data Technology (Beijing) CO., Ltd

Innovation Center for Industrial Big Data

Chen Chen

Agenda

• Introduction to K2Data & IIBD

• Industrial Big Data is the crucial technological element of new industrial revolution

• Industrial Big Data real world cases

K2Data： Drive the Intelligent Upgrade of Chinese Industry with Data

The Big Data Company that Understands Industry Best

Resonate with“Made in China 2025”

Industrial Leader

Founded by Data & Industry Experts from IBM, Huawei, Siemens and THU, having rich global practical experiences and successful cases.

Industrial Big Data Pioneer, leading the development of industry, together with leading companies in fields such as new source of energy, petrochemical, high-end equipment manufacturing, electronics manufacturing and environmental protection.

Leading the draw up of “Made in China 2025” Key Area Technology Roadmap, initiate and operate Innovation Center for Industrial Big Data network, serving the manufacturing industry cluster professionally.

New generation information technology

Aeronautic and AstronauticEquipment

Advanced Rail Transportation

Equipment

Electrical Equipment

agricultural machinery

new materials

National manufacturing

innovation center

Green manufacturing

High-end equipment innovation

5 Major Projects

Operating System and Industrial Software：

“Cloud+Client Industrial Big Data Platform”

Coordinative manufacturing cloud

Embedded operating system

Industrial software

10 Fields

In response to“Made In China 2025”strategy，Innovation Center for Industrial Big Data is founded

· Build independent Industrial Data · Software Platform· Drive the intelligent upgrade for Chinese · Manufacturing Industry· Construct national Industrial Big Data· Innovation Base

Our Central Duty

Bio-pharmaceutical and high performan-ce medical apparatus and instruments

High-end numerical control machine and

robots

Ocean Engineering equipment and high-

tech ships

Energy saving and new energy vehicles

National manufacturing innovation center :

The future-oriented projects which focus on thefundamental research and industrialization of 10key fields，will build a collection of “Industry-Academia-Research ” combined manufacturinginnovation centers.

International Collaboration

Core Technology breakthrough

Talents cultivation

Application Promote

Industry incubation

Standerazation

Intelligent manufacturing

strengthen industrial development at the

root level

Innovation Center for Industrial Big Data, Beijing

syncretize across industry boarders

innovate in coordination

forming a network topology

Members Innovation Center for Industrial Big Data, Suzhou

Agenda

• Introduction to K2Data & IIBD

• Industrial Big Data is the crucial technological element of new industrial revolution

• Industrial Big Data real world cases

Gartner2012 - The “bitter smile curve” of manufacturing

The need for breaking through industrial bottleneck and improving technology brings the industrial revolution which sweeps the whole world

From ”Industry 1.0” to “Industry 4.0”

Facing pressure Continuously declining

Stay stable Global rising star

Those economies with low costs, their competitiveness are weakening

for various reasons

Those economies withe high costs, their competitivenessare weakening due to the slow growth of productivityand rising cost of energy.

These economies stay relatively stable compared to global leaders in

competitiveness

More competitive compared with other economies due tothe moderate growth of wages, increasing productivity,stable currency exchange rate and advantage in energy cost

Brazil China czech republic

Poland Russia

Australia Belgium Franch

Italy Sweden Switzerland

IndiaIndonesiaUK Holland Mexico USA

01 02

03 04

Figure：in global manufacturing cost-competitiveness index, most economies can be classified into four significant change patterns above

source： Boston Consulting Group Analytics

“Made in China”enters second half in worldwide competition: from low-cost expansion to relying on innovation

“Made in China”is facing unprecedented challenges

German Industry 4.0 American Industrial InternetMade In China 2025

The integrated analysis and utilization of data is the crucial capability

The advanced capability of analyzing is the key element

Drive the revolution of traditional industry by new generation information technology has become the common choice worldwide: by digitizing, networkingand intellegentizing, to propel the transformation and upgrade of manufacturing, so as to provide more adaptivity and flexibility that supply requires from demand, to generate new source of growth.

