Vol 17 No 1 - January June 2017

ISSN: 1694-2507 (Print)
ISSN: 1694-2108 (Online)
International Journal of Computer Science
and Business Informatics
(IJCSBI.ORG)
VOL 17, NO 1
JANUARY-JUNE 2017

Table of Contents VOL 17, NO 1 JANUARY-JUNE 2017
Development and Exploitation of Software Complex of Virtual Community Life Cycle Organization ....1
Olha Trach and Solomia Fedushko
Systematic Review of Persuasive Health Technology Design and Evaluation Models..........................12
Kasali Funmilayo, Kuyoro Afolashade and Awodele Oludele
Designing Condition-based Maintenance Management Systems for High-Speed Fleet .......................28
Gopalakrishna Palem
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International Journal of Computer Science and Business Informatics
IJCSBI.ORG
ISSN: 1694-2108 | Vol. 17, No. 1. JANUARY-JUNE 2017 1
Development and Exploitation of
Software Complex of Virtual
Community Life Cycle
Organization
Olha Trach and Solomia Fedushko
Social Communications and Information Activities Department,
L'viv Polytechnic National University,
Ukraine, L'viv, S. Bandera Street 12
ABSTRACT
This paper presents development of software complex of virtual community life cycle
organization. The investigations stages and directions of virtual community life cycle,
introduction of indicators of tasks directions of virtual community life cycle organization,
determination of criticality of indicators of virtual community life cycle organization,
definition of socially-oriented risk of virtual community life cycle are enabled to develop a
software complex of virtual community life cycle organization. Software complex of virtual
community life cycle organization consists of three levels: management level, level of
performance, level of databases and information resources. Developed software tool
"Virtual organization of community life cycle" is an important and actual task. The
software tool is the basis for increasing the efficiency of creating a virtual community and
improving its functioning throughout its existence. The attainment of the objectives and the
development of virtual communities based on software complex of virtual community life
cycle organization are developed in this studies.
Keywords
Virtual community, lifecycle, directions, indicator, manager, software tool
1. INTRODUCTION
Virtual communities have become an extremely popular phenomenon, and
with each passing day their number is growing, and existing communities
are rapidly developing. As a result, the creation of virtual communities grew
into a separate type of professional activity, and virtual communities
become a certain type of project. However, observations showed that often
treating to virtual community as a project (with clear goals, objectives,
sequence of steps) they were failures. That is because the virtual
communities treated as a traditional project, but there are things that go
beyond the traditional project. In this paper, we investigate the appearance
of the project by type of the virtual community, its features complex of
works associated with the project activity of the virtual community.

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2. RELATED WORKS
Considering the rapid development of virtual communities actual are the
following directions of scientific research:
 Safety and information wars in virtual communities (propaganda or
disinformation spreading) [1];
 creation and management of virtual communities [2, 3];
 creation and management of information content of the virtual
community [4];
 users attraction and monitoring of virtual community, socio-
demographic characteristics users of the virtual communities [5, 6];
 marketing and advertising in the virtual community [7];
 research of virtual community life cycle organization [8-11].
Monitoring of users, content, marketing component help to highlight the
directions for virtual community life cycle organization. Also, these studies
are needed to allocate the parameters of the indicator directions for tasks of
virtual community life cycle organization. Research on security of virtual
communities help еo form socially-oriented risks with virtual community
life cycle organization.
However research on virtual community life cycle organization is
incomplete and imperfect, are sporadic. The researchers representing only
conceptual models of virtual community life cycle, models frequently
consist of only four elements.
3. BACKGROUND STUDY
Any project consists of a sequence of stages, which have title and certain
characteristics, namely project life cycle. A virtual community life cycle –
execution of tasks and it stages by time period, from planning to create a
community to full its liquidation. For qualitative creation and management
of virtual community structured life cycle, includes the following steps:
planning, analysis, designing, development, testing, implementation,
exploitation, comprehensive verification, conservation community,
liquidation. And dedicated stage, characteristics of the virtual community,
was named virtual community life cycle directions [12]:
 user direction (processing of activities of participants and users of
virtual communities);
 informational direction (content of virtual community);
 resource direction (technical and technological support for creating
and managing of virtual community);

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 reputational direction (support of content of activity and its virtual
community ranking, positioning of the virtual community).
Virtual community life cycle directions – is the focus of the implementation
stage of virtual community life cycle organization, resulting tasks
performed during all directions.
The process of implementation stage in four directions is distributed nature,
separate components that perform performers of virtual community life
cycle organization [13]:
 manager of creation of virtual community – a specialist responsible
for success of virtual community life cycle organization;
 analyst – responsible for analyzing the data in a certain field;
 performer of stages – responsible for the implementation stages;
 performer of directions – responsible for execution of tasks
directions.
For effective implementation of stages of virtual community life cycle
happening execution of tasks directions. For perform the tasks directions of
virtual community life cycle organization introduced indicators [14]:
 Planned indicator (IndPlan), which consists with reference
indicator (IndReference) and indicators analysis of the subject area
of virtual community (IndAnalysis);
 Real indicator (IndReal), which consists with input indicator (IndIn)
and output indicator (IndOut).
Highlighted basic indicators of tasks directions of virtual community life
cycle organization, which are the primary data of the community:
 indicators of the user direction Ind_Us(Taski);
 indicators of the informational direction Ind_Inf(Taski);
 indicators of the reputational direction Ind_Rp(Taski).
Tasks of resource direction performed without indicators а based on
technical and technological characteristics. Set of indicators of tasks
directions of virtual community life cycle organization is show next (Figure
1).

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Informational
direction
User direction
Resource
direction
Reputational
direction
 indicator of the number
of participants
 age indicator
 geographical indicator
 lingual indicator
 writing activity indicator
 reading activity
indicator
of content
 posts indicator
comments
multimedia
 indicator of theme
content
 indicator of uniqueness
content
 indicator of
responsibility
 indicator of
protection trolling
 indicator of
communicative
aggression
INDICATORS OF DIRECTIONS TASKS OF THE ORGANIZATION OF
LIFE CYCLE OF VIRTUAL COMMUNITY
 technical and
technological data
Figure 1. Indicators of tasks directions of virtual community life cycle organization
According to organizational management algorithm for the effective
implementation of stages of virtual community life cycle happening perform
the tasks directions, as shown in the following scheme (Figure 2):
Forming the task
Choosing
direction
Choosing
indicators
Performing
tasks
Report About
perform the task
Forming planned
indicator
Determining
criticality of
indicators
Informational
direction
Reputational
direction
Getting real
indicator
Resource direction
User direction
Figure 2. Formation of the tasks directions
The process of forming of planned indicator to perform tasks directions of
the organization of life cycle of virtual community [14] is described in the
following scheme (Figure 3):
IndAnalysis
IndEtalon
Indicators analysis of
the subject area of
virtual community
Reference
indicator
Determining
zone
IndPlan
Indicators
Figure 3. The scheme of forming planned indicator of virtual community life cycle
organization

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The process of determination of the criticality indicators to perform tasks
directions of the organization of life cycle of virtual community [14] is
described in in the following scheme (Figure 4):
User direction
Informational
direction Processing
indicators
Reputational
direction
Resource
direction
Critical
Non-critical
Indicators
Important
Forming indicators to
form the tasks of
virtual community
life cycle
organization
Indicators
Figure 4. The scheme of determination of the criticality indicators to perform tasks
directions of the organization of life cycle of virtual community
Creating a virtual community has its own specifics and in risks in particular.
Therefore, the description of virtual community life cycle organization
highlighted a number of socially-oriented risks and described in [15],
namely:
 the risk of a negative-minded audience ;
 risk of reducing the quality of content;
 the risk of anti-legal materials and activities of community;
 the risk of losing control of the community.
Based on conducted researches, highlighted the features of virtual
communities and developed a software and algorithmic complex of virtual
community life cycle organization (Figure 5).
4. METHODOLOGY
The structure of the program complex of virtual community life cycle
organization shown next.

