Presentation

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Chapter One
MAINTENANCE
1.1 INTRODUCTION
1.1.1 THE CHANGING WORLD OF MAINTENANCE
Over the past thirty years, maintenance has changed, perhaps more so than any
other management discipline. The changes are due to a huge increase in the
number and variety of physical assets (plant, equipment and buildings) which
must be maintained throughout the world, much more complex designs, new
maintenance techniques and changing views on maintenance organization and
responsibilities.
Maintenance is also responding to changing expectations. These include a
rapidly growing awareness of the extent to which equipment failure affects
safety and the environment, a growing awareness of the connection between
maintenance and product quality, and increasing pressure to achieve high plant
availability and to reduce costs.
The changes are testing attitudes and skills in all branches of industry to the
limit. Maintenance people have to adopt completely new ways of thinking,
planning and acting, as engineers and as managers. At the same time the
limitations of maintenance systems are becoming increasingly apparent, no
matter how much they are computerized.
In the face of this avalanche of change, managers everywhere are looking for a
approach to maintenance. They want to avoid the false starts and dead ends
which always accompany major upheavals. Instead they seek a strategic
framework which synthesizes the new developments into a coherent pattern, so
that they can evaluate them sensibly and apply those likely to be of most value
to them and their companies.
1.1.2 THE FIRST GENERATION
The First Generation covers the period up to World War II. In those days
industry was not very highly mechanized, so downtime did not matter much.
This meant that the prevention equipment failure was not a very high priority in
the minds of most managers. At the same time, most equipment was simple and
much of it was over-designed. This made it reliable and easy to repair. As a
result, there was no need for systematic maintenance of any sort beyond simple
cleaning and lubrication routines. The need for skills was also lower than it is
today.

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1.1.3 THE SECOND GENERATION
Things changed dramatically during World War II. Wartime pressures increased
the demand for goods of all kinds while the supply of industrial manpower
dropped sharply. This led to increased mechanization. By the 1950‟s machines
of all types was more numerous and more complex. Industry was beginning to
depend on them.
As this dependence grew, downtime came into sharper focus. This led to the
idea that equipment failures could and should be prevented, which led in turn to
the concept of preventive maintenance. In the 1960‟s, this consisted mainly of
equipment overhauls done at fixed intervals.
The cost of maintenance also started to rise sharply relative to other operating
costs. These led to the growth of maintenance planning and control, and are
now an established part of the practice of maintenance.
Finally, the amount of capital tied up in fixed assets together with a sharp
increase in the cost of that capital led people to start seeking ways in which they
could maximize the life of the assets.
1.1.4 THE THIRD GENERATION
Since the mid-seventies, the process of change in industry has gathered even
greater momentum. The changes can be classified under the headings of new
expectations, new research and new techniques.
1.1.5 MAINTENANCE
From the engineering viewpoint, there are two elements to the management of
any physical asset. It must be maintained and from time to time it may also need
to be modified.
The major dictionaries define maintain as cause to continue (Oxford) or keep in
an existing state (Webster). This suggests that
Maintenance means preserving something. On the other hand, they agree that to
modify something means to change it in some way.
When we set out to maintain something, what is it that we wish to cause to
continue? What is the existing state that we wish to preserve?
The answer to these questions can be found in the fact that every physical asset
is put into service because someone wants it to do something. In other words,
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expect it to fulfill a specific function or functions. So it follows that when we
maintain an asset, the state we wish to preserve must be one in which it
continues to do whatever its users want it to do.
Maintenance: Ensuring that physical assets continue to do what their users
want them to do.
1.2 FUNCTIONS AND PERFORMANCE STANDARDS
Before it is possible to apply a process used to determine what must be done
to ensure that any physical asset continues to do whatever its users want it
to do in its present operating context, we need to do two things:
determine what its users want it to do
ensure that it is capable of doing what it users want to start with.
Primary functions, which summarizes‟ why the asset was acquired in the
first place. This category of functions covers issues such as speed, out-put
carrying or storage capacity, and product quality and customer service.
Secondary functions, which recognize that every asset is expected to do
more than simply fulfill its primary functions. Users also have
expectations in areas such as safety, control, containments, comfort,
structural integrity, economy, protection, efficiency of operation,
compliance with environmental regulations and even the appearance of
the asset.
1.3 OBJECTIVES OF MAINTENANCE
The objectives of maintenance are defined by the functions and associated
performance expectations of the asset under consideration. But how does
maintenance achieve these objectives?
The only occurrence which is likely to stop any asset performing to the standard
required by its users is some kind of failure. This suggests that maintenance
achieves its objectives by adopting a suitable approach to the management of
failure.
However,
firstly, by identifying what circumstances amount to a failed state
then by asking what events can cause the asset to get into a failed state.

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In the world of RCM, failed states are known as functional failures because
they occur when an asset is unable to fulfill a function to a standard of
performance which is acceptable to the user.
1.4 FAILURE MODES
As mentioned all the events which are reasonably likely to cause each failed
state. These events are known as failure modes „Reasonably likely‟ failure
modes include those which have occurred on the same or similar equipment
operating in the same context, failures which are currently being prevented by
existing maintenance regimes, and failures which have not happened yet but
which are considered to be real possibilities in the context in question.
Most traditional lists of failure modes incorporate failures caused by
deterioration or normal wear and tear.
1.5 FAILURE EFFECTS
Failure effects, which describe what happens when each failure mode occurs.
These descriptions include all the information needed to support the evaluation
of the consequences of the failure, such as:
what evidence (if any) that the failure has occurred
in what ways (if any) it poses a threat to safety or the environment
in what ways (if any) it affects production or operations
what physical damage (if any) is caused by the failure
what must be done to repair the failure.
The process of identifying functions, functional failure modes and failure effects
yields surprising and often very exciting opportunities for improving
performance and safety, and also for eliminating waste.
1.6 FAILURE CONSEQUENCES
Failure consequences can be classified into four groups:
Hidden failure consequences: Hidden failures have no direct impact, but
they expose the organization to multiple failures with serious, often
catastrophic, consequences. (Most of these failures are associated with
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SAFETY AND ENVIRONMENTAL CONSEQUENCES: A failure
has safety consequences if it could hurt or kill someone. It has
environmental consequences if it could lead to a breach of any corporate,
regional national or international environmental standard.
Operational consequences: A failure has operational consequences if it
affects production (output, product quality, customer service or operating
costs in addition to the direct cost of repair)
Non-operational consequences: Evident failures which fall into this
category affect neither safety nor production, so they involve only the
direct cost of repair.
The consequence evaluation process shifts emphasis away from the idea that all
failures are bad and must be prevented. In so doing helps us to, it focuses
attention on the maintenance activities which have most effect on the
performance of the organization, and diverts energy away from those which
have little or no effect. It also encourages us to think more broadly about
different ways of managing failure.
1.7 Age and Deterioration
Any physical asset which is required to fulfill a function which brings it into
contact with the real world will be subjected to a variety of stresses. These
stresses cause the asset to deteriorate by lowering its resistance to stress.
Eventually this resistance drops to the point at which the asset can no longer
deliver the desired performance – in other words, it fails.
Exposure to stress is measured in a variety of ways including output, distance
travelled, operating cycles, calendar time or running time. These units are all
related to time, so it is common to refer to total exposure to stress as the age of
the item. This connection between stress and time suggests that there should be
a direct relationship between the rate of deterioration and the age of the item. If
this is so, then it follows that the point at which failure occurs should also
depend on the age of the item.
Deterioration is directly proportional to the applied stress, and
The stress is applied consistently.
If this were true of all assets, we would be able to predict equipment life with
great precision. The classical view of preventive maintenance suggests that this
can be done – all we need is enough information about failures.