Big countries in manufacturing introduced strategies in succession, based on their conditions

Automated optimization on scheduling

Defected product rate root causes analysis and yields improvement

production factors in new industrial era

production equipment and products

Automatic control system

Information management system

Intelligent analysis and optimization system -“Industrial Brain”

SCADA

PLC

DCS

CAx

PLM

MESERP

Energy consumption optimization

Fault prediction

Dangeralert, prevention and control

MRO

The essence of new industrial revolution is, with the mergence of informatization and industrialization, Big Data and AI are key technological elements.

Consensus：Big Data and AI are key technological elements to new industrial revolution

Trend of Big Data technology development

Big Data is migrating from consumer internet to Industrial Internet

Internet Data

web data, social data,electronic business data

Insustry Data

time series data, procedure data,scientific data, non-structural data

Composite geek

analysis, programming, domain knowledgedatabase, distributed computing

Industry domain Talents

domain knowledgelimited computer skills

Big Data that integrates developments

Autonomous Controllable

Industry4.0

Model agriculture

Publicservices

Government governance

Web search

E-Commerce

Industry from Data to Big Data

Machinery data

Cross-industry-border data chain

Industrial digitizing data

environmentmeteorology geography

blueprints

videosmodels

documents

Industrial Big Data

Characteristics of Industrial Big Data

High-end manufacturing big data, represented by various types of unstructured engineering

data, process and BOM data, and high-end equipment monitoring time series data, shows

“multimodal, high throughput, strong correlation”features

Various data modals, complex structures and relations

Typical high-end manufacturing enterprise can have up to 300 kinds of data types

Turbine can have ~350,000 parts data

Large data throughput50Hz，500 data points /station，20k

wind turbinesAs high as tens of millions data

points/s

Many collaborative professions are needed

More than 200 professions in aircraft related R&D

生命中期

生命初期

模型层

生命中期

模型层

制造BOM

设计BOM

维修需求

维修策略

维修规程

概念设计

详细设计

仿真分析

产品配置

试验数据

设计需求

使用规范

制造工艺

工装设备

工艺仿真

制造质量

工厂布局

调试报告

包装运输

装配试验

核心层

中性BOM

服务保

障模型

制造BOM

关联模型 实例运行追溯

模型

实例BOM

关联

模型

维修策略建模语言

中性BOM建模规范维修视频

故障记录

保障流程

试验报告

装备履历

异常报告

巡检记录

生命特征

运行状态

维修计划

历史记录

维修变更

备品备件

服务评价

实例BOM1

实例BOMn

实例BOM3

实例BOM2

…

生命初期热流体

飞机CAD模型

材料模型

行为模型

边界条件

网格化

热学求解器

温度场

结构分析机翼CAD模型

材料模型

行为模型

边界条件

网格化

强度求解器

强度场

流体飞机CAD模型

材料模型

行为模型

边界条件

网格化

流体求解器

压力场

电磁飞机CAD模型

材料模型

行为模型

边界条件

网格化

电磁求解器

电磁场

研发大数据

网格化几何拓扑

结构产品 材料

有限元分析 动态模型 其它

结果

声学飞机CAD模型

材料模型

行为模型

边界条件

网格化

声学求解器

声学场

多学科异构数据信息交互模型多学科异构数据信息交互模型

Massive & high-speed

Generated by machines 24/7

at high sampling frequency, large amount of data

Characteristics of Industrial Big Data

Research object

Existing foundations

New driving force

Expectations of analysis

Focus on Physical entities and the environment

Internet-supported interaction

Medium / micro-mechanism model and quantitative knowledge of the field, It is difficult to move forward on the current basis

Macro concept and qualitative understanding, vast room for improvement

New perception technology, Product service transformation

Usability based on CausalityHigh reliability of the model (Difficult to accept probabilistic predictions)

New interactive channels (Such as social media)

Helpful in relevant caseslaw of large numbers

Industrial Big Data Business Big Data

Industrial enterprises are faced with challenge that while using big data

Besides using the platform to solve technical challenges, companies also need to address

a number of management challenges to ensure that the value of the data is truly reflected.

• Don’t know how to start, how to combine big data with the business of their own

• With traditional IT system implementation method, the startup cost is high, the effect is slow, and the ROI is not clear

• Each industrial application area has its own unique domain knowledgesand mechanisms, which requires mechanism + data. And there‘s no universal solution

• Low level in informationalizationand industrialization, no data, data not gathered, data incomplete

• Lack of big data talent, no ability to manage big data systems and mining data value

management challenges technical challenges

• The realization of big data value need to cooperate with sensors, smart devices, automation systems. The closed-loop is quite long.