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PROGRAM COMPLEX OF VIRTUAL COMMUNITY LIFE CYCLE ORGANIZATION
COMPONENT
OF FORMING
PLANNED
INDICATOR
TASKS
FORMING
COMPONENT
COMPONENT OF
CERTAINTY
CRITICALITY
INDICATORS
COMPONENT OF
FUNCTIONAL-
NETWORK
MODEL
DATABASE OF
REPORTS
PROTECTION
FROM RISKS
COMPONENT
TEAM "PROJECT MANAGEMENT"
TEAM «DIRECTIONS PERFORMERS»
MANAGEMENT
LEVEL
LEVEL OF
PERFORMANCE
PROCESSING
OF TASKS
COMPONENT
USER DIRECTION
PERFORMER
REPUTATIONAL
DIRECTION
PERFORMER
RESOURCE
DIRECTION
PERFORMER
INFORMATIONAL
DIRECTION
PERFORMER
ANALYSTMANAGER
DATABASE OF
INDICATORS
VIRTUAL
COMMUNITY
SITES
SOCIAL
NETWORKS
LEVEL OF DATABASES
AND INFORMATION
RESOURCES
INFORMATION RESOURCES
Figure 5. Structure of the program complex of virtual community life cycle
organization
The structure of the program complex of virtual community life cycle
organization consists of three levels:
 management level;
 level of performance;
 level of databases and information resources.
4.1 Management level
Fulfills duties of this level team of performers, which provide process of
implementation of virtual community life cycle organization. The
functionality of this level are used throughout the life cycle of the virtual
community. Management level consists of two teams and six workplaces.
Team "Project Management" – team of performers, which provide process
of implementation of virtual community life cycle organization. Performers
of this team engaged in formulation of tasks, analysis, adoption key
decisions of virtual community life cycle organization. Team "Project
manager" has two workplaces "Manager" and "Analyst". Workplaces
"Manager" responsible for virtual community life cycle organization. The
main functions of the workplace is to create tasks, distribution of tasks
between performers team "Artists", documentation and acceptance the key
decisions regarding the organizational process of creating of the virtual
community. Workplace "Analyst" responsible for analyzing the data in a
certain field. Specialist of the workplace is the analyst. Due to project
constraints of the virtual community, analysts may be several. As the analyst
is not a particular profession, by specialty analyst is divided into: marketing
analyst, systems analyst, financial analyst and others.

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Team «Directions performers» – team of performers, which responsible for
implementing the tasks directions of virtual community life cycle
organization. Workplaces correspond to directions of virtual community life
cycle organization, namely: user, informational, reputational, resource. The
workplace can be attached several performers depending on the specific
virtual community created. «User direction performer» responsible for
activities related to the participants of the virtual community.
«Informational direction performer» responsible for activities related to
the information content of the virtual community. «Reputational direction
performer» responsible for activities associated with maintaining the
reputation of the virtual community. «Resource direction performer»
responsible for activities related to technical and technological
characteristics of the virtual community.
4.2 Level of performance.
Level of performance of virtual community life cycle organization
responsible for executive components of virtual community life cycle.
Performers of component of this level is a manager, analyst and directions
performers.
«Component of functional-network model». Functional-network model of
virtual community life cycle organization based on Petri net. The model
corresponds to the functioning of the virtual community [17].
«Tasks forming component». Component responsible for structuring the
process of forming tasks of virtual community life cycle organization.
Manager creates the task and delegate to performers of tasks directions of
virtual community life cycle organization.
«Component of certainty criticality indicators». For quick and efficient
perform the tasks, according to the purpose and objectives of a virtual
community, appears necessity to define criticality of tasks indicators of
virtual community life cycle organization. To determine the criticality of
indicators necessary real and planned indicators of tasks performs of virtual
community life cycle organization.
«Component of forming planned indicator». Planned indicators are close
to ideal indicators and have an important role (especially when there is a
critical comparison). Generates planned indicator manager of virtual
community. To forming qualitative planned indicator of virtual community
life cycle organization necessary to obtained from analyst reference
indicator and indicators analysis of the subject area of virtual community.
«Processing of tasks component». Responsible for structuring of
processing of performs tasks direction of virtual community life cycle
organization. For performance of management algorithm processing of tasks

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direction of virtual community life cycle organization corresponds direction
performer, appointed by the manager of the virtual community.
«Protection from risks component». Responsible for protecting the
appearance of socially oriented risks, which provides measures with
counteraction. Performers of component are direction performers and
manager of virtual community life cycle organization.
4.3 Level of databases and information resources.
Level includes database of tasks indicators and database of reports of virtual
community life cycle organization. Also includes information resources.
«Database of reports» used as a data source about of virtual community life
cycle organization. «Database of reports» including reports about
performance of tasks directions of virtual community life cycle
organization.
«Database of indicators». Information about indicators of tasks directions
of virtual community life cycle organization includes the following
information: planned indicator, reference indicator, indicators analysis of the
subject area of virtual community, real indicator, input indicator, output
indicator of virtual community life cycle organization.
«Information resources». Includes a set of information environment of
WWW, necessary for analysis of the subject area and reference community.
Necessary information resources for analysis: virtual community, sites,
social networks.
5. RESULTS
Based on software and algorithmic complex developed software tool
«Virtual community life cycle organization» (Figure 6).
Potential users of the software «Virtual community life cycle organization»
can be: owners and managers of virtual communities; marketers; PR-
professionals companies, political parties, famous personalities.

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Figure 6. The user interface software.
After completion of the works on stage manager evaluates performance
directions to 10-point scale. Graph of job evaluation added to the general
documents of virtual community life cycle organization. Figure 7.
Evaluation stages of «Department SCIA» presented the example of
evaluation stages of virtual community life cycle organization «Department
SCIA» in the social network Facebook.
Figure 7. Evaluation stages of «Department SCIA»
6. CONCLUSIONS
Complex research on developing mathematical and software of virtual
community life cycle organization has provided an opportunity to develop a
software tool «Virtual community life cycle organization Complex research
provides predictable create a virtual community; predictable sequence of
steps and documentation; increases the level of control and the needs of
creators and customers of the community.