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Most people still tend to assume that similar items performing a similar duty
will perform reliably for a period, perhaps with a small number of random early
failures, and then most of the items will „wear out‟ at about the same time.
Age-related failure patterns apply to items which are very simple, or to complex
items which suffer from a dominant failure mode. In practice, they are
commonly found under conditions of direct wear (most often where the
equipment comes into direct contact with the product). They are also associated
with fatigue, corrosion, oxidation and evaporation.
Examples of points where equipment comes with the product include furnace
refractory, pump impellers, valve seats, machine tooling, screw conveyors,
crushers and hopper liners, the inner surfaces of pipelines, dies and so on.
Fatigue affects items- especially metallic items- which are subjected to
reasonably- high frequency cyclic loads. The rate and extent to which oxidation
and corrosion affect any item depend of course on its chemical composition, the
extent to which it is protected and the environment in which it is operating.
Evaporation affects solvents and lighter fractions of petrochemical products.
Two preventive options which are available under these circumstances are
scheduled restoration task and scheduled discard tasks.
Scheduled Restoration Task
As the name implies, scheduled restoration entails taking periodic action to
restore an existing item or component to its original condition ( or more
accurately, to restore its original resistance to failure). Specifically:
‘Scheduled restoration entails remanufacturing
a single component or overhauling an entire
assembly at or before a specified age limit,
regardless of its condition at the time’.
Scheduled tasks are also known as scheduled rework tasks. As the above
definition suggest, they include overhauls which are done at pre-set intervals.
Examples: locomotives, wagons , coaches, signaling point, e.t.c.
The Frequency of Scheduled Restoration Tasks
The frequency of a scheduled restoration task is governed
by age at which the item or component shows a rapid
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In practice, the frequency of a scheduled restoration task can only be determined
satisfactorily on the basis of reliable historical data. This is seldom available
when assets first go into service, so it is usually impossible to specify scheduled
restoration tasks in prior-to-service maintenance programs. However, items
subject to very expensive failure modes should be putting age exploration
programs as soon as possible to find out if they would benefit from scheduled
tasks.
1.8 The Technical feasibility of Scheduled Restoration
The above comments indicate that for a scheduled restoration task to be
technically feasible, the first criteria which must be satisfied are that
There must be a point at which there is an increase in the conditional
probability of failure ( in other words, the item must have a „life‟)
We must be reasonably sure what the life is.
Secondly, most of the items must survive to this age. If too many items fail
before reaching it, the net result is an increase in unanticipated failures. Not
only could this have unacceptable consequences, all the items must survive to
the age at which the scheduled restoration task is to be done, because we cannot
risk failures which might hurt people or damage the environment.
Finally, scheduled restoration must restore the original resistance to failure of
the asset, or at least something close enough to the original condition to ensure
that the item continues to be able to fulfill its intended function for a reasonable
period of time.
Scheduled restoration task are technically feasible if:
There is an identified age at which the item shows a rapid increase in
the conditional probability of failure.
Most of the items survive to that age( all of the items if the failure has
safety or environmental consequences)
They restore the original resistance to failure of the item.
1.9 The Effectiveness of Scheduled Restoration Task
Even if it is technically feasible, scheduled restoration might still not be worth
doing because other tasks may be even more effective.
If a more effective task cannot be found, there is often a temptation to select
scheduled restoration tasks purely on the grounds of technical feasibility. An
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items will receive attention before they need it while others might fail early, but
the net effect may be an overall reduction in number of unanticipated failures.
However even then scheduled restoration might not be worth doing, for the
following reasons.
As mentioned earlier, a reduction in the number of failures is not
sufficient if the failure has safety or environmental consequences,
because we want to eliminate these failure altogether.
If the consequences are economic, we need to be sure that over a period
of time, the cost of doing the scheduled restoration task is less than the
cost of allowing the failure to occur. When comparing the two, bear in
mind that an age limit lowers the service life of any item, so it increases
the number of items sent to the workshop for restoration.
When considering failures which have operational consequences, bear in mind
that a scheduled restoration task may affect operations. In most cases, this effect
is likely to be less than the consequences of the failure because:
The scheduled restoration task would normally be done at a time when it
is likely to have the least effect on production (usually during a so called
production window).
The scheduled restoration task is likely to take less time than it would to
repair the failure because it is possible to plan more thoroughly for the
scheduled task.
If there are no operational consequences, scheduled restoration is only justified
if it costs substantially less than the cost of repair (which may be the case if the
failure causes extensive secondary damage).
1.10 Scheduled Discard Tasks
Again as the name implies, scheduled discard means replacing an item or
component with a new one at pre-set intervals. Specifically:
Reliability - Centered Maintenance
Scheduled discard entails discarding an item
or component at or before a specified age limit,
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These tasks are done on the understanding the replacing the old component with
a new one will restore the original resistance to failure.
1.11 The Frequency of Scheduled Discard Tasks
Like scheduled restoration tasks are only technically feasible if there is a direct
relationship between failure and operating age. The frequency at which they are
done is determined on the same basis, so:
The frequency of a scheduled discard task is governed by
the age at which the item or component shows a rapid
increase in the conditional probability of failure.
In general, there is a particularly widely held belief that all items‟ have a life‟
and that installing a new part before this‟ life‟ is reached will automatically
make it‟ safe‟. This is not always true, so RCM takes special care to focus on
safety when considering scheduled discard tasks.
For this reason, there are two different types of life-limits when dealing with
scheduled discard tasks. The first apply to tasks meant to avoid failures which
have safety consequences, and are called safe-life limits. Those which are
intended to prevent failures which do not have safety consequences are called
economic-life limits.
1.12 Safe-life limits
Safe-life limits only apply to failures which have safety or environmental
consequences so the associated tasks must prevent all failures for example
signaling apparatus communications. In other words, no failures should occur
before this limit is reached.
In practice, safe-life limits can only apply to failure modes which occur in such
a way that no failures can be expected to occur before the wear out zone is
reached.
Ideally, safe-life limits should be determined before the item is put into service.
It should be tested in a simulated operating environment to determine what life
is actually achieved, and a convective fraction of this life used as the safe-life
limit.
There is never a perfect correlation between a test environment and the
operating environment. Testing a long-lived part to failure is also costly and
obviously takes a long time, so there is usually not enough test data for survival
curves to be drawn with confidence. In these cases safe-life limits can be

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established by dividing the average by an arbitrary factor as large as three and
four. This implies that the conditional probability of failure at the safe-life limit
would essentially be zero. In other words the safe-life limit is based on 100%
probability of survival to that age.
The function of a safe-life limit is to avoid the occurrence of a critical failure, so
the resulting discard task is worth doing if it ensures that no failure occur before
the safe-life limit.
1.13 Economic-life limits
Operating experience sometimes suggests that the scheduled discard of an item
is desirable on economic grounds. This is known as an economic-life limit. It is
based on the actual age-reliability relationship of the item, rather than a fraction
of the average age at failure.
The only justification for an economic life limits are cost-effectiveness. In the
same way that scheduled restoration increases the number of jobs passing
through the workshop, so scheduled discard. As a result, the cost-effectiveness
of scheduled discard tasks is determined in the same way as it is for scheduled
restoration tasks.
In general, an economic life-limit is worth applying if it avoids or reduces the
operational consequences of an unanticipated failure, or if the failure which it
prevents causes significant secondary damage. Clearly, we must know the
failure pattern before we can assess the cost effectiveness of scheduled discard
tasks.
1.14 The Technical Feasibility of Scheduled Discard Tasks
Scheduled discard tasks are technically feasible under the following
circumstances:
Scheduled discard tasks are technically feasible if:
There is an identifiable age at which the item shows a rapid increase in
the conditional probability of failure
Most of the items survive to that age (all of the items if the failure has
safety or environmental consequences).
1.15 Failures which are not Age-related
This is due primarily to a combination of variation in applied stress and
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Many failures are caused by increases in applied stress, which are caused in turn
by incorrect operation, incorrect assembly or external damage. (Ideally,
„preventing‟ failures of this sort should be a matter of preventing whatever
causes the increase in stress levels, rather than a matter of doing anything to the
asset.)
Items are made more complex to improve their performance (by incorporating
new or additional technology or by automation) or to make them safer (using
protective devices).
In other words, better performance and greater safety are achieved at the cost of
greater complexity means balancing, with the size and mass needed for
durability. This combination of complexity and compromise:
Increase the number of components which can fail, and also increases the
number of interfaces or connections between components. This in turn
increases the number and variety of failures which can occur.
Reduces the margin between the initial capability of each component and
the desired performance (in other words, the „can‟ is closer to the „want‟),
which reduces scope for deterioration before failure occurs.
These two developments in turn suggest that complex items are more
likely to suffer from random failures than simple items.

