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

2

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

3

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

4

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?

5

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

6

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

7

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

9

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

10

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

11

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.

1 2

34

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

21

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.

1 2

34

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).

29

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