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REFERENCES
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[3] Wang, J. T., Yang, J. M., Chen, Q. & Tsai, S. B. 2016. Creating the sustainable
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[4] Tamjidyamcholo, A., Bin Baba, M. S., Shuib, N. L. M. & Rohani, V. A. 2014.
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[5] Fedushko S., 2016. Development of verification system of socio-demographic data of
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[6] Fedushko S., Syerov Yu., and Korzh R., 2016. Validation of the user accounts personal
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[7] Jungwirth, B. 2011. The New Community Rules: Marketing on the Social Web.
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[8] Porter, C. E., Devaraj, S. & Sun, D. 2013. A Test of Two Models of Value Creation in
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[9] Shen, K. N. & Khalifa, M. 2013. Effects of technical and social design on virtual
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[10]Mousavidin E., Goel L., 2009. A Life Cycle Model of Virtual Communities.
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[11]Howard R. HOW TO: Manage a Sustainable Online Community [Electronic resource].
– Mode of access: http://mashable.com/2010/07/30/sustainable-online-community/. –
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[12]Syerov Yu., Trach O., Fedushko S., 2016. Effect of Implementation of improved
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International Journal of Computational Research and Development, V. 1, I.1. pp. 1-5.
[13]Trach O., Vus V., Tymovchak-Maksymets O., 2016. Typical algorithm of stage
completion when creating a virtual community of a HEI. IEEE XIIIth Intern. Conf.
“Modern Problems of Radio Engineering, Telecommunications and Computer
Science”, Lviv-Slavske, February 23 – 26, pp. 849-851.
[14]Trach O., Peleshchyshyn A., 2017. Development of directions tasks indicators of
virtual community life cycle organization. International Scientific and Technical
Conference “Computer Science and Information Technologies”. [In print]
[15]Trach O., 2017 Socially-oriented risks in the organization of the life cycle of the virtual
community. Information activity, Documentation, Library: History, Present and
Prospects: Materials III All-Ukrainian. nauk. and practical. Conf., pp. 40-44.

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[16]Trach O., Peleshchyshyn A., 2016.Functional-network model of tasks performance of
virtual communication life cycle directions. Proceedings of the XIth International
Scientific and Technical Conference (CSIT 2016), Lviv Polytechnic Publishing House,
pp. 108-110.
This paper may be cited as:
Trach O., Fedushko S., 2017. Development and exploitation software
complex of virtual community life cycle organization. International Journal
of Computer Science and Business Informatics, Vol. 17, No. 1, pp. 1-11.

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Systematic Review of Persuasive
Health Technology Design and
Evaluation Models
Kasali Funmilayo
Babcock University, Ilisan, Ogun State
Nigeria
Kuyoro Afolashade
Nigeria
Awodele Oludele
Nigeria
ABSTRACT
Persuasive technologies for promoting physical fitness, good nutrition and other healthy
behaviors have been growing in popularity. Despite their appeal, the design and evaluation
of these technologies remains a challenge and usually require a fully functional prototype
and long term deployment just like any other information system. Hence, the focus of this
paper is to review some persuasive and behavioral change models used in designing and
evaluating persuasive technologies and identify their inherent limitations. To achieve the
stated objectives, the systematic review method of research was done to understand the
various persuasive system models and relevant information was extracted using the
Inductive approach. Currently, the Persuasive System Design (PSD) framework is
considered to be one of the commonly and most comprehensive framework for designing
and evaluation of persuasive systems. However, some of its design features overlap and are
difficult to analyze. This review research has brought to light the need to extend the PSD
theoretical model with a measurable and integrated usability model which can adequately
measure the efficiency and effectiveness of persuasive design outputs at the early phase of
persuasive system development in future studies.
Keywords
Behavioral change models, Persuasive health applications, Persuasive systems, PSD model.
1. INTRODUCTION
More recently, social technology continues to penetrate into every areas of
human lives at break-neck speed, most present medium of mass media have
integrated some sort of social interaction and awareness into their messages.
Social networks such as Facebook, Twitter, Netflix, Tumblr, Instagram,
online dating sites, educational sites, amongst others, continue to draw and

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lure millions of people into using social technology for multiple purposes.
The influence of technology on humanity is affecting all sectors of human
life both public and private, on-line and off-line [23] and one way or the
other its effect on people‟s behavior cannot be over emphasized. The word
influence comes under the umbrella of the word persuasion which is an
effort to influence/motivate/inspire people‟s beliefs, thoughts, actions,
feelings, motivations, intentions or behaviors [44] and in a scenario where
software systems are designed to achieve such purpose, it is termed
persuasive technology. Fogg, a leading and foremost researcher in
persuasive technology, was the first scientist to invent the word Captology
in 1996 which is “concerned with the domain of research, design, analysis
and application of Persuasive Technology” [15]. Captology describes the
area where technology and persuasion intersect as depicted in Fig. 1.
Technological Medium Persuasion
Fig. 1: Captology in view
Persuasion technologies are normally used in most areas of human lives
ranging from education, politics, religion, marketing, sustainability, health,
and training in any form amongst other applications. The main aim of
persuasive applications is to change human attitude or behavior through the
power of software designs [37].
There currently exist numerous persuasive technologies in existence whose
purpose is to encourage or facilitate attitudinal change towards a healthier
lifestyle [2] but evaluating these technologies remains a challenge and they
normally require a system that has been fully designed and deployed for use
over some period of time [25]. At the recently concluded 11th
International
Conference on Persuasive technology held in Austria, [1] also affirmed that
most persuasive applications need to be tested before deployment but the
problem is that there is really generally no agreeable way among such
systems developers as designing these systems require lots of planning, time
and other resources and what available models do is just to guide such
applications developers during the design process of persuasive systems.
Persuasive technology is basically about automating behavior change, and
in other to successfully code knowledge that results in behavioral change,
there is a need for practical understanding of human psychology, precisely,

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intuition about the causal factors of behavior change as also corroborated by
[16] if not, researchers of persuasive technologies will just be replicating
concepts that actually work without a thorough understanding of why those
concepts work.
Hence, the focus of this work which is intended to serve as an overture
towards further research, is to review the Persuasive System Design (PSD)
model which is currently a highly rated approach to the design and
evaluation of persuasive technologies [35] and other behavioral change
models that are aimed at changing peoples‟ poor attitude to exercise and
good diet that can help towards the prevention of diseases in other to come
up with a more useful model that can aid researchers and developers
towards the design of more usable, reliable, maintainable and more efficient
persuasive systems that can readily be evaluated even before such systems
are deployed. The remaining part of this work is arranged as follows:
Section 2 gives the literature review presenting some common health
behavioural change models and popular persuasive design models explains
in detail the applicability of some models toward the design of persuasive
systems, Section 3 explains the Methodology employed for this research,
while Section 4 gives the conclusion and recommendations for further
studies.
2. BACKGROUND STUDY
The current upsurge in untimely death and human ravishing sickness as a
result of different diseases that can be prevented warrant urgent attention
and behavioural change towards a healthier lifestyle by using a more
technological and pragmatic approach towards preventing such diseases.
There have been various Information systems that have been designed in an
attempt at using technology to control/prevent/monitor or treat diseases such
as expert systems, decision support systems and Persuasive technology
systems that are aimed at changing people‟s attitude towards a healthier
lifestyle [24] [47] [29].
The rise of social web and the use of mobile applications to create, share
and access information in innovative ways has accelerated the opportunities
for developing new kinds of Interactive Information Systems for influencing
users.
In recent years, researchers‟ interest have continued to sway towards Human
Computer Interaction (HCI) in designing persuasive systems that are aimed
at improving man‟s quality of life [38]. Despite the plethora of research into
Interactive Information Systems aimed at behaviour change [36], health-
related behaviour change has attracted lots of attention e.g., physical activity
[27] [10], diet [40], cardiac rehabilitation [30], and even the management of
chronic illnesses (e.g. diabetes [31] [46], healthy sleep behaviors [14],
kidney disease [45], asthma [26] amongst others.