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2.0 INTRODUCTION
The introduction of highly reliable sensors, remote condition monitoring
equipment, data acquisition, data analysis, e.t.c will change the form and
functionality of engineering systems and maintenance within any infrastructure
sectors. Infrastructural companies use intelligent today to increase reliability,
safety and reduce cost. It is vital to know that this intelligent infrastructure will
create human factor challenges. In this paper, basic principle of intelligent
infrastructure that cut across sectors (Cloud Computing, Cognitive Computing,
Cense (sensors) Photonic etc) and human factors are discussed.
2.1 CLOUD COMPUTING
“The cloud” is simply a business model for the creation and delivery of
computer resources. The model‟s reliance on shared resources and
virtualization allows cloud users to achieve levels of economy and scalability
that would be difficult in a traditional data center. As such, the cloud is already
transforming how we access and use technology – similar to how adoption of
mass production transformed manufacturing during the Industrial Revolution.
At the same time, enterprises have been cautions about moving their workloads
to cloud services. According to a recent Frost & Sullivan survey, just 9 percent
of enterprises are currently using cloud infrastructure services. Adopters and
non-adopters alike cite concerns about security, loss of control, application
performance, and availability and resilience of workloads (e.g. storage,
corporate applications, test and developments).
So, is the cloud a friend or foe to overtaxed IT departments? The answer
depends heavily on which cloud is chosen. Although the industry refers to
“THE cloud,” it is a misnomer that can cause confusion. In fact, each cloud is
different, with each provider offering unique cloud services and configurations.
Common cloud options include:
Public cloud, in which multiple companies share physical servers and
networking resources hosted in a provider‟s data center.
Private cloud, in which companies do not share resources (although
efficiencies may be realized by hosting multiple virtual applications from
the same company on a single physical server). Private clouds can be
located either in a provider‟s data center or in the company‟s own on-
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Hybrid cloud, In which virtualized applications can be moved among
private and public cloud environments.
For each workload (e.g., storage, corporate applications, test and
development), enterprise IT departments must not only weigh the benefits
and risks of each option from various providers, but also weigh the value
against traditional in-house data center and hosting options.
Scalable, on-demand resources: The ability to launch a cloud
application in minutes, without having to purchase and configure
hardware, enables enterprises to significantly cut their time to market. By
taking advantage of cloud options for “bursting” during peak work
periods, enterprises can also cost-effectively improve application
performances and availability.
Budget-friendly: Cloud computing services require no capital
investment, instead tapping into the operating budget. As many
companies tighten up their processes for approval of capital expenditures,
a service can be easier and faster to approve and deploy.
Utility pricing: The pay-per-use model that characterizes most cloud
services appeals to enterprises that want to avoid overinvesting. It also
can shorten the time to recoup the investment.
Cloud computing exhibits the following key characteristics:
 Cost
 Agility
 Virtualization
 Maintenance
 Security
 Reliability
 Device and location independence
 Application programming interface
 Multitenancy
 Scalability and elasticity
 Performance

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BENEFITS OF CLOUD COMPUTING
 Achieve economies of scale – increase volume output or productivity
with fewer people.
 Reduce spending on technology infrastructure – maintain easy access to
your information with minimal upfront spending. Pay as you go (weekly,
quarterly or yearly) based on demand.
 Globalize your workforce on the cheap. People worldwide can access the
cloud, provided they have an internet connection.
 Streamline processes. Get more work done in less time with less people.
 Reduce capital costs. There is no need to spend by money on hardware,
software or licensing fees.
 Improve accessibility. You have access anytime, anywhere, making your
life so much easier.
 Monitor projects more effectively – stay within budget and ahead of
completion cycle times.
 Less personnel training is needed – it takes fewer people to do more work
on a cloud, with a minimal learning curve on hardware and software uses.
 Minimize licensing new software - stretch and grow without the need to
buy expensive software license a program.
 Improve flexibility – you can change direction without serious people or
financial issue at stake.
 Almost unlimited storage.
 Backup and Recovery.
 Automatic software integration – In the cloud, software integration is
usually something that occurs automatically. This means that you do not
need to take additional efforts to customize and integrate your
applications as per your preferences. This aspect usually takes care of
itself.
Not only that, cloud computing allows you to itemize your options with
great ease. Hence, you can handpick just those service and software
applications that you think will best suit your particular enterprise
 Ease access to information
 Quick development
DISADVANTAGES
1. Technical issues:
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- Outages
- Internet problem
- Technical issues
2. Security in the cloud:
- Surrounding company‟s sensitive information to a third party cloud
service provider.
3. Prone to attack
- Internal hack attack
Three Categories of cloud computing
1. Infrastructure as a service serves a data storage (Iass)
2. Platform as a service (Paas)
3. Software provides as a service (Saas) with access to already created
applications that are operating in the cloud.
Infrastructure as a service (IaaS)
1. In the most basic cloud-service model, providers of IaaS offer computer –
physical or (more often) virtual machines – and other resources. IaaS
clouds often offer additional resources such as images in a virtual-
machine image-library, raw (block) and file-based storage, firewalls, load
balancers. IP address, virtual local area networks (VLANs), and software
bundles.
To deploy IaaS applications, cloud users install operating-system images
and their application software on the cloud infrastructure.
2. Software as a service (SaaS):
Provides use with access to already created applications that are operating
in the cloud.
Cloud providers install and operate application software in the cloud and
cloud users access the software from cloud provider. The clients do not
manage the cloud infrastructure and platform on which the application is
running. This removes the need to install and run the application on the
cloud user‟s own computers simplifying maintenance and support.
3. Platform as a service (PaaS)
Cloud providers deliver computing platform typically including operating
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web server. Application developer can develop and run their software
solutions on a cloud platform without the cost and
complexity of buying and managing the underlying hardware and
software layers. With some PaaS offers, the underlying computer and
storage resources scale automatically to match application demand such
that cloud user does not have to allocate resources manually.
2.2 COGNITIVE COMPUTING
Cognitive Computing refers to the development of computer systems modeled
after the human brain. Originally referred to as an artificial intelligence,
researchers began to use the term cognitive computing instead in the 1990‟s to
indicate that the science was designed to teach computers to think like a human
mind, rather than developing an artificial system. Cognitive Computing
integrates technology and biology in an attempt to re-engineer the brain as one
of the most efficient and effective computer on earth.
Cognitive Computing has its roots in the 1990‟s when computer companies first
began to develop intelligent computer systems. Most of their systems were
limited, however because they could not learn from their experience. Early
artificial intelligence could be taught a set of parameters, but was not capable of
making decisions for itself or intelligently analyzing a situation and coming up
with a solution. Enthusiasm for the technology began to wane, as Scientist
feared that an intelligent computer could never be developed.
However, with major advanced in cognitive science, researchers interested in
computer intelligence became enthused. Deeper biological understanding of
how the brain worked allowed scientists to build computer systems modeled
after their mind and most importantly to build a computer that could integrate
past experience into its systems. Cognitive Computing was reborn with
researchers at the turn of the 21st
century developing computers which operated
at the higher rate of speed that the human brain did.
Cognitive computing integrates the idea of a neural network, a series of events
and experiments which the computer organizes to make decisions; the neural
network contributes to the compiles body of knowledge about a situation and
allows it to make an informed choice and proficiently to work around an
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Cognitive Computing researchers argue that the brain is a type of machine and
can therefore potentially be replicated; the development of neural network was a
large step in this direction.
As the body of knowledge about the brain grows and scientist experiment more
with cognitive computing, intelligent computers are the result from computers
which are capable of recognizing voice commands and acting from them for
example, are used in many navigation systems on board aircraft and boats and
while these systems often cannot handle crises they can operate the craft under
normal conditions.
At the turn of the 21st
century, many researchers believed that cognitive
computing was the hope of near future. By replicating the human brain in
computer form, researchers hope to improve conditions for the human as well as
gaining a deeper understanding of the biological reactions that power the brain.
Computers capable of reason were begin to emerge in the late 1990s with hopes
for consciousness following.
Hear and See with the aid of camera but computers should be able to interpret
images more intuitively from telling whether a picture is on a beech or in a send
box to whether a mole should be examined by a doctor. It‟s also whether will
let our cars and robots operate safely.
Sound Chatting on line and dictation. But by listening closely and adding
context to sounds in the environment a computer may be able to tell you
whether your baby‟s crying means distress, hunger or just a need of attention.
And later some patterns could be detected and shared among a network of
computers to highly predict disasters and weather patterns
Touch:- means more that a touch screen. Your device can feel your finger but
what do you feel? A glass or plastic screen. Researchers are working on
creating tailored vibration that could let you feel textures instead, from clothing
materials to someone else skin.
Smell:- Subtle chemical signals that we take for granted – smoke (locomotive
engine), perfume, wet dog are powerful clues to what is happening in our
surroundings. We all have simple smell sensors, smoke and carbon monoxide
detectors in our homes. But more sophisticated sensor could detect alcohol on
someone (Breath from Locomotive drivers) in a loco, sense early signs of
infections or disease in our driver (to prevent accident) or just let you know the
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Taste:- Computer design a school lunch for family dinner that has been adjusted
for the dietary needs and restrictions from each individual. Obesity from
diabetes, this will be helpful in our hospitals and clinics.
Chemical Sensors in your phones that sense your dinner and suggest a pairing
wire (arrangement of wagon with loads.
2.3 CeNSE
CeNSE or the nervous system of the Earth, consisting of a trillion nanoscale
sensors and activators embedded in the environment and connected via an array
of networks with computing systems, software and service to exchange their
information among analysis engines, storage systems and end users.
2.3.1 SENSORS
A sensor (also called detector) is a converter that measures a physical quantity
and converts it into signal which can be read by an observer or by and
instrument (mostly electronic today) e.g. mercury in glass thermometer,
thermocouple converts temperature into an output voltage which can be read by
voltmeter.
A sensor is a device which receives and responds to a signal when touched.
Sensors sensitivity indicates how much the sensor‟s output changes when the
measured quantity changes. Sensors that measure very small changes must
have very high sensitivities. Sensors have an impact on what they measure.
Sensors need to be designed to have a small or little effect on what is measured;
e.g. if its mercury in a thermometer moves 1cm when the mercury temperature
changes by 1o
C the sensitivity is 1cm/ o
C (it is basically the slope dy/dx
assuming a linear characteristics)
2.3.2 CLASSIFICATION OF SENSORS MEASUREMENT ERRORS
A good sensor obey the following rules
(a) Is sensitive to measured property only
(b) Is insensitive to any other property likely to be encountered in its
application.
(c) Does not influence the measured property.
Sensitivity of a sensor is defined as the ratio between output signal and a
measured property.

