1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
BIJ
18,4 Construction plant breakdown
criticality analysis – part 1:
UAE perspective
472
P.B. Ahamed Mohideen
Birla Institute of Technology and Science, Pilani, India
M. Ramachandran
Birla Institute of Technology and Science, Pilani – Dubai,
Dubai, United Arab Emirates, and
Rajam Ramasamy Narasimmalu
Mechanical Engineering Coimbatore Institute of Technology, Coimbatore, India
Abstract
Purpose – The purpose of this paper to develop a novel strategic approach to handle corrective
maintenance procedure in the event of a breakdown/disruption of service. A proposal to minimize the
recovery time and the breakdown cost in the system in construction plant is presented.
Design/methodology/approach – The past plant breakdown records of a construction organization
are considered for the analysis. From the previous breakdown records, a high level metric using Pareto
analysis and the cause effect analysis is used to identify the main breakdown main codes (BMC) and the
subsequent breakdown sub codes (BSC). Prioritized BMC and BSCs are used to formulate dedicated
breakdown maintenance teams, which act swiftly in the event of the breakdown with the modified methods.
Findings – The study was conducted, on four different types of heavy lifting/earth moving/material
handling system equipment, which are used to load/unload/haul and transport construction materials.
Failure due to tyre puncture and allied problems contribute to maximum failure. A strategy plan to
minimize this type of failure is proposed. With the identification of the most contributing BMCs and
BSCs, it is further proposed to develop an “overall breakdown maintenance management”.
Research limitations/implications – The collected data pertains to the construction plant located
in a particular region, namely the Middle East, and hence the proposed solution is dedicated/relatively
applicable to similar plant from the same region. A more robust model can be suggested considering
the work environment in the other regions.
Practical implications – The proposed methodology is highly adaptable by similar industries
operating in the Middle East region.
Social implications – Construction plant and equipment contribute to the success of construction
organizations, by providing enhanced output, reduced manpower requirement, ease of work and
timely completion of the project. Delays in completion of projects generally have both social and
economical impact on the contractors and the buyers. The proposed model will bring down the
lead-time of the project and enable the contractors to crash down their project completion time.
Originality/value – Numerous studies on preventive maintenance models and procedures are available
for a system and in particular to construction plant maintenance in the literature. This model attempts to
handle the issues of unpredictable breakdowns in the construction plant to minimise the breakdown time.
The proposed model is a novel approach which enables a quick recovery of the construction plant, attributed
Benchmarking: An International from the breakdown parameters derived from the previous history of the work records/environment.
Journal
Vol. 18 No. 4, 2011 Keywords Construction, Pareto analysis, Plant and equipment, Maintenance management, Breakdowns,
pp. 472-489 Criticality analysis, United Arab Emirates
q Emerald Group Publishing Limited
1463-5771 Paper type Technical paper
DOI 10.1108/14635771111147597

2. 1. Introduction Plant breakdown
Systems are planned, controlled and maintained with the objective to meet customer
requirement with a predetermined quality level and maximize the utilization of
available production capacity. As time passes, the machines age and un-planned
failures occur, causing the system performance to drift away from its initial state.
Therefore, the function of the system must be periodically restored to the desired level;
this is practically achieved by maintenance operations. The maintenance actions which 473
are normally classified as corrective/break down maintenance includes all actions
performed as a result of a failure to restore an item to a specified working condition,
while preventive maintenance (PM) includes all actions performed on an operating
equipment to restore it to a better condition. A maintenance strategy is a structured
combination of these two maintenance actions, which describes the events (e.g. failure,
passing of time, certain machine condition, etc.) and the type of action they trigger
(i.e. inspection, repair, maintenance or replacement).
United Arab Emirates (UAE) is one of the countries where in infrastructure
development and allied activities are rapid. With an increase in the Governments
spending on infrastructural projects such as roads, ports and airports, UAE is firmly at
the centre of the dynamic construction arena. In the year 2008-2009 there were
1,248 projects valued about 931 billion dollars under progress. The construction is the
third largest sector of the UAE economy after oil and trade, constituting US$23 billion,
about 6 percent of the GDP, even in the current post-financial crisis. Business Monitor
International (BMI) forecasts that the contribution of construction to the economy will
return to levels of over 10 percent of GDP in the years 2010 and 2011 (BMI, 2009). More
than 35 percent of the world’s heavy construction equipment and 25 percent of world’s
tower crane population are in the UAE.