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The benefits of using computers to promote quality of life by persuading
users as against traditional media is its interactivity and over human-human
persuasion includes six distinctive reasons, as highlighted by [15] ranging
from the fact that persuasive applications tend to be more persistent than
human beings, they offer greater anonymity, they can manage large volumes
of data especially in this big data era, they can use many tactics to influence,
scale easily and lastly they are more ubiquitous.
Developing persuasive systems usually put a heavy strain on developers, so
design issues should deserve more attention as they have real implications
because if the systems are not properly designed then the persuasion
potential will not really be achieved. More so, the most commonly studied
and applied model to building Persuasive technologies is the PSD model but
despite its use as a model to guide developers in designing Persuasive
systems, its use as an evaluation tool for persuasive systems has been
subject to criticisms amongst persuasive systems researchers. There is also
a need for persuasive systems developers and researchers to understand the
sociological, psychological and philosophical context behind various
models that can be employed towards designing effective persuasive
systems if not they will just be imitating other information systems and
ascribing them as being persuasive in nature although some information
systems have persuasive features but they cannot be said to be persuasive
applications which have distinctive persuasive characteristics and features as
also speculated in the PSD model.
2.1 Overview of Existing Health Behavioural change and Persuasive
Design and Evaluation Models
[5] proposed the Health Belief Model (HBM) which was developed to
figure out why people used or did not use preventive health services by
public health departments. HBM theorizes that people‟s beliefs about
whether or not they are at risk of a health issue, and their perceptions of the
benefits of taking action to avoid it, influence their readiness to take action.
[41] came up with the Trans-theoretical model (TTM) based on the concept
of “stage of change” and it suggests that people are at different stages of
readiness to adopt healthful behaviors. The notion of readiness to change, or
stage of change, has been examined in health behavior research and found
useful in explaining and predicting changes for a variety of behaviors
including smoking, physical activity, and eating habits. The TTM has also
been applied in many settings.
[4] introduced the Social Cognitive Theory (SCT) which is the cognitive
formulation of social learning theory explains human behavior in terms of a
three-way, dynamic, reciprocal model in which personal factors,
environmental influences, and behavior continually interact. SCT
synthesizes concepts and processes from cognitive, behavioristic, and

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emotional models of behavior change. A basic concept of SCT is that people
learn not only through their own experiences, but also by observing the
actions of others and the results of those actions. There also exist the Social
Ecological Model (SEM) which is based on the principles of social
ecological models and these principles are also consistent with social
cognitive theory concepts which suggest that creating an environment
conducive to change is important to making it easier to adopt healthy
behaviors as shown in Fig. 2. [17].
Fig. 2: Social Ecological Model [13]
Fishbein and Ajzen in 1980 proposed the Theory of Reasoned Action (TRA)
which is based on the fact that that actual behavior could be determined by
considering intention with beliefs associated with the given behavior as
cited in [11]. They also claimed that behavioural intention could be
determined by considering both attitude towards the actual behavior and the
subjective norm associated with the behavior in question. A limitation of
TRA is that some behaviors are not under a person‟s control and in other to
address this limitation, Ajzen in 1985 went further by introducing the
concept of perceived behavioural control just to improve on the predictive
capabilities of the TRA and named this enhanced model Theory of Planned
Behaviour (TPB).
Looking at health behaviour change from Elaboration Likelihood Theorists,
[39] promulgated the Elaboration Likelihood Model (ELM). The model
described how attitudes are formed and strengthened by persuasive
arguments. The model proposed that people convey either high or low
elaboration which is their level of effort when they are faced with a
persuasive message. The level of elaboration will now decide which
processing route the message will take either central or peripheral. [52] gave
a very detailed and simple explanation on both routes in his online article on
how to apply the ELM to design. The ELM is very similar to the Heuristic-
Systematic model (HSM) proposed by [8] which just tries to describe how
people receive and process persuasive messages.
[43], a German professor of psychology proposed the Health Action Process
Approach Model (HAPA) which is an open framework of various

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motivational and volitional concepts that are presumed to explain and
predict individual changes in unhealthy behaviors towards a better one.
HAPA suggests that the adoption, initiation, and maintenance of health
behaviors should be conceived of as a structured process including a
motivation phase and a volition phase. Motivation phase describes the
intention development while the Volition refers to planning, and action. The
model accentuate the particular role of perceived self-efficacy at different
stages of health behavior change as shown in Fig. 3.
Fig. 3: HAPA Model [43]
Fogg Behaviour Model for persuasive Design (FBM) was proposed in 2009
to understand human behavior, he opined that behavior is a product of three
factors which are motivation, ability, and triggers, each of which with its
own modules as shown in Fig. 4. The FBM asserts that for a person to
perform a target behavior, he or she must be sufficiently motivated, have the
ability to perform the behavior, and be triggered to perform the behavior
with each of these behaviors happening simultaneously if not, the behaviour
is highly unlikely to happen. Fogg‟s behavior model provides an
understanding of relationships between motivations, abilities and triggers.
However, it does not explicitly discuss persuasive features implementation
in designing a persuasive system.

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Fig. 4: FBM with the 3 factors and their subcomponents [16]
Dan Lockton addressed behaviour change from a perspective dubbed
“Design with Intent” (DwI) which he defined as a “design intended to
influence or result in certain user behaviour” [28]. The DwI Method is
intended to be generally applicable to influencing user behaviour. The latest
iteration of the model is comprised of two modes: „Inspiration‟ and
„Prescription.‟ In the „Inspiration‟ mode, the designer takes inspiration from
a set of headline design patterns that are applicable to a wide range of target
behaviors, grouped into six different „lenses,‟ representing particular
disciplinary perspectives on using design to influence behaviour. In the
„Prescription‟ mode, the designer formulates a range of target behaviors or
intended outcomes describing interactions and, as a consequence, a subset of
the most applicable design patterns from each „lens‟ is presented for each
target behavior.
Health theories help to understand why people do/do not practice health
promoting behaviors, identify what information is needed to design an
effective intervention strategy and provide insight into how to design a
successful persuasive health program. They help to explain behavior and
also suggest how to develop more effective ways to influence and change
behavior although the success of the adoption of persuasive technologies
will largely depend on the grounded understandings of these theories as
noted by [9] but the reality is that there exist numerous theories aimed at
behavior change but they are majorly used as a checklist or rules of thumb
for software systems rather than a systematic design methodology to the
design of user interface.
[3] also presented this same view in their conference paper produced for the
Global Summit on telemedicine and eHealth. A lot of these behavioural
change models are psychological in nature and offer little information on
how to design and implement persuasive systems as also confirmed by [32]