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2.3.3 SENSOR DEVIATION
If the sensor is not ideal, several types of deviations can be observed
(a) The sensitivity in practice differs from the value specified (sensitivity
error though the sensor is linear).
(b) Since the range of the output signal is always limited, the output signal
will eventually reach a minimum or maximum when the measured
property exceeds the limits. The full scale range defines the maximum
and minimum values of the measured property.
(c) If the output signal is not zero (0) when the measured property is zero, the
sensor has an offset of bias. This is defined as the output of the sensor at
zero input.
(d) If the sensitivity is not constant over the range of the sensor, this is called
non-linearity. The sensor is called non-linearity sensor.
(e) If the deviation is caused by a rapid change of the measured property
overtime, there is a dynamic error. This error is known as bode plot
showing sensitivity error and phase shift as function of a frequency of a
periodic input signal.
(f) If the output signal slowly changes independent of the measured property,
this is defined as a drift.
(g) Noise is a random deviation of the signal that varies in time.
(h) Hysteresis is an error caused by when the measured property reverses
direction but there is some finite lap in time for the sensor to respond,
creating a different offset error in one direction than in the other.
(i) Digitalization Error – if the sensor has a digital output, the output is
essentially an approximation of the measured property
2.3.4 WAYS OF MINIMIZING THE SYSTEMATIC ERRORS OR RANDOM ERRORS
(1) Calibration Strategy
(2) Noise can be reduced by signal processing such as filtering.
2.3.5 TYPES OF SENSORS
(1) Biosensors – In biomedicine and biotechnology sensors which detect
analytes, in biological components such as cells, protein, nucleic acid or
biometric polymers are called biosensors.

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(2) Nanosensors – these are non-biological sensors even organic for
biological analytes is referred to as a sensor or nanosensor e.g. micro
cantilevers.
2.3.6 WIRELESS SENSOR NETWORK
A wireless sensor network (WSN) consists of spatially distributed autonomous
sensors to monitor physical or environmental conditions, such as temperature,
sound, pressure etc and to cooperatively pass their data through the network to a
men location. The more modern networks are bi-directional, also enabling
control of sensors activity. It can be used for surveillance, industrial and
consumer applications, such as industrial process monitoring and control,
machine health monitoring etc.
The Wireless Sensor Network is build of nodes which can be few hundreds,
thousands where each node is connected to one or sometimes several sensors.
Each sensor network has typically several parts.
(1) Radio transceiver with an internal antenna or external antenna.
(2) Microcontroller, an electronic circuit for interfacing with sensors.
(3) Energy source, usually a battery or an embedded form of energy
harvesting.
(4) Typology of the Wireless Sensor Network vary from a simple star
network to an advanced multi-hop wireless mesh network.
(5) The propagation technique between the hops of the network can be
routing or flooding.
2.3.7 APPLICATION
(1) Area monitoring – In area monitoring, the Wireless Sensor Network is
deployed over a region where some phenomenon is to be monitored e.g.
geo-fencing of gas or oil pipeline.
(2) Environmental/Earth monitoring
(i) Sensing Volcanoes (Wash out) etc
(ii) Oceans, glaciers, forests etc
(3) Air quality monitoring (Loco), Printing press.
The degree of pollution in the air has to be measured frequently in order
to safeguard people and the environment from any kind of damages due
to pollution e.g Gas
(4) Interior Exterior Monitoring
(5) Forest fire detector to protect our cables (Telephone etc).

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(6) Landslide detection System (Civil) makes use of a wireless sensor
network to detect the slight movements of soil and changes in various
parameters that may occur before or during a landslide through the data
gathered, it may be possible to know the occurrence of landslide before it
actually happens.
(7) Water Quality Monitoring- The use of many wireless distributed sensors
enable the creation of a more accurate map of the water status, and allows
the permanent deployment of difficult access without the need of manual
data retrieval.
(8) Natural Disaster Prevention e.g. floods – wireless nodes have
successfully been deployed in rivers where changes of the water levels
have to be monitored in real time.
(9) Industrial Monitoring – machine health monitoring – wireless sensor
networks have been developed for machinery condition based
maintenance (CBM) as they offer significant cost savings and enable new
functionalities. In wired systems, the installation of enough sensors is
often limited by the cost of wiring. Previously inaccessible locations,
rotating machinery, hazardous or restricted areas and in mobile assets can
now be reached with wireless sensors.
(10) Data Logging
2.3.8 CHARACTERISTICS OF WIRELESS SENSOR NETWORK
(1) Power consumption constraints for nodes using batteries or energy
harvesting
(2) Ability to cope with node failures
(3) Mobility of nodes
(4) Communication failures
(5) Heterogeneity of nodes
(6) Scalability to large scale of deployment
(7) Stability to withstand harsh environmental conditions.
(8) Ease of use.
2.4 DATA ACQUISITION
Data acquisition is the process of sampling signals that measures world physical
conditions and converting the resulting samples into digital numeric values that
can be manipulated by a computer.

