Transcript Le nuove tecnologie - Ordine degli Ingegneri della Provincia di Milano

Le nuove tecnologie al servizio
dei processi operativi di Trenitalia:
i nuovi ingegneri
Paolo MASINI
DT-IRTB
Milano, 23th February 2016
Ferrovie dello Stato Italiane Group
Organizational structure
Ministry of Treasury
100%
Ferrovie dello Stato Italiane
100%
100%
Trenitalia
Operator of
passenger and freight
rail transport
100%
TX Logistik
Rail freight
operator
RFI
Rail infrastructure
manager
100%
Italferr
Railway engineering
company
100%
Other Companies
BusItalia SITA nord
Operator
of public bus transport
51%
55,7%
Netinera
Italcertifer
Grandi/Cento Stazioni
Company for certification
of rail components and
systems
Manager
of main rail stations
Operator of regional
passenger transport (rail
and road) in Germany
60%
2
FS Italiane Group - Key figures 2014
Net result
Revenues
8.390 M€
Revenue share in foreign markets
12%
Staff (employees)
69.115
(M€)
2006
16
2007
2008
54
2009
129
2010
285
381
2011
2012
460
303
2013
-409
EBITDA dynamics
-2.115
Revenues
461
+125%
-650
1.782
1.035
1.358
+31%
1.673
+6%
1.917
+8%
2.033
2.114
+6%
+3,9%
Ebitda
+23%
Costs
M€
International Accounting Standards (IAS)
3
2014
Competitive position of FS Italiane Group - 2014
Long distance
passenger
rail transport
N° 3 in EU
Regional passenger
rail transport
N° 4 in EU
Rail freight transport
N° 5 in EU
Public bus transport
N° 4 in Italy
4
Profitability benchmark
2006
Revenues - M€
6.703
2007
7.680
2008
7.816
2009
7.821
2010
7.985
2011
8.264
2012
8.228
2013
8.329
2014
8.390
Ebitda Margin - %
-9,7%
6,0%
13,2%
17,4%
21,0%
21,6%
23,3%
24,4%
25,2%
Ebit Margin - %
-28,8%
-0,3%
5,7%
5,6%
6,3%
8,0%
8,7%
9,9%
7,9%
2006
2007
2008
2009
2010
2011
2012
2013
2014
32.912
34.528
36.498
33.199
37.530
41.041
42.739
41.960
42.552
Revenues - M€
Ebitda Margin - %
16,5%
16,5%
14,6%
15,2%
12,6%
12,5%
13,7%
11,9%
11,8%
Ebit Margin - %
7,5%
8,4%
7,1%
6,7%
4,8%
5,3%
5,9%
4,2%
4,3%
2006
2007
2008
2009
2010
2011
2012
2013
2014
21.965
23.691
25.184
24.882
30.466
32.645
33.820
32.232
27.243
8,7%
Revenues - M€
Ebitda Margin - %
13,4%
11,7%
10,3%
6,8%
7,1%
9,3%
8,5%
8,7%
Ebit Margin - %
6,2%
6,9%
3,4%
-1,8%
4,6%
2,5%
3,5%
0,9%
*
3,8% *
FS Italiane Group is the top European
player in profitability
Source: FS, SNCF and DB annual reports
(*) 2014 data refers only to SNCF Mobilités
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*
Rail transport – Trenitalia key figures for 2014
Staff
31.802 employees
Fleet
1.580 locos
112 EMUs (electro trains)
1.547 MUs (light trains)
Passengers
25.896 coaches + freight cars
498 shunting locos
38,6 Bn of passengers km /year
Tons
Trains
14,7 Bn of tons km/year
7.263/day
Revenues
5.576,72 Million €
Source: Trenitalia 31 Dec. 2014
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Scenario
Recente passato
• Trenitalia è il maggior operatore ferroviario italiano e il terzo
europeo.
• Grande trasformazione: da un pesantissimo passivo ad un attivo
strutturale;
• Miglioramento dei servizi e mantenimento del livello di sicurezza.