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which is why researchers interested in persuasive systems tend to focus
more on Fogg‟s persuasive design principles and the PSD model although
there exist other persuasive design models but they are yet to be as popular
and widely accepted as Fogg and PSD model.
Fogg‟s model is a functional triad and the design principles introduced in it
signify the first and highly used generalization of persuasive technology.
The model was developed by [15], a leading researcher and an authority in
the field of persuasive technology. In his eight step model, developers are
guided towards creating a persuasive technology. However, Fogg asserts
that “the eight steps are not intended to be a rigid formula; instead, the
steps serve as milestones to make the design process more effective”. These
steps are highlighted and well explained in [48].
Fogg also explained the three roles computing technology can play in the
functional triad which are to act as a tool, media or social actor in the act of
persuasion from users‟ perspectives. Fogg also identified persuasive
technology tools which are interactive products that are designed to change
attitudes or behaviors or both by making desired outcomes easy to achieve.
Such tools include Reduction, Tailoring, Tunneling, Suggestion, Self-
monitoring, Surveillance and Conditioning.
The model just helps to understand the concept of persuasive technologies
better but it is too restrictive to be applied directly to persuasive system
development and/or evaluation. The major weakness that is inherent in this
model as claimed by [35] is that it does not really indicate how the proposed
design principles can be modified into software requirements and moreover
executed as software features; but to be able to design and evaluate the
persuasiveness of a software system, it is very important to understand both
the information content and the software functionalities which is what gave
the PSD model more popularity in its usage as against the Fogg‟s model in
designing persuasive systems.
The PSD model is a conceptual framework for developing persuasive
systems and it was postulated by [35]. It has gained so much popularity
amongst persuasive systems designers and researchers. The model has been
successfully applied in so many domain like in health, education, amongst
others. It explains the means of designing and evaluating persuasive systems
and also explain what kind of content and software functionality may be
found in the final product as depicted in Fig. 5 below. The model lay
emphasis on seven fundamental assumptions or hypothesis behind
persuasive systems where two of these postulates relate to how users are
seen in general, two of the postulates relate to persuasion strategies, and
three of the postulates address actual system features. The model highlights
ways to analyze the persuasion context which include the intent (this could
be exogenous, endogenous or autogenous), the event and the strategy. It also

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lists twenty eight design principles for persuasive system content and
functionality.
Fig 5: Phases in Persuasive System Development [35]
This model is an improved version of Fogg‟s model and most of the design
principles in this model was adopted and modified from Fogg‟s model. The
PSD model has been widely acknowledged as being suitable for designing
persuasive systems but it cannot promise the success on any behavioural
change support system [34]. The goal of the PSD model is not really to
implement all the design features suggested in it but to choose the right
features based on the system‟s context of use and domain as claimed by its
proponents. Its limitation is that some of these features overlap with one
another and usually difficult to analyze. Hence, new persuasion techniques
to evaluate and fortify persuasive components need to be ascertained. Other
models include the 3D-RAB model proposed by [50] and they showed how
it can be applied in classifying users based on changes in levels of cognitive
dissonance. The model tends to present a method that can be used to analyze
the user context on the PSD model. In the model, it was postulated that eight
states of cognitive dissonance among users should be considered. This
approach was evaluated using an already existing BCSS and designers were
encouraged to apply the 3D-RAB model in order to design solutions for
targeted users.
The model is just an approach to analyze targeted users and it cannot be
used to design persuasive technologies as also claimed by its proponents.
[33] aimed to look at analyzing persuasive designs from a data analytics
point of view by trying to integrate analytical models into persuasive
designs for improved results and the researcher also tried to describe how to
represent human behaviour as a mathematical model so as to overcome the
limitations of a systematic approach to persuasive design evaluation as seen
in other models, theories and frameworks for persuasive design. Although
this is very novel approach to systematic persuasive applications design, the
idea is still very abstract in nature as the actual mathematical model based

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on their identified factors was not shown as it is still a research work in
progress although this model comes closest at trying to analyze persuasive
applications both quantitatively and qualitatively.
2.2 Applicability of Models in the design of Persuasive Systems
[49] described an interface during an attempt to develop a persuasive system
that is aimed at motivating physical activity among university students in
their day to day activities to curb the modern issue of obesity. The trans-
theoretical model of behaviour change and Fishbein and Ajzen‟s theory of
reasoned action were used as the principle that governed the interface
design. Several prototypes were developed for this study and each prototype
was evaluated both for design and functionality with a total number of 41
users. The system could not be implemented as the work was just a
conceptual description. As a part of the PEGASO European project, [6]
created a persuasive system based on mobile technology in motivating
teenagers to easily adopt a healthy lifestyle. They used the Virtual
Individual model (VIM) and some of Fogg‟s behavioural model idea like
Tailoring, social network integration and the trigger concept. They intended
doing pilot studies in 3 different countries to validate the effectiveness of
their approach after the successful completion of their project.
[42] designed a fictional system called Fit4Life; a system that encourages
individual to address the larger goal of reducing obesity in society by
promoting individual healthy behaviors by using the PSD model to outline
the persuasion context, its technology, its use of persuasion messages and an
experimental design to test the system‟s efficacy. [22] designed a persuasive
mobile application to support controlled alcohol usage by using the user
centered design approach based on ISO 9241-210 and Google Inc. user
experience design experience on an android platform. The persuasive
features in the system was evaluated using 12 of the design principles in the
PSD model as against the 28 defined principles.
[19] did a field trial of the Polar FT60; a fitness watch with Global
Positioning System (GPS) and heart rate monitor to describe and understand
findings from a three month long qualitative field trial to explore how a
training program in a new prototype heart rate monitor promotes proper
exercising. The PSD model was used to identify distinct strategies and
techniques that were embedded into the system and 12 users‟ responses to
these strategies were also explored. They only demonstrated how persuasive
techniques can be identified, embedded into system functionality and also
how persuasive techniques function together in real world settings. They
were able to find out that leveraging goal settings, tracking performance,
adopting social roles along with a high overall perceived credibility
influences user behaviour. The studied persuasive principles were limited to
the design of the particular product that was investigated and the researchers

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did not participate in the design process. [18] investigated how the PSD
model can be utilized so as to support the development of personal health
and well-being systems. In other to achieve this, they integrated the PSD
model into the development of 2 health related Behaviour Change Support
System (BCSS). In the first study, their aim was to use the PSD model to
identify new persuasive functionality within a fall risk assessment and fall
preventive system. In the second study, their aim was to use the PSD model
to identify new persuasive functionality and new service concepts within an
existing smart phone app for mental well-being. Their study showed that the
PSD model can be used in the development of BCSSs to describe the overall
process, analyze the persuasion context and design qualities. They also used
the PSD model to evaluate both systems by providing heuristics of expert
evaluation and systematic ways to analyze user experience data. Both
human centered and iterative process were used in designing both systems.
As a result of their research, they were able to ascertain that although the
PSD model purposes how persuasive systems should be developed in a very
holistic manner, its limitation is that it does not explicitly give advice on
how to include a framework or theory into the development of the content
delivered via the system and users it does not also give advice on how to
include users in the development process which is very important according
to [21].
[51] designed a persuasive fitness app that can enhance physical activity
behaviour of individuals by conceptualizing the persuasive technology
design principles embedded in social cognitive theory which suggests that
individual behaviour is determined by triadic, dynamic and reciprocal
interaction among cognitive, personal factors and environmental influences.
[7] focused on building a persuasive system for behaviour modification
around emotional eating by undertaking 3 user studies. The first study was
done to gather emotional eating patterns using a custom built app called
EmoTree so as to understand users‟ emotions associated with eating. The
second study was done to learn about a suitable intervention technique for
emotional eating based on self-reported ratings of emotions to gather early
feedback before actual system implementation and they found out that there
exist lots of individual differences in emotional eating behaviour. Their last
objective was to build a wearable, sensor system for detecting emotions
using a machine learning approach to predict users‟ emotions. In this work,
no particular design method was followed and no formal theory of
behaviour change was considered.
As noted from various attempts at designing persuasive systems, developing
persuasive systems puts a very rigorous burden on software developers as
there are lots of theories, design approaches and principles to be considered
at the early design stage hence effective evaluation at the early design phase
is an important requirement that needs to be strictly adhered to so as to save