• Involve business philosophy and management process changes

Implement-ation path

Input-output

Systematic Engineering

Data Basis

technical capabilities

Professional barrier

Industrial big data business implementation

Business

driven

What is the overall business

objective?

How to creative intelligent &

ideal business processes?

How to map data flow to

business flow?

How to sync, exchange, associate,

integrate data?

How to assure data quality?

How to save, manage and use data?

What are the features the data,

and how much data?

What and where are data from?

H

O

W

？Data

driven

• Manufacturing full lifecycle business innovation (advanced manufacturing): With the innovation of big data-driven product design, intelligent manufacturing and intelligent services, we can achieve the goal of "improving quality, increasing efficiency, reducing consumption and controlling risks" purpose.

• Industry Internet New Business Innovation (Manufacturing + Internet): Industrial internet services that support the peripheral ecosystem of service products, aiming to create emerging markets and business models based on intelligent networked industrial products.

design

manufacture

Circulation

operate

Re-manufacture

financing

leasing

Service contract

situational social

Peripheral E-commerce

Industrial Big Data

advanced manufacturing Internet+

Manufacturing servitization

transformation cases：

agriculture-> agriculture service

household electrical appliances-> intelligent life service

sanitation car-> intelligent sanitation services

energy equipment-> Internet of Energy

• Improve quality

• Increase efficiency

• Reduce cost

• Control risk

Path to realize Industrial big data value

Agenda

• Introduction to K2Data & IIBD

• Industrial Big Data is the crucial technological element of new industrial revolution

• Industrial Big Data real world cases

Global Intelligent Industry Cases

Rolls-Royce

• The weight of the

compressor disc

was reduced the

by 15%

• Fuel consumption

was reduced by

10%, which saves

$ 2.5 million per

aircraft per year

BMW

• Saved 30% water, 40%

energy, and reduced

emission by 20% in

painting process

• Compared to traditional

hydraulic machines, the

production efficiency of

high-speed stamping

machine was increased

by more than 70%,

saved 50% energy

Shell

• With the help of

leak warnings,

each petrol station

saves an average

of $ 4,000 annually

• Accurately target

potential

customers,

achieving up to

70% customer

conversion rates

Caterpillar

• With “predictive

maintenance”

measures,

equipment

downtime was

reduced from 900h

to 24h,

• By forecasting

spare parts,

Reduce inventory

costs by ~10%

Construction machinery working condition big data system

•Construction machinery and equipment are mostly operate

in the field, under harsh environment and complex

conditions. Based on real-time construction machinery big

data solutions which monitors equipment status in real

time, preventive maintenance and service of equipment

can be achieved. Before equipment fails, it can proactively

warns and triggers maintenance plan. Based on big data

analysis of equipment operating status, it brings

innovation in decision making, which helps enterprises

accurately judge the degree of heat in the market, to

achieve accurate product marketing, product improvement

and enterprise risk control.

Construction machinery and equipment full life cycle file

• Equipment basic information

• Equipment production &

transaction information

• Equipment maintenance

information

• Equipment inspection

information

• Equipment Parts

Replacement Information

• Device Sensor Time Series

Data

Vehicle basic information

Vehicle offline information

Vehicle sales information

Vehicle sensor return information

Vehicle repairinginformation

Vehicle sensor return information

Vehicle full life cycle file(all data at the tip of your finger)

Vehicle Sensor Data Display and Statistical Analysis

Data applicationsMaintenance outlets distribution analysis

En route vehicle management

Live working status and heatmap analysis

Abnormal maintenance and fraud analysis

Analysis of the phenomenon presented: From January to May 2017, the average daily working hours were over 20 hours with a total of 20 vehicles

High-usage vehicle distribution >20 hours/day

Distribution of vehicles which works more than 20 hour per day on average

Possible thinking:

With high regional vehicle sales growth and increasing loader equipment usage-intensity, is it

needed to further increase sales?

How to define the maintenance contract for high usage-intensity vehicles, in finer granularity?

