INTEGRATING RAILWAY MAINTENANCE DATA

Development of a Semantic Data Model to Support Condition Monitoring Data

from Multiple Sources

J. Tutcher, C. Roberts, and J. M. Easton

School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, U.K.

Keywords:

Railways, Data exchange, Domain ontology, Data interoperability, Condition monitoring.

Abstract:

Railway networks comprise a large number of information systems, many of which are implemented by differ-

ent stakeholders according to different design requirements, and in different ways. Owing to the safety-critical

nature of these systems, data is rarely shared across boundaries, and the potential for re-use of information is

lost. Using ontology, it is hoped that information from these systems can be extracted and shared, in order to

facilitate better operational decision-making. This paper examines the aspects of data re-use likely to beneﬁt

the industry, and describes a railway condition monitoring ontology that is being designed in conjunction with

several industrial stakeholders to improve operational efﬁciency.

1 INTRODUCTION

Railway networks around the world are of many dif-

ferent information systems, supplied and controlled

by different stakeholders. Owing to the complexity

of these systems, and the vastly different design ap-

proaches taken in each case, very little information

sharing occurs, leading to duplication of data and sub-

optimal decision-making in many areas. With railway

usage havingincreased steadily in most countriesover

the past decade (Ofﬁce of Rail Regulations, 2010),

and with this trend set to continue, there is now a de-

mand for greater and more intelligent utilisation of

capacity and resources, and it is possible that much-

needed efﬁciency gains in operation and maintenance

of railways can be achieved through facilitating the

sharing and exchange of information across the net-

work.

This paper discusses the application of ontol-

ogy for integrating day-to-day railway control data

(speciﬁcally train locations and characteristics) with

railway condition monitoring (RCM) system data.

Various condition monitoring methods are in use

throughout the world, including a number of points

monitoring systems, signal lamp monitoring, and

axle monitoring (for overloaded or over temperature

axles). None of these, however, currently utilise the

wealth of operational data available through other sys-

tems in order to provide context to information pre-

sented, and it is up to human operators to spend time

researching associated train information should any

of these systems present a fault - a task which is slow,

error-prone, and costly. Through work undertaken

with Invensys Rail Group (IRG), a manufacturer of

railway signalling and control systems, it is the au-

thors’ aim to research and show that not only can rel-

evant railway operations data be automatically pre-

sented with RCM fault notiﬁcations, but also that a

common data model for various RCM systems can

be established in the form of an ontology. This will

be done through the development of a semantic data

model to integrate data from InvensysRail Group sig-

nalling and control systems with existing RCM sys-

tems currently in use on the railways.

1.1 Industrial Support

Invensys Rail Group (IRG) is a railway signalling

and control company, whose products and systems

are ubiquitous across railway networksthroughoutthe

world. This project is being undertaken with support

from IRG, allowing knowledge gained throughout to

be implemented in future products and systems. In

addition to IRG’s support for this project, Network

Rail in the UK are also exploring the capabilities of

more effective condition monitoring and data man-

agement in order to maintain the railway network

more effectively, most notably through their Intelli-

gent Infrastructure (IIS) project (Ollier, 2006).

442

Tutcher J., Roberts C. and M. Easton J..

INTEGRATING RAILWAY MAINTENANCE DATA - Development of a Semantic Data Model to Support Condition Monitoring Data from Multiple Sources.

DOI: 10.5220/0003661404420444

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 442-444

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 EXISTING SYSTEMS

Multiple RCM systems are currently in use across the

UK rail network, to varying degrees of complexity. In

modern signalling installations some power and track

circuit condition monitoring capabilities are already

built into control software, but the extent to which

these are integrated with maintenance work is still

limited. Beyond a signallers’ warning to the train

driver, no alerts are issued by the system itself. The

types and capabilities of existing signalling and con-

trol systems vary widely across the UK rail network

and throughout the world; as a result, while one ben-

eﬁt of an ontology-based system design is that multi-

ple system architectures can be dealt with easily, it is

likely that this project’s scope will be initially limited

to IRG’s WestCAD system.

As part of the InteGRail project (Langer et al.,

2008; Lewis et al., 2006), a European project aimed

at exploring integration between railways, a number

of ontology demonstrations were created showing the

capabilities of the technology in the rail domain. A

’core ontology’ was developed, encompassing high

level railway infrastructure information such as routes

and vehicle types, and several child ontologies de-

signed to explore particular use cases identiﬁed ini-

tially. One such ontology looked at the handling of

condition monitoring data from several existing data

sources, and used reasoning in order to establish ex-

plicit and implicit fault conditions on a particular area

of railway. Knowledge gained from the InteGRail ap-

plications will be built on in this project, speciﬁcally

in the case of the railway condition monitoring system

ontology design.

2.1 Wheel Impact Load Detection

Network Rail (owners and managers of UK mainline

rail infrastructure) have 26 wheel impact load detec-

tion (WILD) systems in use on their network. These

systems are placed at points along railway track to

assess the physical condition of trains as they pass,

in order to prevent further damage to railway infras-

tructure. Sensors measure stress/strain exerted on the

rails to determine information about a vehicle as it

passes. For each train passing a WILD site, date/time

of passage, track and direction, wheel loads, and axle

counts are recorded by the system and passed to a re-

mote PC (AEA Technology, 2008). Using the cur-

rent system, this information is passed to a central

computer, and then distributed to regional centres for

analysis using a piece of bespoke PC software. It is

up to human operators in each location to interpret

this data and decide which (if any) axles are dam-

aging or dangerous, as well as to identify the actual

trains passing the WILD system at any time ’ identiﬁ-

cation is done based on known train movements using

TRUST, a Network Rail run computer mainframe-run

database (Network Rail, 2010) as well as through ed-

ucated guesses of train types from axle counts. This

is far from ideal, and the time taken to deduce train

location and fault conditions is considerably longer,

and less consistent, than should be possible using an

automated ontology-based alternative.