Construction companies in the UAE generally execute the construction projects
always on an accelerated pace. Projects in the UAE are generally unique natured with
high level of risk, highly fragmented, competitive and with more numbers of challenges.
The construction companies need to utilize their available resources efficiently and
effectively to meet the project requirements and deadlines without sacrificing the quality
and safety. A huge worker force is required to complete these projects in time. A frequent
change imposed by the client and the Engineer, adds up to the existing problems and
causes lot of work disruption and cost overrun. Use of construction equipment eases
these problems to a great extent and helps the clients complete the project in the
stipulated period.
Construction projects are awarded to clients based on their past performance and the
infrastructure facilities owned by them. Construction plant and equipment contribute to
the success by providing enhanced output, reduced manpower requirement, ease of
work and timely completion of the project. The inter dependant activities in construction
industry requires the continuous working of all the machineries at all times without
interruption on the projects for better progress, productivity, and profits (Geert and
Liliane, 2002). The machinery dependency rate has become very high due to fast track
projects in the present time (John, 2002). The challenges faced in the construction
industries includes logistics management, horizontal/vertical transportation, material
handling, execution methods, interruptions, delays, prolonged duration of projects,
finishing trades, infrastructure requirements. Introduction of Construction plant and
machinery helps in minimizing the chaos and confusion created due to the above

3. BIJ problems and cut down monetary losses. In construction industries, despite predictive
18,4 maintenance being practiced, Plant breakdowns are inevitable due to the working
environment, age of the machines, over utilization of various systems. Construction
plant stumbling due to breakdowns, directly influence the project completion time and
the credibility of the contractor in the long run. Therefore, a need for a swift action during
breakdowns is felt in the construction plant maintenance system. Nevertheless, there is a
474 lack of reported literature on quick action plans for the corrective actions. Furthermore,
metrics are needed to evaluate the effectiveness of these corrective maintenance
strategies and support decisions regarding designing a new maintenance policy or
re-designing an existing one. Such metrics should be simple to use to facilitate their
application in today’s changeable environment.
2. Literature review
Well defined maintenance system will ensure optimal performance of the machineries.
Maintenance is often stated as “an activity carried out for any equipment to ensure its
reliability to perform its required functions” (Mishra and Pathak, 2002). These
maintenance strategies require increased commitments to training, resources,
improvement to conventional systems and integration, they also promise improved
performance (Laura, 2003). Break down maintenance is an unscheduled activity and has
numerous ill effects in countries like UAE, where the projects are completed on a fast
track. The construction plant breakdowns can make the project overrun on time and
results in subsequent loss of revenue to the project. Figure 1 shows the various factors
which are the results of unplanned and frequent breakdowns. The dissociation effects
shown above indicate that all the good and favorable conditions go away from the
contractor/organization, if repeated breakdowns on the plant and equipment occur
consistently.
In UAE, the construction companies operate with two kinds of plant assets, owned
and the leased/rented fleet. The leased/rented fleet of plant and equipment are generally
maintained by the rental companies. The plant machineries used in construction range
from small hand tools up to very heavy construction equipments, mechanical linkage
systems to complicated high-pressure hydraulic circuits including electrical, electronic,
and computer controls. The upkeep of these plant and equipment demand for proper
planning of maintenance strategies. In General, firms operate with the combination of
old/new equipment, rented/owned fleets, in dust prone polluted conditions with extreme
weather conditions. Even though there are many maintenance strategies followed, as the
general wear and tear of these plant and equipment are likely to be very high,
the breakdowns are inevitable. Oloke and Edwards (2001) mention that “the plant
breakdown and associated maintenance costs continue to affect the optimization of plant
utilization throughout the construction sector”. Hisham (2003), mentioned that “proper
maintenance of plant and equipment can significantly reduce the overall operating cost
while boosting the productivity of the plant”.
