Closing the Loop: The Data-First Approach in Digital Railway - - PowerPoint PPT Presentation

Closing the Loop: The Data-First Approach in Digital Railway

Michele Budetta

Senior Vice President Service and Maintenance Hitachi Rail Italy

Suhail Jiwani

Senior Director, Product Management Lumada IOT Platform

Hitachi Rail offers an Integrated Capability

Rail industry demand is underpinned by strong fundamentals

OFFICES / SALES PRESENCE Hitachi Rail Ansaldo STS HEADQUARTERS Hitachi Rail Ansaldo STS FACTORIES Hitachi Rail Ansaldo STS

Americas Revenue: 11% Middle East & Africa Revenue: 4% UK Revenue: 26% Europe (excl. UK) Revenue: 32% Asia Pacific (excl. Japan) Revenue: 10% Japan Revenue: 17%

Total global headcount – 11,091 (1) 3

We have a geographically diverse business with the ability to bid effectively on major projects all over the world

§ Global population is forecast to grow to approximately 10.8 billion by 2080 § Rail will play an increasingly important role in the mass transit segment of travel as global population grows

Rail industry demand is underpinned by strong fundamentals

3.0 4.4 6.1 7.8 9.2 10.2 10.8 1960 1980 2000 2020 2040 2060 2080

Global Population (billions) (1)

Population Growth

§ Significant increase in urbanisation over the last century, which is forecast to continue § Urban mobility has been a key factor in enabling this change § Inner-city, metro and commuter rail demand will increase with continued urbanisation

Rail industry demand is underpinned by strong fundamentals

Percentage of People Living in Urban Areas (2)

Urbanization

30% 37% 43% 52% 60% 66% 70% 34% 1950 1970 1990 2010 2030 2050

§ As population and urbanisation increases, reducing CO2 emissions will become an increasingly politically sensitive issue § Rail could play a key role reducing CO2 emissions

Greenhouse Gas Emissions by Travel (grams

• f CO2 per passenger km) (3)

Environment

150g 170g 30g – 70g

Hitachi is well placed to compete in growing markets

Market Position by FY2016 Revenue (2) 947.9 867.3 841.4 497.9 497.7 297.1 178.2 164.8 ~300

Overseas Revenue

3,776.9 Full Line Up Rolling Stock Systems ~10 ¥ billion

§ M&A and Consolidation in the industry to increase § Several pure play system business have been acquired to become part of a full line up rail business § Competitors are all seeking to enhance their technological capability

Market Landscape

Hitachi Rail Service & Maintenance – Very High Speed, Long Haul & Regional fleets Served

Frecciabianca fleet 136 loco + 734 cars Electric and diesel loco fleet 307 loco Double-Deck CDPTR fleet 706 cars TSR fleet35 Trains (115 cars) Frecciarossa fleet ETR500 59 trains (649 cars) Frecciarossa fleet ETR1000 50 trains (400 cars)-25 years Caravaggio-Rock fleet (from 2019) 300 trains (1425 cars) AT300: Class 800 Intercity Express 122 BI-MODE (866 CARS) Class 395 Javelin Trains 29 TRAINS (174 CARS)

Hitachi Rail Service & Maintenance – Mass Transit & Tramways Fleet

Rome Line C-13 Trains (78 cars ) driverless Lima Line 2 42 Trains 252 cars driverless Fortaleza - Linea Sur – 25 Trains (125 cars) Honolulu-20 Trains (80 cars) driverless Miami-68 Trains (136 cars) Copenhagen Cityringen- 39Trains (136 cars) driverless Thessaloniki 18 Trains (72cars ) driverless Taipei 17 Trains (68 cars ) driverless Thai Red Line 15 Trains (90 cars ) Madrid 46 Trains (216 cars ) Sirio Zuhai-Bejing 10 Trains (40 cars ) Milan Line 4-5 68 Trains (340 cars) driverless

Strategy to Action - Grow Rolling Stock Maintenance

An overview of our current rolling stock maintenance business

§ Hitachi Rail has over 50 maintenance sites worldwide § We have invested in our facilities, most recently in Doncaster and Swansea (UK), to provide the additional capacity required to deliver our recent contract wins § We have won several major maintenance contracts including: § 27.5 year IEP contract § Several other UK contracts § Trenitalia ETR1000, ETR500, and TSR trains

Strategy to action – Focus on IOT and Digital

Hitachi Rail is in a unique position in having an integrated supply chain to develop and roll-out its IOT and digital solutions

Asset Management

§ Data Analytics

• Transform from fixed maintenance

inspections to condition based maintenance with predictive interventions to minimise required maintenance and maximise railway asset availability

• Utilise data and knowledge gathered to

‘future proof’ new train designs § Delivery

• Improve profitability for long-term

maintenance contracts

• Enhanced competitive position for future bids

Brake thickness Brake temperature Slip detection Pneumatic pressure

CBM and IOT are Customer Requirements

Trenitalia CBM and IOT economics Development Investment in IoT €50M Maintenance Annual Cost €1.3B Vehicles where TI applies IOT 4000 Saving expected with IOT and CBM 18 % Saving expected due to penalty reduction €20M Trenitalia collects up to 10,000 parameters per locomotive each second, transmits these in real time via the Internet, and exploits them to better understand the health status of its fleet.

Lumada IOT Platform

Connect Collect Analyze Act

Key Tenets of Lumada Platform

Speed to Value Low Friction Ecosystem Enablement

Lumada’s Intelligent Asset Avatars

Asset Avatar Type

§ Asset Avatar Type is a digital blueprint for a class of assets (e.g: Trains, Trucks) § Asset Avatars are an instance of an asset avatar type and inherit properties of the physical asset. It is continuously updated with sensor values

Physical Asset Physical Asset Asset Avatar Asset Avatar Physical Asset Asset Avatar

Visualization Apps Data Query Service Alerts Analytics

Condition Based Monitoring for Hitachi Trains

Digital Thread: Data-Driven Optimization

Sensors

Smart Factory, IOT and CBM

CBM in IOT and Smart Factory Traditional CBM Goal

§ Improve design and production quality. § Improve vehicle reliability and maintenance efficiency. § Ensure the reliability of the vehicle operation. § Reduce maintenance cost.

Data

Time varying data. Multiple data sources Very limited time varying features

Scope

Component and System level Parts (LRU) level

Approach

Data driven, Model driven Model driven

Tasks

• Failure prediction, fault/failure

detection & diagnosis. Maintenance actions optimization.

• Any task that improve Design and

production

• Failure prediction

(prognosis), fault/failure detection & diagnosis (diagnosis)

Thank You