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It converts the analog waveforms into digital values for processing components
of DAQ or DAS are:
(1) Sensors
(2) Signal conditioning circuitry to convert sensors signals into a form that
can be converted to digital values.
(3) Analog to digital converters, which convert conditioned sensor signals to
digital values.
(4) Software programs using java, LISP, Pascal etc.
(5) DAQ Hardware – interfaces between the signal and a PC. It could be
inform of a modular that can be connected to the computer‟s port
(parallel, serial, USB etc or cards connected to slots (MCS).
INPUT DEVICES
- Analog to digital converter
- Time to digital converter
HARDWARES
CAMAC – Computer Automated Measurement and Control
- Industrial Control
- Industrial USB
- LAN extensions for Instrumentations
- NIM
- Power Lab
- PC1 extensions for Instrumentation
Graphical programming environments include ladder, logic, visual CH, Visual
Basic and Lab view.
2.5 HUMAN FACTOR
2.5.1 GENERAL PROBLEM IN COMMUNICATION
While there are many forms of communication that take place within a railway
system the work reviewed here is limited to communication between drivers,
signalers and trackside workers. Potential problems and misunderstandings in
communication can arise when two people who are separated by location
(driver/signaler) are trying to talk to each other. The problem generally revolves
around a misunderstanding of the intend meaning of the communication.

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Gibson [1] reviewed the literature on communication in general although
incorrectly assumed there was no previous work directly relevant to the rail
industry (see report by Arthur D. Little Ltd. [2]. However, there is a general
need to investigate a wide variety of communication processes within the rail
industry. Gibson identified three sources of communication failure, associated
with the sender, language used, and hardware. Only the first two of these lie
clearly within the Human Factor domain, and are relevant within a number of
situations where railway personnel have to communicate with each other over
the radio or telephone. These include driver-signaler communication and
signaler – PICOP (person in Charge of Possession) or more recently signaler-
COSS (Controller of Site Safety).
2.5.2 DRIVER-SIGNALLER COMMUNICATION
Arthur D. Little Ltd. [2] was commissioned to investigate communication risk
between drivers and signalers. Unlike the drivers and signalers only
communicate with each other when the driver has been brought to a halt at a
signal failed at danger, or in an emergency. Three generic errors were identified
in the scenario where a train has been stopped at a signal at danger, all
encompassed by Gibson‟s framework: the driver mistakenly believes they have
been authorized to pass a signal at danger; the signaler correctly authorizes the
wrong train; the signaler incorrectly authorizes the correct train.
The impact of the first and the third of these errors is potentially catastrophic.
While the report by Arthur D. Little concluded that the current procedures were
sufficient to ensure safe operation at minimal risk, the potential for error (and
hence potential catastrophe) still exists. It is necessary to gain a better
understanding of the mechanism of communication between drivers and
signalers, where potential for error lies and the possible causes of deviation
from correct procedure (e.g., fatigue, distraction). New technology (e.g., in-cab
displays) will inevitably impinge upon the driver-signalers dynamic and attempt
to assess how best to integrate this technology into the rail network from a
Human Factor perspective.
2.5.3 SIGNALLER – PICOPs/COSS
PICOPs, or more recently COSS, take possession of a block of track when
maintenance work required. This requires coordination between the signaler and
COSS in order to ensure the safety of the trackside workers. Halliday explained
the procedures to ensure safety, this information needs to be structured and

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involve the use of correct radio discipline (such as using the phonetic
alphabetic) in order to minimize potential errors in communication such as those
formulated by Gibson [1]. One of the aims of the proposed work is to gain a
deeper understanding of how different functions within the rail system interact
and how these interactions are influenced by the organizational context within
which they take place. Improvement in communication between Signaller and
COSS (and hence the safety of trackside workers) may require alternative
interventions that go beyond simply adhering to radio discipline.
As mentioned above, Roth et al. [3] highlighted the advantages of shared or
“open” radio communication channels where all rail personnel can listen in and
selectively attend to relevant information. In rail network, VHF radio use “open
channels” to allow monitoring of background information keeping personnel up
to dare with what is happening across the system. Hence they can quickly and
appropriately attend to any emergencies.
2.5.4 IMPACT OF FATIGUE ON DRIVER PERFORMANCE AND SAFETY
2.5.4.1 DETECTING FATIGUE STATES IN DRIVERS
Research on fatigue, within the railways as elsewhere, fails to distinguish the
general behavioural outcome (tiredness) and the possible causes of the state. In
particular, fatigue is often ascribed to sleepiness brought about by sleep
deprivation or poor management of shift cycles, and the problem for
performance typically identified with the increased risk of eye closure or actual
sleep. It is important to recognize that mental fatigue can result entirely from
overwork, in the form sustained cognitive operations, even with normal sleep
and well-adjusted shift cycles. Hockey & Meijman [4] have identified at least
three different forms of fatigue-mental, sleep-based and physical, which have
quite different origins requiring different management solutions and
countermeasures. In this paper, some of the issues relating to sleep loss and shift
working, both of which can cause dramatic losses of attention, but largely
ignored the problem of fatigue from sustained demanding cognitive work.
These other effects are more subtle, and their effects therefore more insidious.
They affect information processing strategies by reducing the operator‟s
commitment to high effort attention states. Within rail systems research, the
notion of the train driver as an information processor (rather than someone
engaging in heavy physical work) was introduced over 30years ago [5]. Grant‟s

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suggestions for research on mental fatigue include simultaneous capture of
behavioural observations and physiological measures.
This is the approach taken in major recent programmes aimed at identifying
markers of strain as a basis for predicting performance breakdown in aviation,
and recognizes that risk is related to a progressive effect of the onset of fatigue.
However, over the intervening period since Grant‟s report, little or no work has
been conducted using this methodology. Instead the main emphasis has been on
inferring casual patterns from accident data and shift work patterns. This is still
a viable approach, but an analysis of fatigue requires much better predictors
than can be gained from overt performance measure alone. The use of failsafe
devices (such as ATP of RPWS for SPADs) is an extreme technical response to
failure of the driver‟s concentration, bringing the system to a halt and
necessitating considerable disruption, as well as reducing confidence in the
driver.
2.5.4.2 IMPACT OF SHIFT WORK ON FATIGUE
Shift work is identified as a major contributory factor to fatigue as the internal
body clock fails to adjust to shift work and leads to an accumulation of sleep
loss due to working shifts. A number of the major findings from examining shift
work patterns and their subsequent impact on fatigue are reported by Folkard &
Sutton [6].
During nightshifts one of the major findings is of reduced alertness and
performance due to the internal body clock gearing up for sleep rather than
work. In order to recover from the effects of shifts work the main consensus of
opinion is that the recovery period should allow sufficient time to recover from
accumulation of fatigue. However, while the review by Folkard and Sutton is
extensive it is in contrast with the findings of Wharf [7] who found that while
the consensus is that there is a decrement in performance during the night shift.
In conclusion, information within such a rail system will be distributed widely
across signalers, controllers, drivers and trackside workers as well as potential
in-cab information systems and train operating companies (TOCs). Information
regarding the state of the network will flow between all these agents in the
systems and will be represented not only externally (in the form of signals,
information displays etc) but also internally (in terms of the cognitive
processing of the controller/driver/signaler etc) Fig. 1 shows a simplified
representation of such a system, highlighting the flow of information.