Fast track construction projects are highly dependent on the construction machineries.
The inter dependant activities in construction field requires the continuous working of all
the machineries at all times without interruption on the projects for better progress,
productivity, and profits (John, 2002). Present construction technologies are forced to exert
fast trend approach construction delays are linked with financial losses due to penalties,
etc. (Randy and George, 1988). With all of the above facts in place, if there are occurrences

4. Plant breakdown
Mental agony
for the Maint.
Crew Mental agony
Loss of
for the
morale
operators
475
Loss in
Replacement/ maintenance
hiring costs budget
Breakdown Loss of
Un-
rectification efficiency
planned/frequent
costs
construction
plant
breakdowns
Mental agony Loss of
for the project future jobs
team
Loss of good Loss of
will from client production
Delays to Risk of
programme safety Figure 1.
Effect of construction
machinery break down
of frequent plant and machinery breakdowns, the associated maintenance costs continue
to increase which directly affects the optimization of plant utilization throughout the
construction sector (Oloke and Edwards, 2001).
The literature contains numerous suggested maintenance policies/strategies, which
can be categorized as follows: Age-dependent PM polices. The PM actions (minimal,
imperfect or perfect) are triggered by the age of the component such as (T, n)
policy,where T stands for the time between perfect PM and n stands for the number
of failures between perfect maintenance (Sheu et al., 1995). Periodic PM policies – The
PM is pre-planned at fixed time intervals (Xiao-Gao, 1995). Sequential PM policies –
PM is carried out at age-dependent decreasing time intervals (Nakagawa, 1986). This
reference looks to be odd related to our breakdown maintenance arguments. But it does
state about the number of failures in a PM. I leave it for your final approval.
Lu and Meeker (1993) develop general statistical models and data analysis
methods for using degradation measures to estimate a time-to-failure distribution.
Susan et al. (2007) extend the problem of reliability estimation to a component operating

5. BIJ in real-time changing environments. Gebraeel et al. (2005), propose an exponential model
18,4 in which the deterministic parameters represent a constant physical phenomenon
common to all the components of a given population, while the stochastic ones follow a
specific distribution and capture variations among individual components, nominally
identical. The distributions of the stochastic parameters across the population of
components (a-priori distributions) together with the monitoring information collected
476 for each component (a-posteriori distribution) are used to compute the residual life
distribution for the individual component. A Bayesian approach is employed to update
`
the prior information of each individual component at any instant. Curcuru et al. (2010),
proposed a procedure for computation of the maintenance time that minimizes the global
maintenance cost. By adopting a stochastic model for the degradation process and
by hypothesizing the use of an imperfect monitoring system, the procedure updates by
a Bayesian approach, the a-priori information, using the data coming from the
monitoring system.
Meselhy et al. (2010), developed a periodicity metric functional resetting procedure
to evaluate and quantify function resetting due to a given maintenance policy to reduce
complexity in the system. The developed periodicity metric can be used as a criterion
for comparing different maintenance policy alternatives and as a tool for predicting
system performance under a given maintenance policy.
Very few researchers have conducted studies done on the data capturing and
modeling of breakdowns as breakdowns contribute lots of uncertainties to the plant
performance and productivity. Sawhney et al. (2009) tremendous efforts have been made
to develop different types of maintenance strategies for enhancing the performance of
equipment but nothing has been done to actually streamline breakdown maintenance
activities. Sawhney et al. (2009), proposed a value stream mapping procedure to evaluate
breakdown maintenance operations (Henry, 1993), mentioned that research in the UK
has shown that plant downtime accounts for an average of four working days per item,
each year. Watts (1994), also mention that during breakdowns, the capital money
invested in the construction plant and equipment fail to work for the business, placing
strain on site productivity, and ultimately the organization’s liquidity. Canter (1993),
indicated that plant breakdown relates to the state in which a plant item is temporarily,
or permanently, unusable. The breakdown of equipment occurs due to the unpredictable
failure of components and due to gradual wear and tear of the parts, which cannot be
prevented.