2016 Q2A total of 2 loaders

2017 Q2, 20 in total18 loaders2 excavators5 loaders working 18 hours a day

2016 Q4, 5 in total4 loaders1 excavator2 loaders working 18 hours a day

Huolinguole City has a total of 33 mining companies, of which 11 coal companies, including 8 open-pit coal mines and 3 underground coal mines, and also 22 non-coal mines.

Situation Huolinguole region, Tongliao, Inner Mongolia

The Digital strategic transformation of wind power equipment suppliers

Productivity increasesSpeed of innovation increases

R & D cost decreases

suppliers

Expand Innovative Models in the Energy Internet

Energy internet

Improve collaborative manufacturing efficiency and quality

Wind power industry data

analysis platform

Enterprises that generates electricity

Electricicalgrid

Enterprisesthat consume electricity

Enhance intelligent wind turbine and wind farm service capabilities

The Data System in Wind Power Industry

测风塔

Laser Radar

Wind tower

topographic map

SurfaceRoughness

DEMITerrain

elevation

Wind energy Solar energy

resources

Satellite remote sensing images

Geological resource attributes

On-site GPS

Global surface weather

On-site Audio and

video

Remote sensing datalike Global

Meteorological Satellite Radar

Global Ocean Data

Global Numerical ReanalysisGrid data

Global high altitude weather

information

Wind farm CFD model

Oil quality testing

Project report

50Hz High frequency (load, Vibration, etc.)

At seconds level

20msdata

Daily statistics

Fault data

Status data

power curve data

Data of Displacements

Action record (Action list file)

Repairing & Maintenance data

Fault snapshot (f file)

Fault time series(b file)

Status flip (o file)

Fire switch

10 minutes average (m

file)

Statistics accumulated (Date file)

Spectrum (energy, envelop

etc.)

Load result

Design report

Other simulation results

Big data storage service platform Third-party open platform (geography, weather etc.)

Infrastructure Hybrid Cloud

Internet of Energy

Smart

micro-

network

Power trade

Wind farm construction

Wind resourcesdevelopment

Power Generation Asset Management and Efficiency Improvement

Project evaluation

& establishingBiddingdesign

Constru-ction

Mergingnetworks

Spare PartsPreparationmaintenance

AssetManage-

mentacceptancetraining

Outbound quality

guarantee

Analyzing service layer

Digital products

Business domain

Machine timing data Maintain behaviors Fault logGeographic information

Product logisticsMeteorological

dataRoad

informationTerrain data

Electric grid data

Simulation data

Data service layer

Infrastructural platforms

Virtual power plant

Project Development

Wind farm planning and design

Digital wind farm solution

Intelligent wind turbine ……

Digital wind farm operation and maintenance solutions

alarm

Re-manufacture

Wind power forecastz

Spare parts banks

Precise operation &maintenance

Wind Farm Assessment and Optimization

Technical transformation

Old machine transformation

Power generation increasingExtend life

Virtual power plant B2Bpower trading solutions

smart micro-network solutions in industrial parks

Energy Efficiency Energy Management Solutions

Digital wind power business platform

Digital wind power business platform

•With the development towards the south and offshore, due to the limited wind resource conditions and

complicated terrain environment, it is necessary to change the power generation equipment from original

crude design to personalized precise design. In addition to traditional single wind farm arrangement

scheme , hybrid technology and precision control technology is introduced, with which the load of wind

turbines can customized point by point.

• managing 700GB of data generated by a single-point single-wheel simulation, 500,000 files effectively

• The time for post-processing analysis is reduced from 5 days to 2.5 hours, which greatly enhance the iteration speed for wind turbine design and development efficiency

• Customized and precise design reduces the cost of electricity and economic risks in wind farm construction

Data Application - Load Simulation Analysis

Data Application -anemometer data optimization

Business Problem– After the wind turbine is put into operation,

the data of measuring towers could deviate due to environmental factors

– The main control system performs yawing control based on the wrong data of wind direction, thus losing power generation efficiency

Solution and Results– By modelling working curve of wind turbine

within the past 3 months, analyzing the changing patterns of wind angle and power generation, automatically determine whether adjustment is needed or not.