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Figure 1: A model of the network operating environment within which drivers
(D), signalers (S), controllers (C), and maintenance and the implications of this
for the rail network.
Between the various functions/elements of the network operating environment
and how these interactions bear directly upon the effectiveness, reliability, and
safety of the broader rail environment, as well as the costs (financial, resources,
and other) of these outcomes. Adopting an approach of this kind to railway
Human Factor issues will allow a much broader understanding of the processes
taking place, within the system, as well as providing a more supportive
explanatory framework for determining the origins and solutions of the
problems of inefficiencies and error associated with Human Factor. It is based
of these inefficiencies error the intelligence infrastructure is vital to minimize or
reduce human factor.
2.6 PHOTONICS
The science of photonics includes the generation, emission, transmission,
modulation, signal processing, switching, application, as detection sensing of
light. Photonic have both wave and particle nature.
Optical and photonic computing is intertwined to use photonics or light particles
produced by lasers or diodes in place of electrons. Compared to electrons,
photonics have a higher bandwidth. Most research projects focus on replacing
current computer components with optical equivalents resulting in an optical
digital computer system process binary data. The fundamental before building
block of modern electronic computer is the transistor. To replace the electronics
components with “optical transistor” is required. This is achieved using
materials with a non-linear refractive index. In particle, materials exist where
Rail Environment
Effectiveness Reliability
Costs
Safety
Network
Operating
Environment
C
D
M
S

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the intensity of incoming light, the intensity of light transmitted through one
material in a single manner to the voltage response of an electronic transistor.
Such an “optical transistor can be used to create optical logic gates which in
turn are assembled into the higher level components of the computer Central
Processing Unit (CPU)
Photonic logic is the use of photons (light) on logic gates (NOT, AND, OR,
NAND, NOR XOR, YNOR). Switching is obtained using non linear optical
effects when two or more signal is combined.
2.7 NON VOLATILE MEMORY
F-RAM products combine the non –volatile data storage capability or ROM
with the benefits of RAM, which include a high number of read and write
cycles, high speed read and write cycles, and low power consumption. FRAM,
product line features various interfaces and densities which include industry
standard serial and parallel interface, industry standard package types, as
4kilobites, 16kilobites, 64kilobites, 25kilobites, 1megabite, 2megabites and
4megabit densities.
F-RAM performs read and write operation of the same speed, there are no
delays as before in non-volatile. Floating gate memories have long write delay
of 5 seconds. FRAM writes in nano seconds essential in application like auto
safety system.
FRAM offers virtually unlimited write endurance, which means it does not wear
out like other nonvolatile memory devices floating gate devices experience a
hard failure and stop writing in as little as IE5 cycles, making them unsuitable
for high-endurance applications.
FRAM operates without a change pump, enabling low power consumption of
floating gate devices, demand high voltage during write operations. FRAM
writes at the native voltage of the manufacturing process; 5V or even less or
more advanced process.
2.8 SCALABILITY STORAGE
Scalability storage is the ability of a system, network or process to handle a
growing amount of work in a capable manners or its ability to be enlarged to
accommodate that growth. A system whose performance improves after adding
hardware, proportionality to the capacity added, is said to a scalable system.

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3.0 DATA ANALYSIS
Data analysis is a process of inspecting, cleaning, transforming and modeling
data with the goal of highlighting useful information, suggesting conclusions
and supporting decisions making.
Data analysis can also be defined as the process of finding the right data to
answer questions, understanding the processes underlying the data, discovering
the important patterns in the data and then communicating the results to have
the biggest possible impact.
In this paper l will focus on how the different cadres of employees in an
organizations that is junior staff, middle level managers, senior managers, chief
executive officers/managing directors, board members and chairman of
companies/organizations can make use of data in taking decisions, consequently
the section of this paper will focus on Management Information System.
3.1 WHAT IS MANAGEMENT INFORMATION SYSTEM?
A Management Information System (MIS) is a computer based system that
provides the information necessary to manage an organization effectively.
Management Information System (MIS) is designed to enhance communication
among employees, provide an objective system for recording information and
support the organization‟s strategic goals and direction.
The system entails three primary resources. Information, Technology and
people.
3.2 OBJECTIVE OF MANAGEMENT INFORMATION SYSTEM
The objective of a Management Information System (MIS) system is to provide
useful information, data and analysis remains consistent but the features and
uses are customizable to suit the preferences and needs of every business,
individual or government for example, a government, without a profits focus,
can install a Management Information System (MIS) system that personally
tracks “customer” (auto licenses) as relates to their budgets.

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3.3 FUNCTION
The function of Management Information System (MIS) is to identify, manage,
and manipulate data (or groups of data) in a fashion that enables good decision
making.
In the first half of the 20th
century, business manages information on paper, with
detailed filing systems and calculated reports. Cotemporary Management
Information System (MIS) involve one or more computers, working in concrete,
to achieve the stated goals of an organization. The function is always the same,
but the desired results fluctuate with the specific goals and needs of individual
organizations. Since the universal language of commerce is numbers, using the
incredible speed of computers, Management Information System (MIS) achieve
their function amazingly well.
3.4 TYPES
There are many types (and sub types) of management information systems as
there are business functions. Some of the most popular types of Management
Information System (MIS) are as follows:
Customer relationship management
Marketing, particularly target marketing efforts, directed of specific
groups of potential customers or selling niche products financial
managements.
Financial management
Strategic plan development
Inventory management systems
Optimal investing strategy creation
Projected sales volume
Projected operating expenses and cost control.
Other types of Management Information System (MIS) calculate project tax
revenue for governments‟ statistical evaluations of all types for business,
researchers and universities scientific purposes in all discipline; and cost/benefit
relationship for decision-making purpose.

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3.5 BENEFITS
The benefits of Management Information System (MIS) to businesses
governments, scientists, universities, students, non-profits and all other entities
are diversified. Some examples of the most often realized benefits include the
following:
Implementation of Management by objectives (MB) techniques:
Management Information System (MIS) allows all participants both
management and staff, to view, analyze, and interpret useful data to set
goals and objectives.
Generates competitive advantage: Business succeeds or fail based on
how they handled competitive challenges. Management Information
System (MIS) if implemented properly provided a wealth of information
to allow management to construct effective plans to meet, and beat, their
competitors.
Fast reaction to market changes: The victory often goes to the quick,
not necessarily the best; Management Information System (MIS) can
deliver facts, dash friends to businesses with lighting speed. Having this
information allows companies to react quickly to market changes,
regardless of the type (positive or negative of volatility.
3.6 CLASSIFICATTION OF MANAGEMENT INFORMATION SYSTEM
(MIS)
3.6.1 TRANSACTION PROCESSING SYSTEMS
Transaction processing systems are designated to handle a large volume of
routine, recurring transactions. Banks use them to record deposits and payments
into accounts. Supermarkets use them to record sales and track inventory.
Managers often use these systems to code with such tasks as payroll, customer
to suppliers.
3.6.2 OPERATIONS INFORMATION SYSTEM
Operations information systems were introduced after transaction processing
system. An operation information system gathers comprehensive data, organizes
it and summarizes in a form that is useful for managers. These types of systems
access data from transaction processing system and organize it into a usable
form. Managers use operations information system to obtain sales, inventory,
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3.6.3 DECISION SUPPORT SYSTEM (DSS)
A decision support system (DSS) is an interactive computer system. They can
be used by managers without help from computer specialists. A DSS provides
managers with the necessary information to make informed decisions. A DSS
has three fundamental components:
Database management system (DBMS), which stores large amounts of data
relevant to problems the DSS has been designed to tackle, model based
management system (NBMS), which transforms data from the DBMS into
information that is useful in decision making and dialog generation as
management system (DGMS), which provides a user friendly interface between
system as the managers who do not have extensive computer training.
3.6.4 EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE
Expert systems and artificial Intelligence use human knowledge captures in a
computer to solve problems that ordinarily need human expertise mimicking
hum expertise and intelligence requires the computer to do the following:
recognize, and learn from experience. These systems explain the logic of their
adire to the user; hence, in addition to solving problems they also can serve as a
teacher. They use flexible thinking processes and can accommodate new
knowledge.