In the past, many authors have investigated and proposed numerous models to
improve the plant’s performance based on predictive maintenance. Very few the authors
have examined the effect of break down maintenance on the construction plant.
No detailed algorithms for breakdown maintenance in construction plant or models
based on the records of break down maintenance have been reported in the literature.
The current research work aims to develop a systematic procedure to identify a strategic
procedure to minimize the loss in a construction industry due to breakdown
maintenance. The paper focuses on the study of the breakdowns in the system rather
than developing a PM for the breakdowns, the focus is to how quickly the system can
recover from the break down that has incurred in the system. The real-time reporting of
the plant history is examined to understand and determine the factors affecting the
breakdown process, overcoming these factors to manage the breakdowns effectively.
Based on the study, BMCs & BSCs are identified. These BMCs and BSCs, are subsequent

6. used to developed and deployed dedicated groups of Breakdown maintenance teams Plant breakdown
into the system, which can attend to the break downs and minimize the recovery time.
The size and the number of teams deployed are arrived from the past history of the BMC
and BSC in the system. The current paper reports on identifying the BMC and BSC, while
the next paper will report on the overall performance of the proposed maintenance
strategy. The next presents the proposed research methodology of break down
maintenance for construction plant management. 477
3. Research methodology
Pareto analysis is a statistical technique in decision making that is used for selection of
a limited number of tasks that produce significant overall effect. It uses the principle –
the idea that by doing 20 percent of work can generate 80 percent of the advantage of
doing the entire job. Or in terms of quality improvement, a large majority of problems
(80 percent) are produced by a few key causes (20 percent). The Pareto’s chart is drawn
using the data collected to identify the significant few and insignificant many.
For example, 20 percent of the workers will cause 80 percent of the problems, while
another 20 percent of the personnel will deliver 80 percent of our entire production. In
essence, the problem-solver estimates the benefit delivered by each action, then selects
a number of the most effective actions that deliver a total benefit reasonably close to
the maximal possible one. Pareto analysis is a creative way of looking at causes of
problems because it helps stimulate thinking and organize thoughts. However, it can
be limited by its exclusion of possibly important problems which may be small
initially, but which grow with time.
The purpose is to highlight the most important among a (typically large) set of
factors. A Pareto chart provides facts needed for setting priorities. It organizes and
displays information to show the relative importance of various problems or causes of
problems. It is essentially a special form of a vertical bar chart that puts items in order
(from the highest to the lowest) relative to some measurable effect of interest: frequency,
cost, time. Placing the items in descending order of frequency makes it easy to discern
those problems that are of greatest importance or those causes that appear to account for
most of the variation. Thus, a Pareto chart helps teams to focus their efforts where they
can have the greatest potential impact.
The Pareto’s law is used as an effective tool for equipment maintenance management
in the areas of breakdown maintenance analysis, maintenance expenditure analysis,
routine PM, critical analysis of maintenance lags, defect analysis on components, unsafe
practice analysis and accident analysis.
The past breakdown records of the firm is the input for the analysis. Figure 2 shows
the flow chart of the process of examining the breakdown maintenance record.
The effective execution of the breakdown maintenance process depends on the
uninterrupted, unambiguous, effective execution of breakdown maintenance function.
The various contributors/breakdown factors, which influence the breakdowns on the
plant and equipment, are listed based on their occurrence. These breakdown factors
contribute to the breakdown down hours and to the general overall breakdown
percentage of the target organization’s plant and equipment. The fish bone diagrams are
constructed to identify the factors responsible for the different types of breakdowns in
the construction plant. Based on their occurrences, these factors are given specific codes
called breakdown main codes (BMC). Break down sub codes (BSC) are identified from

7. BIJ Construction plant breakdown maintenance management
with the introduction of breakdown factors and criticality analysis
18,4
Breakdown
data of the
Projects/operators target company
customer Inspection
and records
478 Breakdown main
code (BMC) and
Breakdown calls/ breakdown sub
complaints/breakdowns codes (BSC) creation Check previous
records
Identify roaming/
Analysis of breakdown codes
local B/D team
Fault enquiry and with Pareto’s principle
analysis
Breakdown
call register
Execute breakdown
Figure 2.