3 ONTOLOGY APPLICATION

3.1 The Case for Ontology

Projects such as InteGRail (Langer et al., 2008) have

recently shown applications of ontology across the

rail industry, and speciﬁcally in handling various

types of railway condition monitoring data (as in the

wheel impact load measurement / hot axle box detec-

tion ontology created as part of that project). The In-

teGRail project successfully showed the inference ca-

pabilities of ontology technology within the ﬁeld; this

project aims in addition to explore data integration

between train control systems and condition moni-

toring. A correctly modelled ontology system also

has the more fundamental advantage of allowing data

collected to retain its meaning and context, allowing

more ﬂexible querying and interrogation of data than

is currently possible.

3.2 Data Exchange & Interoperability

A major advantage of an ontology-based approach to

storing RCM data is that it can deal very well with

heterogeneous data. Even across multiple systems

ing on fault data from different types of monitoring

equipment and sensors can be a challenge, and the

structure of an ontology-basedapproach lends itself to

coping with these scenarios very well. If the ontology

is constructed such that states from condition moni-

toring equipment can be deduced regardless of sys-

tem type (for instance inferring a ’fault’ condition in

each different type of system), communication is rel-

atively trivial. Across existing multi-vendor systems,

where data structures and communications protocols

are likely to differ more, a semantic model allows data

integrity to be maintained whilst building a common

platform on which to reason and observe. Ontology

also providesa ﬂexible platform for expandingsystem

reach further into the system’s life, and will allow ex-

pansion into other areas of railway maintenance and

INTEGRATING RAILWAY MAINTENANCE DATA - Development of a Semantic Data Model to Support Condition

Monitoring Data from Multiple Sources

443

operations without the requirement for a drastic re-

structuring of the system.

3.3 Reasoning & Inference

Using an ontology model to combine condition mon-

itoring data with railway operations provides the po-

tential for autonomous reasoning, removing the ne-

cessity for human operators to spend large amounts

of time cross-referencing and checking raw data in

order to determine and describe vehicle/infrastructure

faults. This is likely to be a key beneﬁt to railway op-

erators, and an aspect of the system which will be de-

veloped in depth. A reasoner will allow, for example,

the association between a vehicle’s geographic loca-

tion and an RCM fault value to be done automatically.

This is one of the key beneﬁts of using an ontology-

based system to integrate data across the railway, and

knowledge inferred through reasoning techniques is

likely to be of great value to railway operators and

other stakeholders.

4 A WILD ONTOLOGY

Initially, the development of a new ontology system

to address the data integration issues discussed above

will aim to capture informationfrom the Network Rail

WILD axle load monitoring system and combine it

with train running data (such as vehicle locations and

descriptions). It is proposed that such an ontology

include knowledge of several aspects of the railway

domain, as follows:

• Railway geography and network layout, in order

to locate vehicles on track and RCM sensors. In-

vensys Rail’s WestCAD system tracks train lo-

cations in discrete ’bays’ along the track, based

on data from railway track circuits or axel coun-

ters; the WestCAD data, or even track circuit data

could be used to locate trains in an ontology.

• Railway vehicle knowledge. Train headcodes

contain limited information about vehicle class

(freight, passenger, high speed services); this in-

formation should be represented as such, with

the possibility of being expanded upon using data

available from other systems (in-cab diagnostics,

train operator data).

• RCM knowledge such as WILD raw data, passing

vehicle type, fault conditions, equipment charac-

teristics, and equipment geography.

With these aspects correctly modelled, it is possible

that rules created in an ontology reasoner will allow

WILD data to be correlated to railway vehicles, and

could also associate vehicle types with common line

faults (for instance train bogie designs that are less

reliable than others). Train speed and weight as ac-

quired by the WILD system will also form part of

the ontology, allowing speed proﬁling of services over

long periods of time to be performed. Creation of this

data model will allow the retrieval of WILD infor-

mation with regards to speciﬁc trains, automatically,

and will thus save the expense of needing human re-

sources to verify all incoming RCM data. The even-

tual use of this system will add value to existing IRG

products, and continued development of the ontology

system should allow greater utilisation of the technol-

ogy in the railway domain.

4.1 Future Development

It is important that the WILD system as described

above be modelled in such a way that further develop-

ment is possible, in order that many more sub-systems

can be included and further knowledge inferred. It

is the project’s aim to use this ontology application

as a basis for a larger railway systems ontology over

the next two years, showing that further streamlining

of railway data can be achieved across control, op-

erations, and maintenance of the network. Further

work will involve the inclusion of other railway sys-

tems data in the ontology, such as recorded vehicle

’black box’ data (many trains record speed, location,

and diagnostics information as they travel), and com-

prehensive timetable information. With the addition

of these data sets, an ontology system will be able to

perform further reasoning in order to contribute new

knowledge to railway stakeholders.

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AEA Technology (2008). Wheelchex user guide.

Langer, G., Lewis, R., and Roberts, C. (2008). Ontology

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