Il ruolo della digitalizzazione nell’ottimizzazione
Transcript of Il ruolo della digitalizzazione nell’ottimizzazione
Il ruolo della digitalizzazione nell’ottimizzazione
del processo di manutenzione
G. Guido, V.P.Operation&Maintenance
N.Mazzino, V.P.Digital Railways and Innovative Technologies
AICQ, Firenze 30 novembre 2017
Master title
Digitization, digitalization and.. digital transformation
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These terms are often used as synonims but they indeed have a different meaning and span• Digitization is transformation in digital format of
paper documents, signals, and data
• Digitalization is the transformation of processes, functions and activities based on the availability of digital data
• Digital transformation is the overall effect of the digitalization on the business/customers/activities of a company or systems leading to a new definition of business models/operating procedures/ manufacturing processes
Digitalization = data and communication network
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Digitalization relies on the availability of manageable data
Data are generated by different sources (equipment,
people, operating machines, vehicles, etc..) in different
locations. Their usability requires the possibility to
aggregate and transport these data
Digitalization can occur only if adequate and dependable
communication means are available to transport the data
Aggregation, analysis and elaboration of the collected data allows their
transformation into
What can we expect from Digitalization
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Digitalization is expected to bring benefits in multiple areas/sectors:
• enhanced customer experience by offering better and added
value for customers • Smart Ticketing and intermodal mobility
• Human Flow
• Passenger information and On board entertainment systems
• Security
• Integrated Rail Operations by integrating information about
resources and services• Intelligent Traffic Management System embedding Rolling Stock and Crew
management
• Dynamic Headway
• Higher safety for workers
• Cost reduction by collecting real time information about asset
status• Asset Management and Predictive Maintenance
• Spare parts and stock management
• Maintenance training
Which is the context behind?
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Successful stories in Digitalization of the Asset Management coming from other
sectors (e.g. aerospace) made this concept a «must» also for railways and
metro.
Ansaldo STS started working in this field by setting up several research
projects, gaining an important know-how for stepping into production of
predictive maintenance solutions.
To setup this new framework it is necessary to run in parallel for:
• Collecting and analysing data;
• Defining new optimized process;
• Creating new skill and competences.
Signalling and Automation systems are a mine of data/information, but to
make them part of a “digitalized” Intelligent Asset Management system actions
are needed.
Intelligent Asset Management: the main Goals
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This presentation aims to show you the process we are following for upgrading
our systems in order to achieve an Intelligent Asset Management solution to
increase the monitoring, management and maintenance of the most important
assets, also paving the road to «predictive» functionalities.
Standardize
Assets/Components
Mapping and
nomenclatures
Optimize processes
Shift from corrective to
on condition /predictive
maintenance
Use data for events
correlations
(Big Data)
Minimize
Risks
3.4 Asset Management
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Condition Based / Reliability Centered Maintenance based on big data
analytics reduces maintenance cost and increase system availability
The goals of these
processes are:
✓ Gathering vast
quantities of data
✓ Using predictive
analytics to increase
reliability
✓ Improving Train and
components design
✓ Optimising
maintenance
operations and
logistics
✓ Minimizing spares
stock
1. Intelligent Asset Management – the concept
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Diagnostic &
Monitoring
Systems
Existing Asset
Registers
(and similar DBs)
Signalling &
Train Control
Systems
Other
Dynamic Rail
Information
External
Information(e.g. weather data, etc.)
Data Mining, Big Data & Predictive Analytics
Decision-making, Strategies & Execution
Intelligent Assets Management for Optimized Decisions
Maintenance
Decisions
Information
+
Extracted Knowledge
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System Analysis
Issues Identification
Data Availability
Data Collection, Understanding &
Preparation
FunctionalitiesImplementation
and testing
HeterogenousData
Iterative process for building an Intelligent Asset Management System
IAMS Functionalities
Deployment
• Asset Status• CBM• Predictive Maintenance
Refinement(if needed)
IterativeLab-Testing Process
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The first specific applications of the IAMS approach are focused on systems
operated and maintained by AnsaldoSTS (metro systems –first and new
generation).
On these two different systems the process described before was applied for
performing:
• Preliminary analysis;
• Data collection, data analysis and dashboards visualization;
• Gap analysis and definition of possible upgrade.
Metro system (first generation)
PSD
SCADA
ATC
ON
BO
AR
D
ATP
ATOWAYSIDE
INTERLOCKING
ATS
Operational data
Preliminary Analysis steps
• Working sessions with the system experts, so to take advantage of their
experience to identify recurrent issues and available datasets related to them
• Collection of available diagnostic data and related to maintenance/repair
activities
• Data processing for the identification of issues (evidence in data of experts’
feedback)
• Gap analysis, definition of actions to bridge the gaps and identification of the
updated architecture
PSD
SCADA
Train
Monitoring
Architecture Upgrade
Operational data
Diagnostic and
monitoring data
SAP
Events/Alarms
coming from
different systems
DATA
INGESTION
(STORAGE AND
PRE-
PROCESSING)
DASHBOARD
TO VISUALIZE
RESULTS
DATA
ANALYSIS
to identify
possible
correlation
between different
events/alarms
PROCESS
STEPS
Data Analysis performed on the first Metro System generation
Non-structured data
(logs files) have
been acquired and
stored on the Data
Lake. After the
cleaning and
formatting process
data are ready for
the analysis step.
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SCADA
Approach to the Analysis
Dashboard Visualization
When the user clicks on a specific alarm the
system calculates the possible causes and
shows it in the pop-up screen.