Construction plant
breakdown maintenance Dispatching
management flow chart
the sub parameters for each BMC. Pareto’s model intends to study the effect of
breakdown factors which contribute 80 percent of the breakdowns and identify the
critical BMC’s and the BSC’s. This list further examined for the symptoms and the
reasons of these breakdowns. The significant breakdown contributing factors based on
their criticality are identified for the benefit of the organization as a whole.
4. Case study
A detailed study on the pattern of breakdown of plant and machinery in construction
scenario is performed. The selected target company operates in the Middle East region
with its headquarters at Dubai, UAE. They are among the top ten construction
companies in the Middle East. The study feature and the findings are in general
applicable to similar construction organizations in the region. The construction projects
executed includes residential, industrial/commercial and infra structure works. In the
past, disruption of the construction activities due to machinery break down accounts to
1.5 to 2 percent of the total working hours of the system. In spite of being a small
percentage, the revenue lost in the process is fairly high. A well structured predictive
maintenance procedure is followed to ensure the system works without any interruption.
In spite of these precautionary measures, breakdown of the construction machineries is
inevitable due to the prevailing work environment, missed PM schedules, damage
caused to machines during accidents, etc. To identify the critical breakdown factor,
previous breakdown data are investigated. The records from different sites are
consolidated for the study.

8. The firm under investigation has more than 779 different construction machineries Plant breakdown
which exclude transportation vehicles. The machineries mix included light equipment,
heavy equipment, light machinery, heavy plant, and heavy machinery. Since light
equipment (290) is relatively smaller in size replacement is always possible. Light
equipments are not included in our study. Heavy plant like tower cranes and hoists (81)
which operate basically with electric power only were not considered for analysis. The
selected equipment included, Wheel Loaders, Skid Steer Loaders, Back Hoe Loaders, 479
Dumpers, Mobile Cranes, Forklifts, Compressors, Generators and Roller Compactors.
The total number of machineries considered is 189. This represents 38.5 percent of the
population of the equipment excluding the light equipments. A total of 741 (Table I)
breakdowns from the four year record of the breakdown maintenance data for the
selected plant and equipment have been analyzed. The documents considered include
the breakdown registers, jobs cards, plant history cards, etc.
The breakdown data of the selected nine machineries has been taken from the list of
total breakdown records of all the machineries available with the target organization.
Since the focus is on these nine machineries, the list of 741 breakdown data only on
these machineries has been considered for the analysis. To determine the most critical
machine in the system, the ratio of the number of breakdown to available machines is
calculated. The machine with the highest ratio is identified as the critical machine as
indicated in Table I. Wheel loader is identified as the most critical machine with the
highest breakdown in the system.
The last four year breakdown records for the wheel loader are further examined.
The breakdown records are classified into five main categories of failure namely:
mechanical failure, hydraulic failure, electrical failure and tyre failure such as
punctures, tyre burst, etc. A systematic examination on the various breakdowns is
performed and are classified into one of the above four categories based on the major
factor for failure as provided in Table II. About 44.75 percent of the breakdowns are
due to Mechanical failure followed by Hydraulic system failure (24.75 percent) and
15.25 percent failures due to tyre burst, punctures, etc. System halting due to faulty
electrical system was found to be less when compared to the other kinds. Rectification
of electrical failures was usually mere replacement of the worn or fused system and the
time consumed was found to be less based on the inventory of the electrical item held in
the system, breakdown recovery time was found to be a function of the inventory
holding and was neglected for further investigation.
A cause effect analysis (CEA) is performed to list out the various possible factors
that could contribute to the different types of failures which can occur in the machine
system/component and is shown in Figure 3. The mechanical failure constitutes both
mechanical and engine failures. The outcome of the CEA provided an insight into the
possible break down factors in the system. These breakdown factors revealed their
relationships with the various components and their impact on the overall performance
of the machine. To effectively categorize the breakdowns in relation with their
components, various codes namely BMC and breakdown sub codes (BSC) were
developed. To identify the BSC’s, second level CEA is performed on the identified BMC
as shown in Figure 4. The BSCs are developed based on the various breakdown data,
logical discussions, and on the breakdown knowledge of the maintenance crew.