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cost and reduce designers time as evaluating a system after the overall
product has been designed can be very tasking, time consuming and
expensive. Most of the existing health behavioral change theories help to
explain, understand behaviors and also suggest how to develop more
efficient ways to influence and change behavior but most of them can only
act as guide towards designing persuasive systems as to our knowledge,
there presently exist no tool or framework yet for evaluating persuasive
technologies except for the PSD model whose limitation is that some of the
28 design features coincide with one another and are usually difficult to
analyze, moreover most of these features are also just to guide designers in
making persuasive systems more influential hence, new persuasive
techniques to evaluate and fortify persuasive components need to be
ascertained. In addition, most of the recent work in persuasive design using
the PSD model are still at the conceptual level.
3. METHODOLOGY
To achieve the stated objectives, the systematic review method of research
was done to understand the various behavioral change and Persuasive
System models and relevant information was extracted using the Inductive
approach towards research in which past theories for designing and
evaluating persuasive designs was thoroughly analyzed. Patterns,
resemblances and regularities in past theoretical premises were observed to
identify their limitations and a new theory/model was proposed to be
generated in subsequent studies without discarding ideas gotten from past
models. This form of research is mostly based on grounded theories
according to [12]. It starts with observations and theories which are
proposed towards the end of the research process as a result of the
observations which is depicted in Fig. 6 below.
Fig. 6: Inductive approach
Most of the papers reviewed were gotten from Google, Google scholar and
Association for Computing Machinery (ACM) databases using keywords
such as Persuasive technology, behavioral change models, and persuasive
design models amongst others.
Discussion and
Recommendations
Identify
patterns and
limitations
Study past
persuasive
design models

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4. CONCLUSIONS
Persuasive technologies for promoting physical fitness, good nutrition and
other healthy behaviors have been growing in popularity. Despite their
appeal, the design and evaluation of these technologies remains a challenge
and usually require a fully functional prototype and long term deployment
just like any other information system. Most health behavioral change
models cannot adequately measure the effectiveness of persuasive systems
as they can only be used as guides during the design process by most
persuasive systems developers and researchers. The PSD model is currently
the most widely used model in designing and evaluating persuasive
technologies but its limitation still remains apparent especially in evaluation
purposes.
A new framework will be proposed and evaluated in subsequent studies to
extend the PSD model by integrating the requirement engineering approach,
new Human Computer Interaction (HCI)/User centered principles and
effectiveness evaluation using a the Integrated Measurement Model for
Evaluating Usability Attributes designed by [20]. A prototype health
application will be designed and an attempt will be made to predict the
usability of such systems at the early phase of the design process using the
fuzzy analytical hierarchy process as usability has also been identified as
one of the most important construct used in evaluating the effectiveness of a
system. The Evaluation framework is also being proposed in further studies
to be formalized using Fuzzy logic to deal with imprecise usability attributes
and to also enable a more systematic approach to persuasive technology
evaluation.
5. ACKNOWLEDGMENTS
We wish to thank Prof. Goga, Dr Akinsanya, Dr Eze and 2016/2017
doctoral students of Babcock University, Computer Science Department for
their constructive and objective criticisms towards the successful
completion of this work.
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Designing Condition-based
Maintenance Management Systems
for High-Speed Fleet
Gopalakrishna Palem
Cenacle Research, 520012 IN
ABSTRACT
Advancement in the big-data technologies in combination with machine-to-machine
(M2M) interconnectivity and predictive analytics is creating new possibilities for real-time
analysis of machine components for identifying and avoiding breakdowns in the early
stages ahead of time. Designing such a condition-based maintenance system for high-speed
fleet requires special attention to the design methodologies used in collecting the operating
requirements from the users and translating them into big-data parallel architectures that are
capable of exhibiting fault-tolerant behavior and load-balancing possibilities to sustain the
real-time data processing demands. This paper discusses the M2M approach for the big-
data condition-based maintenance system and the requirement specification steps involved
in building such a system, along with the cost-savings benefited from the system.
Keywords
Condition-based maintenance, Fleet-management, M2M Telematics, Predictive Analytics
1. INTRODUCTION
Approximately 30% of the life-cycle costs of a high-speed vehicle are spent
on the maintenance of the vehicle, the largest spend besides energy [1]. The
overall life-cycle cost distribution for a high-speed fleet is as shown below.

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Figure 1. Life-cycle costs of high-speed fleet
Pain-points that customers usually complain about such life-cycle costs are:
 Maintenance is the highest cost factor in the operations of high
speed vehicles, besides energy and depreciation.
 Over a period of time, maintenance costs exceed the depreciation.
 Approximately 40% of the maintenance goes for the material / spare
parts costs, while the remaining 60% amounts to personnel costs.
 For an operational fleet, the depreciation and energy costs stay
constant during the fleet’s life-cycle, leaving the maintenance cost as
the only major cost position available for optimization [1][2].
Thus, reducing the maintenance costs highly improves the profit margins for
operators. The different maintenance strategies followed by manufacturers
and operators in this regard are as follows:
 Corrective Maintenance: This is a Run-till-Failure methodology without
any specific plan of maintenance in place. Vehicle is considered to be
functional and fit until it breaks-down.
o Cons:
 Unexpected and uncontrolled production downtimes.
 Risk of secondary failures and collateral damage.
 Uncontrolled costs of spare parts and overtime labor.
o Pros:
 Zero overhead of planning or condition monitoring costs.
 Machines are not over-maintained.
 Preventive Maintenance: A periodic maintenance strategy popular with
the current manufacturers and vehicle service operators. Based on the
asset design parameters, a potential breakdown period is pre-calculated
and a schedule is pre-determined for preventive maintenance. Vehicle is
subjected to regular maintenance periodically on those intervals,

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irrespective of the usage pattern or the condition of the asset, assuming
that the vehicle is going to break-down otherwise.
o Cons:
 A time-driven procedure. Assets are subjected to repair
even in the absence of any faults.
 Unscheduled breakdowns can still happen
o Pros:
 Maintenance cost estimates are known beforehand.
 Inventory control and spare-parts planning is possible.
 Fewer catastrophic failures and lesser collateral damage.
 Predictive Maintenance (PdM): This is an emerging strategy that applies
predictive analytics to the real-time data gathered from the vehicles with
the aim of detecting any deviations in the functional and behavioral
parameters that can lead to vehicle breakdowns. Such anomaly detection
procedures help identify the breakdowns as soon as their potential cause
arises in real-time long before the break-down happens.
o Cons:
 Additional investment needed for the monitoring system
 Skilled labor specially trained to effectively use the
system may be required.
o Pros:
 Parts are ordered on the need basis and maintenance is
performed during convenient schedules.
 Unexpected breakdowns are eliminated.
 Reduced breakdowns result in maximum asset utilization.
Predictive maintenance, is also often commonly referred to as the
Condition-based Maintenance (CBM), as it avoids the unnecessary
inspection and repair costs by recommending a maintenance schedule that is
based on the prevailing conditions of the machine in the real-world
operating conditions [3].