Presente
• Sostiene con successo l’unico caso mondiale di concorrenza sui
servizi di alta velocità.
• Focalizzazione sul trasporto regionale
Sfide future:
• Gare per il servizio regionale (Servizio di eccellenza
a costi concorrenziali;
• Concorrenza sull’AV da altri operatori nazionali e
soprattutto internazionali;
• Altri modi di trasporto: Aerei Low Cost, Bla Bla car e
in prospettiva driverless car;
• Internazionalizzazione
Innovation on the High Speed/High Capacity system
HS/HC Network - Travel time
Lines
TO-MI
MI-BO
BO-FI
RM-NA
RM-MI
No HS
1: 30’
2: 07’
1: 02’
1: 45’
6: 53’
With
HS
47’
1: 01’
35’
1: 10’
Dec 2014
Dec 2014
Dec 2014
Dec 2014
2: 50’
non stop
Dec 2014
HS/HC Network - Modal split on the Milan - Rome (%)
2014
9% 1%
27%
63%
HS/HC NETWORK
Operating
Operating (commercial
speed up to 250 km/h)
Construction
Project
8
12
Competition: Trenitalia and ETR
500 in the past
In 2006 Trenitalia has 2 bn € loss, 54000 people
Really no good image and bad customer satisfaction
ETR500: Eurostar italia
• Low reliability
• High operation cost
• Technology from the 80s
• Built by a consortium … no
individual responsible for
problems
Competition: past fleet interiors
Eurostar Italia ETR500 interiors
Competition: the newcomer
Competition: Trenitalia’s New Brand
In 2009 Trenitalia launched Frecciarossa & Frecciargento brand, with new
external livrea;
Competition: Frecce organization
• Freccia Rossa e Freccia Argento were created
with full integration between fleet manager and
maintenance;
• For every brand: dedicated operational room
that take care of customer, train is a way to
customer satisfaction not the end;
Frecciarossa new interiors
From 2010 up to 2012 Frecciarossa introduces 4 level of services and
advanced PIS and Internet;
Frecciarossa new interiors
Frecciarossa interiors
Feeling Frecciarossa 1000…
Frecciarossa 1000 in numbers
9.8 MW distributed power
16 motors
4 service levels 8 coaches
executive, business, premium, standard
360 km/h
operating speed
powerful
400 km/h max speed
455 seats + 2 prm
native Condition Based Maintenance
Coupling 2 trainsets
TSI compliant
materials recyclable 94.4%
renewable 97%
7 countries as option
interoperable
France, Germany, Austria,
Spain, Switzerland,
The Netherlands, Belgium
comfortable
pneumatic and active lateral dumpers
almost noiseless
sustainable
external < 91dB[A]
Environmental Product Declaration
& Certified Control System - SGS
Layout
EXECUTIVE
PREMIUM
ROME Tiburtina
Arch. Paolo Desideri
FLORENCE
Arch. Norman Foster
BUSINESS
STANDARD
Towards 400 km/h
Very high speed tests operating at 360 km/h
Oct 2015 to June 2016
Milan – Turin
testing
stability, braking, aerodynamics, control & command
systems
Vision and aims
our
vision is managing safe railway services in a more
efficient way in view of customer
satisfaction in an open market
research is devoted towards this aim
improving process and innovate products
via Technical Dept.
Sharing projects
international partnership
academic and industrial
New tecnological opportunity
Technology transfer from other sectors in railway:
• BIG DATA system: process optimization:
• Internet of Things:
• New sensing technologies;
• Vision and laser scanner system: automatic checks
• Robotic systems for inspections and operations
Actual Maintenance
•
Corrective
maintenance:
fault
recovery
in
shop
after
operation;
•
preventive maintenance at fixed deadlines (time, km)
Disadvanteges:
•
Failures are not avoided.