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4.0 INTELLIGENT INFRASTRUCTURE SYSTEMS IN RAIL INDUSTRY
Railway development projects were put in place to respond to the shortage in
infrastructural resources, in order to meet growing demand for capacity (Dft,
2010). Crainic et al., (2009) have pointed out that building new
infrastructure to fulfill these demands is no longer an option. A more
optimal approach to infrastructure maintenance is therefore necessary and that
is to move from breakdown maintenance (fixing after failure) and time-
based preventive maintenance (fixing following a periodical inspection) to
predictive maintenance (fixing before failure).
Reliable sensors, sophisticated algorithms and advanced surveillance systems
have enabled live monitoring of the infrastructure in complex work
environments. This architecture has different names in various industries, such
as Condition Monitoring Systems in power plants (Hameed et al., 2009):
Condition Based Maintenance in mechanical systems (Jardine et al.,2006),
Structural Health Monitoring in aviation (Buderath & Neumair,2007)
Pervasive Healthcare in medical systems (Drew & Westenskow,2006).
Integrated information systems to support maintenance and monitoring have
long been used in different industries and domains. Some examples include:
manufacturing (Lau, 2002; Jardine et al., 2006), undersea and petro-chemical
(Strasunskas, 2006), space exploration (Park et al.,2006), civil
infrastructure (Aktan et al., 1998; Aktan et al., 2000), water and sewage
(Adriaens, et al., 2003), defence (Jones et al., 1998) and transportation (King,
2006; Lyons and Urry , 2006; Ollier, 2006; Khan, 2007; Blythe and Bryan,
2008).
Intelligent infrastructure is mainly considered as a means of centralizing and
integrating the support that is currently provided to infrastructure maintenance
by monitoring the condition of assets remotely. Potential failure or unnecessary
fixed-term replacements will then be prevented by providing relevant
information to the maintenance function. Another main use of intelligent
infrastructure in the railway is to facilitate optimal asset maintenance.
Currently, maintaining assets is performed through fixed schedules. This is
time consuming, costly and risky approach can be replaced by analyzing real-
time asset information and attending to track-side equipment only when
necessary. Therefore, intelligent infrastructure in rail was introduced to move
the railway, and especially its maintenance and engineering activities, from a

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„find and fix‟ mentality to „predict and prevent‟, and potentially to „design and
prevent‟ (Bint, 2008).
Figure 4.1 below shows a visualization of intelligent infrastructure on the rail
network. This is a simplified version of the model that NR applied to guide the
implementation of a pilot intelligent infrastructure system (Network Rail,
2009). The oval on the left hand side of the figure shows some of the
infrastructural assets (e.g. embankment, point, signal, level crossing, and track)
that can potentially benefit from a more optimal maintenance regime. Loggers
or other data acquisition devices collect information regarding these assets
(i.e. remote condition measuring). Data presented in current systems, such as
RCM systems, are presented in an integrated database. A strategic
infrastructure solution is then required to extract the optimum information and
present it to the appropriate operator

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Examples of rail
Infrastructural
assets
Acquire data through
new data acquisition device or loggers
Rail track
Embankment strategic intelligent
Infrastructure
Solution
Level crossing
n
Point machine
Existing proprietary applications
Signal
FIGURE 4. 1: SIMPLIFIED HIGH LEVEL MODELLING OF INTELLIGENT
INFRASTRUCTURE IN NETWORK RAIL

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Capturing data and attributes from domain components;
attributes can refer to the environment in which the
component is located, its age, type, etc.
Jardine et al., (2006) pointed out that, in the first stage (data acquisition), two types
of data should be considered: event data and condition monitoring data. The event
data looks into what happened (e.g. breakdown, overhaul) and the condition
monitoring data measures the health status of the infrastructure. However, they
suggest that, despite the importance of collecting event data, it is often neglected by
developers who wrongly assume that the recording of only condition monitoring
data will suffice.
Patterns of component behaviour are produced. This
can be achieved through studying historical data or
experimental findings.
This stage includes data cleaning, to ensure that the data is relevant and error free,
along with data analysis. The data analysis is usually conducted through algorithms
and mainly includes signal processing, image analysis, time-domain analysis and
frequency domain analysis (Jardine et al., 2006).
Generate system diagnostics and prognostics, followed by the
analysis of recognized patterns.
In this stage, sophisticated algorithms are used to assist operators in diagnosing
faults and suggest rectifying procedures. Although much has been done in
developing and analyzing diagnostics information (Hameed et al., 2009), it is more
difficult to develop rectifying procedures and present operators with a number of
options. This can be related to the difficulty in understanding the behaviour of
assets and, in particular, lack of appropriate understanding of the situational
information associated with the failure.
 Transfer diagnostics and prognostics to relevant operator
In order to ensure effective implementation of an intelligent infrastructure system,
data obtained from the infrastructure must be transformed into useful information,
as well as being exploited in the optimal way (Crainic, 2009). Failure to define the
correct purpose for the data may result in the system presenting too little
information or overloading the operator with inappropriate information. Hence, it

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is important to realize what level of detail is required. For instance, does the
operator require a simple binary (working / failed) assessment of the status of an
asset, or a sufficiently detailed measurement? Moreover, the operator should know
the effect of the measured condition on the overall run of the service in order to
predict potential failures and behaviours of the asset in the future.
Update the pattern log with new conditions.
This is the stage where information and knowledge captured in earlier stages are
now fed back to the system (e.g. using artificial intelligence, artificial neural
networks, or simply operator‟s feedback). However, eliciting knowledge from real-
world maintenance practice is not very straightforward and it is not easy to
document it digitally for future use (Jardine et al., 2006). One solution to facilitate
and support this feature is to develop a robust understanding of problem solving
and fault finding practice as well as operators‟ knowledge and information
requirements.
Aktan et al., (1998) conducted exploratory research to investigate the issues
associated with remote sensing of the asset conditions during live operations while
developing highway bridges. They confirm that, in doing so, a wide knowledge of
advanced sensors, communication and information technology, state parameters,
environment, deterioration mechanism and performance measures is required. Such
intelligent infrastructure systems should be able to:
Sense the definitive features on the piece of infrastructure
Assess the condition by analyzing the information captured
and performance criteria
Communicate the findings through appropriate interfaces
Learn from infrastructure condition patterns
Decide the optimum course of action
From this, they suggested three main factors to be considered in order to develop an
effective intelligent infrastructure system:
1. The knowledge required for diagnosing problems
2. The technology necessary for transmitting the knowledge
3. The people who will work with the technology.

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From the three factors identified by Adriaens, et al., (2003), technology is the least
problematic one especially with the advent of highly sophisticated algorithms,
artificial intelligence application and neural network algorithms, etc (Adeli &
Jiang, 2009). The other two factors (i.e. knowledge and people) are the most
problematic.
Therefore, one of the most important challenges facing the success of an intelligent
infrastructure system is the management of information within the system.
As mentioned in the previous sections, railway control systems have enabled the
control of large areas with complex intertwined components and have
revolutionised the look and functionality of control systems. It seems that
intelligent infrastructure aims to improve this functionality by managing and
integrating the existing technologies, thereby assisting operators to make more
informed decisions. However, the review of the potential domains of intelligent
infrastructure suggests a number of challenges that will be potentially even more
problematic with the introduction of intelligent infrastructure systems. These
include:
 Information overload
 Multi-agent control
 Alarm handling
4.2 INFORMATION OVERLOAD
One of the recurring questions in designing dynamic control environments such as a
railway control is whether more information is better. Process and transport control
systems collect data remotely from complex environments, enabling operators to
monitor and intervene if necessary (Sheridan, 1992).
Within railways, advanced technologies, such as the switch from manual control to
automation, the introduction of highly reliable sensors and the application of
sophisticated algorithms, have increased the volume of data available to operators
in their decision making. While this creates opportunities for more efficient
control, it also places an increasing cognitive demand on the operator.
Similar research in complex environments has shown that operators are
disadvantaged by the provision of multiple sources of information as well as
multiple opportunities for actions (Omodei et al., 2005; Seagull et al.,

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Chapter Four
2001). Therefore, there should be a balance between the number of tasks for which
operators are responsible and the amount of information made available to them.
Cummings and Mitchell (2007) noted that there are limits to how much information
operators can keep track of before they demonstrate degraded performance.
4.3. MULTI-AGENT CONTROL
Control environments are moving more and more towards integration and
centralization. Therefore, the information generated in one control room will be
used in another. For example, in railway maintenance, the information presented to
the maintenance operator in the national control centre will be used by track
workers. Moreover, different operators are responsible for different aspects of a
decision making task. The cooperation between personnel with different roles
within the control environment is a key aspect of the success of these control
processes.
Two aspects of the work that should be analyzed in order to understand and
inform these multi agent control systems are:
1. What are the roles involved with these systems?
2. What are the goals and objectives of each of those roles?
For example, in railway intelligent infrastructure, if we assume that asset failure
prevention is the ultimate goal, the information provided by the system will be used
differently by different people (from the track worker on a railway site to the
operator in a control room and ultimately by the policy maker).
Hoc (2001) looked into the concept of cooperation between different agents in the
dynamic environment (e.g. interface, operators, etc.). He has recommended that one
way of developing an understanding of levels of coordination is to decompose one
goal (i.e. solving a problem) into its sub-goals and look into the activities of different
agents during each sub-goal. It is also important to understand the boundaries
between the different roles of intelligent infrastructure and to support each role
accordingly.
Information presented to operators should be in a cohesive way that matches their
mental model and cognitive processing that is necessary for effective decision
making.