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Figure 2. Predictive Maintenance reduces costs by detecting failures in early stages
To understand this, let us consider a typical periodic maintenance scenario
for a vehicle. In a normal periodic maintenance mode, the vehicle owners
are expected to change the engine-oil frequently at regular periods, such as
after every 4 or 5 thousand Kilometers traveled. In such cases, the real
condition of the vehicle or the performance capabilities of the engine-oil are
not taken into consideration. Maintenance is carried out purely because it is
as per the schedule. Had the owner had a way to realize the underlying
vehicle condition (the remaining useful life, RUL), or the engine oil
lubrication contamination levels at that instance, he or she could potentially
either postpone the oil change, to a later point where the change is really
needed, or even pre-pone it as per the prevailing conditions. CBM provides
such capability to gain insight into the actual operating conditions of the
vehicle and use them to accurately predict the maintenance requirements.
Our earlier paper [3] presented an in-depth review on the inner workings of
CBM systems and how in conjunction with sensor arrays and telematics
they facilitate predictive maintenance.
Increased component availability, better worker safety and improved asset
usage etc. are some of the compelling reasons why more and more operators
and manufacturers are actively embracing CBM based fleet management
solutions.
 Benefits for workers:
– Work-life balance with predictable schedules
– Turn-key solutions with zero paper work
– Increased on-road safety
– Navigation helpers and landmark guides
 Benefits for Management:
– Reduced maintenance costs with Predictive Maintenance

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– Increased asset usage with zero unplanned downtime
– Operational costs are reduced and idle times are eliminated
with smart scheduling
– Improved customer loyalty with always on-time deliveries
– Theft and misuse prevention with real-time asset tracking
In the following sections, we present the methodology involved in designing
such a condition-based maintenance management system using the
machine-to-machine (M2M) approach, and showcase the architectural
outline for one of our recently built system, along with the open-source
tools and frameworks used in building the system and the cost-savings
reported by the customers using it.
2. M2M APPROACH TO THE CBM
A Condition-based Maintenance Management (CBMM) solution designed
around M2M operates on three major technology directives:
1. Remote Sensor Monitoring & Data Capturing.
2. Real-time Stream Processing of Sensor Data.
3. Predictive Analytics.
Sensors are attached to the remote assets to collect various data about the
assets’ operating behavior and send it in real-time to a centralized
monitoring station. The data arrives as continuous streams at the monitoring
station, and is subjected to analysis using anomaly detection mathematical
models to identify patterns of deviations in the expected functionality. Once
any such anomaly is identified by the algorithms, owners are immediately
notified indicating the potential failure and suggesting the appropriate
corrective action. Handling such anomalies in timely manner prevents
further functional degradation of the vehicle, thus avoiding potential costly
breakdowns down the line. Often times the centralized monitoring station
resides on the same network as that of the sensors (such as control area
network) or it could be in a distant remote location connected through
satellite networks or WAN.

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Figure 3. M2M facilitates real-time failure detection and prediction
During their operations, devices such as On-Train Monitoring Recorder
(OTMR) for trains and Flight Data Recorder for flights record events in
real-time from their connected vehicles, and either store them on-board for
later processing when they reach their destination, or relay the events to the
centralized processing system in real-time enroute using the machine-to-
machine (M2M) telematics procedures and get processed on the fly to detect
any current anomalies and predict future failures [4]. Nature of some of the
data collected and analyzed for this purpose could be as follows:
 On-board Diagnostics (OBD) data: Vehicle speed, RPM, fuel etc.
 Driving Patterns: Acceleration patterns, braking patterns etc.
 GPS data: Locations, routing, length of stay of vehicle etc.
 OTMR data: Door close status, Air suspension pressure, Brake
dragging, HVAC failure etc.
In a nutshell, the concept of CBM is centered around: detect failures in their
early stages so that you can prevent them from happening in the later
stages. At the minimal level one can expect the below listed functionality
from a well-designed CBMM system [7][8][9]:
 Find the Remaining Useful Life of assets
 Estimate the Failure Rate for assets
 Design a Predictive Maintenance Schedule
 Maintain right levels of Inventory for spare parts
 Schedule right skilled and sized workforce
 Optimize Inspection routines
 Decide right Warranty period at design time

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 Evaluate What If alternate scenarios
 Compare different designs for reliability evaluation
A major challenge in implementing a CBMM system for high-speed fleet,
however, is: processing the enormous volumes of data streamed-in from
sensors attached to the high-speed vehicle in real-time. This requires:
 Parallel architectures capable of handling large volumes of data,
 Low payload data-structures that optimize sensor data bandwidth,
 Fault-tolerance capabilities that can deal with packet drops and
fragile networks for real-time data streaming,
 Adaptable ontologies capable of supporting varied data types and
protocols in parallel,
 Proof based security to ensure data privacy and anonymity.
Latest advancements in the Big-data open-source family of technologies
offer viable solutions for the above requirements [5][6]. However, before
one can design such big-data solution for the CBMM, the design process
has to go through the requirement gathering and specification mapping
stages to be able to accurately capture the customer requirements and realize
them in software. The following section elaborates on this.
3. THE CBMM SYSTEM DESIGN PROCESS
The design process starts with requirement gathering, which can be
classified as addressing the three solution enabler stages as indicated below:
 Stage 1: Sensor data capturing stage
 Stage 2: Real-time stream processing stage
 Stage 3: Predictive failure-detection stage
The requirement gathering for stage 1 encompasses collecting information
from the customer on the requirements of data capturing and real-time
monitoring. Some of the questions that help gathering information from the
customers at this stage are:
 What data should be collected and which sensors should be used?
E.g. thermal imagery, audio signals, etc.
 What are the components and parts that need monitoring? E.g.
Engine Oil, Train brakes, Engine Crank Time, etc.
 How frequently the data should be collected? Hourly, daily etc.
 How to identify and handle faulty sensors?

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In the requirement gathering for stage 2, the focus is on real-time processing
of the collected data and some of the questions that customers need to
answer in this stage are:
 What is the expected data processing latency?
 What should happen to the collected data post processing?
 How to address missing data points and inaccuracies? For example,
a faulty sensor sending incorrect data.
For the final stage, the emphasis is on the analytical-subsystem. Customer
requirements for this stage are collected through questions such as:
 Define the acceptable behavior and define the anomaly.
 What are the response actions for each anomaly class?
 What is the maximum acceptable time lag after the detection of the
anomaly, before the corresponding corrective action takes place?
 How to deal with multiple anomalies detected at the same time?
Once complete, the gathered requirements are then formulated into a system
specification that gives a formal outline of what is the expected from the
CBMM. E.g. for the stage 1 requirements, the specifications outline what
should be the operational level notifications possible in case of network
unreachability for the sensors during the data capturing stage.
Similarly, stage 2 requirement specifications formalize the data-processing
functionality. The specifications for this stage result in a matrix like
structure as shown in the below table, where each component that is being
monitored is listed alongside the possible events it can generate and the
criticality of each event, along with what action, if any, should be carried
out by the ground/operating crew monitoring that event.
Component Event Source Event
Criticality
Control
Center
Alert
Event reaction
Door Closed after
the train
started moving
Door
side
camera
Low - -
Break Emergency
break tripped
OTMR Critical SMS/Email/
Escalation
Matrix
Check power
supply, air
pressure