For example, in the case of the Wheel Loader, breakdown for duration of 150 hours
has been recorded for the breakdown factor “engine oil, coolant oil mixing”.

9. BIJ
18,4
480
Table I.
in the system
Breakdown details
of the critical machines
2005 2006 2007 2008
No of Breakdown/ No of Breakdown/ No of Breakdown/ No of Breakdown/ Average
machines No of machine machines No of machine machines No of machine machines No of machine Total breakdown/
Sl.no Machine available breakdowns ratio available breakdowns ratio available breakdowns ratio available breakdown ratio breakdowns machine ratio
1 Wheel
loader 2 9 4.5 2 18 9 2 44 22 3 35 11.66 106 11.79
2 Mobile
crane 6 14 2.33 4 14 3.5 4 25 6.25 5 20 4 73 4.02
3 Back hoe
loader 2 3 1.5 2 16 8 2 5 2.5 5 10 2 34 3.50
4 Fork lift 29 36 1.24 31 53 1.70 39 85 2.17 97 79 0.81 253 1.48
5 Skidsteer 4 8 2 11 15 1.36 11 13 1.18 14 9 0.64 45 1.29
6 Genset 5 2 0.4 4 8 2 4 7 1.75 4 3 0.75 20 1.20
7 Dumper 21 23 1.09 19 26 1.36 19 23 1.21 19 20 1.05 92 1.18
8 Air
compressor 14 20 1.42 21 26 1.23 21 17 0.80 21 17 0.80 80 1.07
9 Roller
compactor 9 10 1.11 8 9 1.12 9 12 1.33 11 7 0.63 38 1.05

10. CEA diagram is used to study this problem as shown in Figure 4. This breakdown is Plant breakdown
attributed to the following factors: performance of the engine, and also due to the
coolant oil mixing with the engine oil. This results in reduction in volume of cooling oil,
or/hence excess smoke from the engine and also creates more adverse effects due to
wear and tear on the engine. From the Figure 4, the cause of the engine failure
481
Failure type 2005 2006 2007 2008 Average
Mechanical failure 63 87 29 0 44.75 Table II.
Hydraulic failure 4 2 30 63 24.75 Percentage of different
Tyre bust and allied failure 27 7 13 14 15.25 types of failure in
Electrical failure 0 4 15 9 7.00 wheel loader
Mechanical failures Engine failures Hydraulic failures
Transmission Engine overheat Hydraulic hose cut
Acelerator Engine oil-coolent mixing Oil leak
Gear box Water leak and Oil seal failure
pump gasket leak
Plant
break
down
Tyre trapped inside Battery down
the sand
Tyre burst Self motor
Alternator
Figure 3.
Basic failure in
Tyre puncture Headlights construction plant
Tyre problems
and machineries
Electrical failures
BMC BMC BMC BMC BMC
Knocking No cranking/ Engine Coolant oil
no starting vibration excessive Overhauling
sound consumed
Engine
failures
Figure 4.
Basic engine
Speed Starting Low oil
Overheating
failures – BMC
variation trouble pressure
identification
BMC BMC BMC BMC

11. BIJ and the cause of oil related failures are examined. As the breakdown of the wheel
18,4 loader is due to the factor of engine oil and coolant oil mixing and this would have lead
to the excessive consumption of coolant oil in the engine and hence the best fit attribute
from Figure 4 is Coolant Excessive Consumed. This factor is identified as the BMC.
Now to find out various follow on effects of this excessive coolant oil consumption, the
Cause and effect diagram is drafted as shown in Figure 5, to understand the effects of
482 this cause which reveal various sub effects of this main code and will help us in
understanding the nature of the breakdown in detail. These are called as BSC. The
similar fish bone diagrams are constructed for each type of primary failure to arrive at
the BMCs and BSCs. The identified BMC’s are further scrutinized to examine their level
of participation in the breakdown of the selected machines. The BMC and BSC are
subjected to a Pareto Analysis.