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Chapter Four
4.4 ALARM HANDLING
Technical advances in designing complex control settings allow huge amounts of
data to be collected from various remote sensors. Presenting all of these data
seems to be both impossible and unreasonable. Alarms are then introduced to
assist human operators in managing these numerous sources of data.
When designing alarms, the following factors need to be considered in relation to
auditory displays include: appropriate level of sensitivity, contrast between the
audible siren and background noise, perceived urgency of the task and the
alarm respectively as well as multiple alarms (Robinson et al., 2006). These
factors, in their wider sense, refer to the two perspectives introduced by Woods
(1995): how informative the alarm is and alarm perception.

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Chapter Five
5.0 INFORMATION PROCESSING PARADIGM
The information processing paradigm is the outcome of a linear and
fragmented view of human-computer interaction, in which humans are
seen as information processors and it is possible to explore their activities
through investigating the information inputs (stimulus) and outputs
(response) (Rasmussen, 1986).
FIGURE 5.1: MODEL OF COGNITIVE ACTIVITIES OF RAILWAY
SUPERVISORY CONTROLLERS, TAKEN FROM EZZEDIN &
KOLSKI (2005)

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Chapter Five
Information processing paradigms have been widely used to facilitate studies
associated with decision making, problem solving, as well as alarm handling.
Models of alarm handling were introduced to guide the exploration of the
various stages conducted by operators when handling alarms; very early ones
include that of Lees (1983), which has three stages: detection, diagnosis and
correction. A model suggested by Rouse (1983) also has three stages:
detection, diagnosis and compensation. Although other models are
available, as noted by Stanton (2006), there is little evidence that these
models reflect a real life alarm handling environment. To overcome this
uncertainty, Stanton et al (1998) identified a sequence of activities that are
initiated by the generation of an alarm (Figure3.7)
This model includes two sets of events: routine and critical. When an alarm is
generated, operators observe the reported warning and accept if it is
genuine. Based on their understanding of a failure, operators might
analyze, correct, monitor, or reset the alarm. If the cause of the failure is
unknown, then the operator will conduct a series of investigations to
diagnose the problem. Finally, they monitor the situation to ensure that the
abnormality is dealt with (Stanton, 2006).
CONDITION MONITORING AND DIAGNOSTICS OF MACHINES DATA
PROCESSING, COMMUNICATION AND PRESENTATION.
PART 1
GENERAL GUIDELINES
1. SCOPE
This part of ISO 13374 establishes general guidelines for software specification
related to data processing, communication, and presentation of machine condition
monitoring and diagnostic information.
NOTE: Later parts of ISO 13374 (under preparation) will address specific software
specification requirements for data processing, communication, and presentation.
2. DATA PROCESSING
2.1 OVERVIEW

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Chapter Five
Relevant data processing and analysis procedures are required to interpret the data
received from condition monitoring activities. A synergistic combination of
technologies should establish the cause and severity of possible faults and provide
the justification for operations and maintenance actions in a pro-active manner.
A data processing and information flow of the type shown in figure1 is
recommended either on a manual or automatic basis, in order to implement
condition monitoring successfully. The data flow begins at the top, where
monitoring configuration data are specified for the various sensors monitoring the
equipment, and finally results in actions to be taken by maintenance and operations
personnel. As the information flow progresses from the data acquisition to advisory
generation, data from the earlier processing blocks need to be transferred to the next
processing block and additional information acquired from or sent external systems.
Similarly, as the data evolve into information, both standard technical displays and
simpler graphical presentation formats are needed. The flow progresses from data
acquisition to complex prognostic tasks, ending in the issuance of advisories and
recommended actions (one of which may be modification of the monitoring process
itself.
5.2 DATA-PROCESSING BLOCKS
5 .2.1. MACHINE CONDITION ASSESSMENT PROCESSING BLOCKS
Machines condition assessment can be broken into six distinct, layered processing
blocks. The first three blocks are technology-specific, requiring signal processing
and data analysis functions targeted to a particular technology. The following are
some of the most commonly used technologies in condition monitoring and
diagnostics of machines:
Shaft displacement monitoring;
Bearing vibration monitoring;
Tribology-based monitoring;
Infrared thermograhic monitoring;
Performance monitoring;
Acoustical monitoring;
Motor current monitoring;

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Chapter Five
Sensor/Transducer/Manual Entry
Figure1- Data-Processing and Information-flow blocks
The technology-specific blocks and the functions they should provide are as follows.
a) Data Acquisition (DA) Block: Converts an output from the
transducer to a digital parameter representing a physical quantity and
related information (such as the time, calibration, data quality
collector utilized, sensor configuration).
External
systems
data archiving
and
block
configuration
Technical
displays and
information
presentation
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation
(AG)

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Chapter Five
b) Data Manipulation (DM Block): performs signal analysis, computes
meaningful descriptors, and derives virtual sensor readings from the
raw measurements.
c) State Detection (SD Block); facilitates the creation and maintenance
of normal baseline “profile” searches for abnormalities whenever new
data are acquired, and determines in which abnormality zone, if any,
data belong (e.g.). “alert” of alarm).
The final three blocks normally attempt to combine monitoring technologies in
order to assess the current health of the machine, predict future failures, and
provide recommended action steps to operations and maintenance personnel.
These three blocks and the functions they should support are as follows.
d) Health Assessment (HA) Block: Diagnoses any faults and rates the
current health of the equipment or process, considering all state
information.
e) Prognostic Assessment (PA) Block: Determines future health states
and failure modes based on the current health assessment and
projected usage loads on the equipment and/or process, as well as
remaining useful life predictions.
f) Advisory Generation (AG) Block: provides actionable information
regarding maintenance or operational changes required to optimize
the life of the process and/or equipment.
2.2.2 TECHNICAL DISPLAYS
To facilitate analysis by qualified personnel, relevant technical displays showing
data such as trends as well as associated abnormality zones are necessary. These
displays should provide the analyst with the data required to identify, confirm or
understand an abnormal state.
2.2.3 INFORMATION PRESENTATION
It is important that the data be converted to a form that clearly represents the
information necessary to make corrective-action decisions. This may be done in a
written format, numerically in order to demonstrate magnitudes, graphically in order
to show trends, or a combination of all three.
Chapter Five

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The information should include pertinent data describing the equipment or its
components, the failure type or fault, an estimate of the severity, a projection of
condition and, finally, recommended action. Cost and risk factors may also be
displayed.
2.2.4 EXTERNAL SYSTEMS
Retrieval of previous work histories from the maintenance system and precious
operational data (state/stop/loads) from a process-data historian is important in the
assessment of machinery health. After a health assessment is made, the maintenance
action to be taken may range from increasing the frequency of inspection to repair or
replacement of the damaged machinery or component. The effect on operations may
be an adjustment of operating procedures or a request to shutdown the equipment
immediately. This need for rapid communication to the maintenance and operational
system requires software interfaces to maintenance management system and
operational control systems. These interfaces are useful in order to communicate
recommended actions in the form of maintenance work requests and operational
change requests.

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