– The increased power output is worth over 10,000 RMB per year per wind turbine on average

Data Application-Wind turbine timing belt fracture warning

• Business Problem– A timing belt fracture can cause collateral

disasters like unplanned downtime and blade losing control. For now, the detection method usually has a 10 seconds delay (according to collateral fault detection after the fracture)

• Solution and Results– By data mining the SCADA data fault

symptom pattern, a timing belt fracture warning model is established, the pilot wind farm data proves that it can effectively provide early warnings 90 hours prior to actual fracture

– By analyzing of abnormal behavior patterns of 20ms data on PLC, for timing belt fractures at stable wind speed, 0.6 seconds can be reduced from the current 3 seconds downtime delay.

SCADA数据分析模型可在实际断裂前90小

时预警

预警模型没有带来虚假预警（底部3台风机至今未发生

断裂）

20ms异常模式检测算法可将目前的停机时间再提前

0.6s

发现部分风场长期存在的异常震荡

Data Application - Service Knowledge Management System

Industry data analysis platform

The Deconstruct and mining of Text Data Value

Shorten the response times of remote technical services through smart case matching

Massive historical text information mining, to generate solutions intelligently, and improving on-site service capabilities

Build enterprise knowledge bases to accelerate the precipitation of service best practice

Serviceticket

Faultticket

Spare partsticket

• Values‐ Take the enterprise with an annual

output of 600,000 tons of methanol for example, methanol price is 2200 RMB/ton. If after optimization, the production can increased by 1%, then revenue can increase 13.2 million RMB per year.

‐ By establishing intelligent soft-measurement model of furnace temperature, it can provide better control the furnace temperature, to facilitate the operators, thus ensuring the stability of production.

• Business Challenges- Effective gas production rate is an important

economic indicator of the gasification process. A 1% increase will bring nearly 10 million annual revenue for the enterprise

- However, gasification is a complex and dynamic process， many factors and key parameters (such as furnace temperature and coal quality) can not be completely monitored.

• Solution‐ Using soft-sensing technology, to

accurately estimate the key state variables such as furnace temperature

‐ By utilizing techniques such as deep learning and others, constructed dynamic relationship model between variables.

‐ Based on pattern similarity,providing case-base-reasoning to improve the stability of operations.

Optimization of Gasification process operating parameters

The oil and gas pipeline of an Asia Pacific's leading petrochemical company, pipeline

leakage is the most important element in production safety and HSE management. Based

on real-time mode analysis of pressure sensor data, leaks can be detected in time.

• Alarm is triggered within 1

minute after leakage occurred,

which improves emergency

response speed.

• Accurate positioning of leak

point(300 meters), reducing

the workload of field

investigation

• Low false alarm rate (30%

using classic methods),

minimize business disruptions

Pressure

Transducer

GPS Satellite

LDS

Server

Mobile

Network

RTU

LDS ServerLDS Server

1

• Leak Detection

• Leak Location Estimation

• Pressure Trend Viewer (Navie)

CommunicationCommunication

2

• PLMN (Public Land Mobile

Network)

- CDMA

RTU

(Remote

Terminal Unit)

RTU

(Remote

Terminal Unit)

3

• Pressure Value Gathering &

Transmission via Mobile Network

• Time Synchronization with GPS

Pressure

Transducer

Pressure

Transducer

4

• 7 Yokogawa EJA430 ((25K Hz)

tranducers over 103.85Km pipeline

ResultResult• Positioning Precision: 300 meter

• Detection Time Lag: < 1 min

Oil pipeline leak detection

Based on the big data management platform for turbine industry, providing full lifecycle

management support for products; intelligent fault remote operation and maintenance

platform, using of statistical and machine learning algorithms to automatically identify

failures and trigger warnings timely; a turbine-oriented health assessment model , to provide

customers with a comprehensive diagnosis of equipment health status.

With the help of equipment health operation and

maintenance service, the maintenance costs of one set

of user units is reduced from 890,000 to 450,000, a

decrease of about 50%

By reducing unplanned downtime and normal

overhaul periods, the annual business revenue

generated by one unit increased by about 3 million

By predictive maintenance, remote expert support,

maintenance and service team personnel were

reduced by about 50%, and revenue increased by

about 40%

Power turbine equipment intelligence operation and maintenance

Data Propels the Future of Chinese Industry