•
Complicated system of checks in the first level:
•
Poor connection between component wear and maintenance
deadlines;
•
Maintenance plan is optimized only on logistic base;
Dynamic Maintenance Management System
Target
Reduction of maintenance costs, increased reliability and availability, through:
• Improvement in fault management in the maintenance process
• Substantial decrease in operating faults using predictive models
• Improvement of the process of verification in 1st level
• structural changes to maintenance plans with life and health indicators
• Efficiency in the management of the use of the rolling stock, facilities, labor and spare parts, based on the
results of the above
Main activities
ON Board
Telediagnostic
Ground diagnostic
system
Dynamic Maintenance
Management System
Fault Management and TELEDIAGNOSTICA DI BORDO
.
Currently the most modern fleets are installed computer that:
•
They are connected to the onboard equipment
•
They are equipped with mobile data connection;
•
Transmit diagnostic events to the ground;
•
They can add additional diagnostics;
Main advantages:
•
Increase efficiency
•
Surveillance of the fleet (eg. Redundancy)
•
Help desk
On board unit
The on board unit is:
Interfaced to the MVB bus, vehicles controls
units, external sensors.
COTS (Commercial Off-The-Shelf)
Connected via standalone 3G/4G or PIS
network
Programmed in LabView to acquire,
elaborate, store and transfer to ground
sensor signals and diagnostic events
Frecciarossa DASHBOARD
E464 status
Dashboard E464
Remote desktop
Predictive Maintenance
The computers installed on the trains can continuously record and transmit process variables with
continuity;
On the train there are hundreds of sensors whose information can be used;
Currently the 730 E464 downloading on average 300 MB / g / recording site for about 5000 signals to
1s
Than:
BIG DATA
The wiring of the new sensors is currently still very expensive and should be carefully evaluated in
terms of returns. The situation could change drastically with the advent of pervasive:
Intertenet Of Things
Than we need smart sensors
Improving the maintenance process
•
Actually maintenance is scheduled on a km/time base, planning activities grouping
operation at different time/km deadline;
•
Indeed every component of a train has his own deteriorating process, that is not always
directed linked to the time or km.
•
For exemple a door is influenced more by number of opening/closing cycles than by km
•
Up to now km and time are the only quantities usually monitored, while other quantities
were difficult to measure or time consuming;
Past
• Preventive
maintenance based on
km/time
• Corrective
maintenance after
failures
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Now and future
• Maintenance based on
age/wear indicators
• Maintenance based on
health indicators
Life and health indicators
For the relevant components two type of indicators are defined:
Age : takes into account the predicted wear of the part evaluating
relevant quantities or complex functions of that (e.g. cycles, hours
of operation, kilometer, energy, etc). Maintenance is done when
predefined thresholds are reached.
Health: takes into account the actual status of
operation by measuring proper parameters (e.g.
closing time for a door, temperatures of cooling
systems,
duty
cycles
of
compressors,
etc.).
Maintenance is done when the parameter goes out of
the normal range and before the failure.
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Life and health indicators
Predictive Maintenance: how to use
There are two ways to proceed:
• Classify abnormal situations (identifying fault pattern)
and continuously monitoring the distance from actual
state
• Identify normality and measure continuously the
deviation;
Dynamic Maintenance Management System
Operation
Engineering
Communication
Gateway
Segnali ed
Eventi
Laboratory
Rule creation
Telediagnostica
Predictive analisys
Maintenance interaction
Life&Health Indicator
Alarms delivery
Activity scheduling
Configuration
tool
Profilo ingegneri
• Avere solo data scientist non è efficace;
• I sistemi BIG DATA necessari a gestire la manutenzione sono diversi
da quelli relativi alle analisi dei sentiment, delle vendite o della analisi
di facebook;
• Questo mondo è molto più vicino all’Internet of Things;
• Necessario riuscire a virtualizzare i sistemi fisici con un numero
opportuno di parametri funzionali all’obiettivo finale;
• Customizzare algoritmi generici alla realtà specialistica in oggetto
Quindi qualunque tipo di ingegnere deve avere nozioni
di algoritmi di elaborazione massiva dati perché farà
parte della vita di tutti i giorni, come erano le equazioni
differenziali un tempo
questions?
comments?
remarks?