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For example, in the above, one can see the component door being monitored
for the close event, with a low criticality being attributed to it, while an
emergency brake event is being monitored with high criticality attribution.
Also, in case of emergency brake event, the event reactions list possible
course of action, such as checking the power supply and air-brake pressure,
which act as resolution guidelines for the crew and/or automated resolution
solver system.
The specifications for the final stage revolve around failure prediction.
Formal guidelines are established as to how a failure should be predicted
and which data source and event should be used in the process. For
example, the below table lists trend analysis criteria and pattern matching
criteria as the stipulated methods for the door and break failure respectively.
Component Event Failure Indication
Door Closed after the train
started moving
1. Delay increasing, or 2. Happening for the last
n observations (n > threshold)
Break Abnormal break
pressure patterns
Pattern matches with historical failure data
Based on these specifications, the CBMM system collects the data at the
specified intervals from the sensors and utilizes the below methodologies to
assert the asset’s condition:
 Critical range and limits: Various statistical tests are performed to
assert if the captured sensor data falls inside a critical failure range
decided by the expert and requirement specifications [10].
 Trend Analysis: Verify if the vehicle condition is in a deteriorating
mode with an immediate downwards trend towards breakdown [11].
 Pattern recognition: Establishes the causal relations between the
events and the vehicle breakdowns [12].
 Statistical process analysis: Historical failure record data, collected
through case-study histories, warranty claims and data archives, is
processed with statistical procedures to find a suitable analytical
model for the failure curves. As new data is gathered from the
sensors, it is compared against those statistical models to predict the
future breakdowns [13].
Trend analysis and critical range limit violations can be detected with real-
time monitoring and stream processing of data. However, the pattern
recognition and statistical process analysis requires historical data to be
analyzed and compared against the real-time live data for insights. Usually

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such historical data is gathered through warranty-claims and maintenance
records.
Advancements in the Big-data technologies and predictive analytics are
enabling the stream processing of high volume live-data in real-time and
matching it with the voluminous historical data offline. A reference
architecture that was created for one of our large high-speed fleet
management clients using the afore-mentioned design methodology on Big-
data using M2M is as shown below:
Figure 4. Reference architecture for condition-based maintenance mgmt. system
The layered architecture enables one to easily customize or upgrade only
particular part of the system without completely replacing the whole system.
The XML schemas used as the base to store and operate on the operating
design specifications allow cross-platform compatibility and open-systems
interoperability. Sensors communicate with the data acquisition and
manipulation layers using the M2M framework, while the condition-
detection, prognosis and health-assessment layers were implemented using
Big-data parallelism. The maintenance support layers take care of the
required notifications for the administrators and operating crew using the
report dashboards and HMI visualizations along with security restrictions.
To achieve this level of sophistication, we integrated and customized
multiple open-source frameworks to our requirements, some of which are
listed below.

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 Remote sensor monitoring & data capturing: OpenXc
 Real-time stream processing: Storm, Kestrel, ZMQ, MQTT
 Predictive analytics: R
 Real-time anomaly detection: Esper, CEP
 Distributed fault-tolerant storage: Hadoop, HBase
 Failure report dashboards: HTML 5
 Control center visualization: OpenGl, Vtk, Qt, HMI
The value-add in integrating and customizing these frameworks lies in
achieving the required level of functionality with commodity hardware,
enabling it to handle large volumes of data with adaptable ontologies all the
while reducing the sensor data bandwidth. In their native form, individually,
these open-source frameworks will not be able to achieve the afore-
mentioned objectives in a manner suitable for enterprise customers [14].
The integration and interconnection of different technologies used for
implementing this solution is as shown below:
Figure 5. Technology stack integration for our condition-based maintenance
management solution
After the initiation of a fully functional CBMM system, our customer
reports have indicated the following year-wise average savings resulted
across their business units:
 Reduction in maintenance costs: 25% to 30%
 Spare parts inventories reduced: 20% to 30%
 Reduction in equipment downtime: 35% to 45%
 Elimination of breakdowns: 70% to 75%
 Overtime expenses reduced: 20% to 50%

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 Asset life increased: 20% to 40%
 Increase in production: 20% to 25%
While the predictive technology reduced the unexpected brake-downs, the
collateral benefits, such as work-life balance (with no unexpected brake-
down calls), reduction of over-time expenses and improved asset
availability contributed to the production increase rates.
4. CONCLUSIONS
Advancement in the big-data technologies in combination with M2M and
predictive analytics is creating new possibilities for real-time analysis of
machine components for detecting failures in the early stages and avoiding
them ahead of time. Increased component availability, improved worker
and environment safety, better asset usage etc. are some of the reasons that
are attracting more operators and manufacturers to embrace condition-based
maintenance strategy in their operations. Designing such a system for high-
speed fleet, however, requires special attention to the design methodologies
used for collecting the operating requirements from the users and translating
them into big-data parallel architectures that are capable of exhibiting fault-
tolerant behavior and load-balancing possibilities to sustain the real-time
data processing demands. This paper presented reference architecture for
one of our big-data M2M systems we designed as a large fleet-management
solution for a customer and showcased the technology framework
interconnects used in the said system. With more and more customers
becoming interested in these solutions, one can expect more solutions built
on these architectures using the listed frameworks and suggested design
methodologies in the future.
REFERENCES
[1] Romain Bosquet, Pierre-Olivier Vandanjon, Alex Coiret, and Tristan Lorino,
Model of High-Speed Train Energy Consumption, World Academy of Science,
Engineering and Technology, 2013.
[2] Jui-Sheng Chou, Changwan Kim, Yao-Chen Kuo, Nai-Chi Ou, Deploying
effective service strategy in the operations stage of high-speed rail, Transportation
Research Part E: Logistics and Transportation Review, 47(4):507-519, July 2011.
[3] Gopalakrishna Palem, Condition-Based Maintenance using Sensor Arrays and
Telematics, International Journal of Mobile Network Communications &
Telematics, 3(3):19-28, 2013.
[4] Gopalakrishna Palem, M2M Telematics & Predictive Analytics, Technical Report,
Symphony Teleca Corp., 2013
[5] Dino Citraro, Expanding Real-Time Data Insight at PARC, Big Data, 1(2): 78-81,
2013

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[6] Okuya Shigeru, M2M and Big Data to Realize the Smart City, NEC Technical
Journal, 7(2), 2012
[7] Moubray, John. Reliability-Centered Maintenance. Industrial Press. New York,
NY. 1997
[8] Nowlan, F. Stanley, and Howard F. Heap. Reliability-Centered Maintenance.
Department of Defense, Washington, D.C. 1978. Report Number AD-A066579
[9] NFPA 1911, Standard for the Inspection, Maintenance, Testing, and Retirement of
In-Service Automotive Fire Apparatus, 2007 Edition, 6.1.5.1, p1911-14
[10] Hodge, V.J. and Austin, J A survey of outlier detection methodologies, Artificial
Intelligence Review, 22 (2). pp. 85-126, 2004
[11] Sematech, Failure Reporting, Analysis and Corrective Action System, 1993
[12] Felix Salfner, Predicting Failures with Hidden Markov Models, In Proceedings of
the 5th European Dependable Computing Conference (EDCC-5), 2005
[13] Weibull, W. A statistical distribution function of wide applicability, Journal of
Applied Mechanics-Trans. ASME 18 (3): 293–297. 1951
[14] Tristan Müller, How to choose a free and open source integrated library system,
OCLC Systems & Services, 27(1): pp.57 – 78, 2011
Gopalakrishna, P. 2017. Designing Condition-based Maintenance
Management Systems for High-Speed Fleet. International Journal of
Computer Science and Business Informatics, Vol. 17, No. 1, pp. 28-40.

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