The graph represents the relations between the
occured alarm and events/alarm occurred in
the past to help maintenance team to
identify the real failurecauses and the properintervention required.
The IAMS Platform S
tru
ctu
red
Data
Un
str
uctu
red
Data
Access
Integrate
Transfor
m
Profile
Cleanse
Enrich
Predictive
Analytics
Data
Mining
Machine
Learning
Reports
Dashboard
s
Charts
Portals
Data
Engineering
Advanced
Analytics
Data
Discovery
Cu
sto
miz
ed
Bu
sin
ess
In
sig
hts
an
d B
ig D
ata
Us
e C
as
es
End-to-End Embeddability
Data Lake: a modular, scalable,
distributed storage system able to manage
large amounts of data. This system could
be easily enlarged to cover new assets and
other future developments.
Data analytics of the data contained in the Data Lake to
perform the three main Asset Management
functionalities:
• Asset Status Monitoring
• Condition Based Maintenance
• Predictive Maintenance
Metro System (new generation)
New Generation of a Metro Line
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More in details the IAMS Platform described above,
has been applied to
improve ASTS Track
Circuits (TCS)
monitoring and maintenance
process.
In the next slides it will be shownwhat is currently feasable working
on a new generation of metro lines already able to
collect, store and make data available
for analysis.
Predict failures occurencieswith different time-horizon.
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Ongoing Activities
Data-driven analysis to
improve maintenance procedures
Analysis of historical data
Predictive Models
Provide failures report to the maintenance team.
Keep track of past failuresnumber, causes distribution
over the line.
Data visualizationtechniques and dash board
creation.
Finding «abnormal» beahaviour patterns in TC parameters to identify a
degraded assets (anomalydetection)
Failures nowcasting to investigate and asses
failures causes in a real-time fashion
BINARY FILES
FROM AF-GEN II
Data Sources
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CENTRAL ATC
(AUTOMATIC TRAIN CONTROL)
LOGS FROM
AF-GEN II
MAINTENANCE
REPORTS
EVENTS AND
ALARM LOGS
DIFFERENT
PARAMETERS
FROM TCS
BOARD
ACQUIRED
DATA
INGESTION
(STORAGE AND
PRE-
PROCESSING)
INTERACTIVE
DASHBOARD
WITH RESULTS
FOR THE
MAINTENANCE
TEAM
ANALYTICS ON
NEW DATA
COMBINED WITH
HISTORICAL
DATA
PROCESS
STEPS
New Generation Metro Line Track Circuits (TCS) data for maintenance
scheduling improvement
Feedback for analysis
process refinement
TCS systemavailable data
(events/ alarms/measures):
historicalreal-time data are (acquired hourly)
Non-structureddata (logs files)are acquired
each day and stored on the Data
Lake. After the cleaning and
formatting processdata are ready for the analysis step.
When all dailydata are
collected, analysisis performed. Then, another
anlysis is
performed usingalso historical
data to improveresults reliability
Interactive dashboard
(uptaded eachday) is used from the maintenance
team to identify line sections or single
TCS at risk in orderto improve
maintenanceoperations.
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Failures
Reports
SingleTCS
Analysis
OverallTCS
Analysisx
y
Single TCS parameters trends over
time: time series could be analyzed
and visualized in different time
windows (i.e. daily or monthly).
Histograms for cumulative statistics
distributions to describe an overall
behavior (considering a single
parameter) for all TCS involved.
Bubble chart for failures number to
visualize the distribution of failure
occurrencies along the line during
the selected interval of time.
Histogram fo failure occurrencies
for analysis and visualization of
failed TCS depending on different
failures type (different colors).
Different analysis
performed in
order to provide
support to
maintenance
Types of Analysis
Interactive Dashboard Examples (1)
Graph derived from the dashboard containing analysis
results representing the elaboration of data collected in 3 month. This is used from the maintenance team to visualizefailures distribution over the
line in order to identify criticalline sections. Moreover, the dashboard allows to visualize
TCs associated stations.
(Failures report summary)
Interactive Dashboard Examples (2)
For each specific TCS, it ispossible to track parameterstrends over time to identify«abnormal» patterns and
degrading status. Moreover, itis possible to monitor
parameters values in relation with predefined thresholds.
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Strategic System Level
HMI showing high level
information related to the
status of the system and
providing decision support
tools based on specific KPIs
(e.g. Cost saving, Failure
rate, Risk reduction,…)
focused on the needs of the
each specific final user (e.g.
infrastructure Owner, the
Infrastructure Manager and
the “Global” Maintenance
Service Provider).
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Tactical Subsystem Level
HMI showing information
related to the status each
different subsystem and
providing decision support
tools based on specific
KPIs (e.g. Spare parts
availability, Failure rate,
intervention procedures,…)
and focused on the needs
of the Maintenance
Scheduler.
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Operational Component Level
HMI showing information
related to each component
and subcomponent of each
different subsystem and
providing decision support
tools based on specific KPIs
(e.g. Failure rate, intervention
procedures,…) focused on the
needs of the Maintenance
Crews providing them detailed
information about past,
present and future status of
each components and the
related planned activities.
Conclusions
There’s no magic!
To integrate a IAMS as decision support tools for the maintenance activities aniterative process is needed in order to reach the desired results.
Physically upgrading the system, processing the new data acquired, creating aniterative process with all the experts of the systems to reach a concrete resultable to pave the way towards a digitalization of the maintenance process.
THANK YOU FOR YOUR ATTENTION