The Pareto’s analysis charts reveals the details of the factors which are the cause of
80 percent of the breakdowns happening in the target company on the selected
equipment group. Table III, highlights the identified BMCs based on the above Pareto
analysis. A total of 78 different BMC’s were identified for the selected four equipment
namely wheel loader, Mobile Crane, Back hoe loader and Fork lifter. Based on the
Pareto analysis, 38 BMC’s contribute to the 80 percent of the breakdowns, which
implies that only 66.2 percent of the BMCs contribute to more than 80 percent of the
overall breakdowns on these selected equipment. Providing adequate attention on
these BMC’s, will lead to reduction of breakdown percentage of the target company
(Figures 6 and 7).
5. Results and Discussions
BMCs and BSC’s identified using the CEA diagrams and ranked using Pareto analysis
as listed in Table IV. The first column indicates the BMC followed by the total number of
breakdown cumulative percentage over a period of 4 years it has contributed. The
possible BSCs which could contribute to these breakdowns are also listed in the column 2
onwards. A detailed study on Table V also reveals the factor that certain BSCs account
for failure on all the four equipments considered for the analysis. One reason for this
BMC BMC BMC BMC
Engine oil/ Coolant water
Water leak/ Water leak, rail
coolant oil leak through
pump gasket pipe bolt cut
mixing radiator
BMC
Coolant oil
exessive
consumed
Coolant water
Figure 5. Pump leak leak through
Radiator
leak
BSC analysis from BMC radiator hose
BMC BMC BMC

12. BMC contributors for 100 percent breakdowns Criticality effect – reduced BMC contributors for 80 percent breakdowns Description of BMC
AA3 AA3 Coolant oil excessive consumed
AA1 AA1 Engine major overhauling
AA11 AA11 Engine not cranking will not start
AA2 AA2 Engine over heating
AA4 AA4 Engine low oil pressure
AA5 AA5 Engine oil excessive consumed
AA6 Engine vibration
AA7 AA7 Engine knocking noise
AA8 AA8 Engine speed variation
AA9 Improper colour of exhaust
BB1 BB1 Fip, injector calibration
BB2 Engine knocking sound
BB3 BB3 Engine cranks but did not start
BB4 BB4 Engine hard to start
BB5 Engine speed variation
BB6 BB6 Engine vibration
BB7 BB7 Engine emits white smoke
BB8 BB8 Lack of power
BB9 Excessive fuel consumption
CC1 CC1 Grinding noise when changing gear
CC11 CC11 Gear engine problem
CC2 CC2 Gear box noising while travelling
CC4 Gear shift difficulties
CC6 CC6 Machine does not drive in any gear
CC8 CC8 Excessive oil consumption
DD1 DD1 Clutch slipping when clutch apply
DD10 DD10 Crown wheel noise
DD11 DD11 Differential oil loss
DD12 DD12 Gear cannot engage 4Wd/2Wd/ reverse
DD2 Clutch pedal hard
DD3 Clutch juddering
DD4 DD4 Noise while travelling
DD5 DD5 Machine not achieve maximum speed
DD8 Loss of oil
DD9 DD9 Starting trouble
(continued)
BMC contributor
Plant breakdown
in the system
Table III.
483

13. BIJ
18,4
484
Table III.
BMC contributors for 100 percent breakdowns Criticality effect – reduced BMC contributors for 80 percent breakdowns Description of BMC
EE1 EE1 Brake is ineffective
EE12 Air pressure drop
EE16 EE16 Operating force of brake pedal is too light
EE17 EE17 Operating force of brake pedal is too heavy
GG3 GG3 Un even tyre wear
GG4 Tyre one side wear toe in
GG5 Tyre one side wear toe out
GG6 Wheel hubs lubrication leakage
GG7 GG7 Speed too slowly (tyre air loss)
GG8 Wheel excusive noise while brake
HH1 Excessive play in the steering
HH2 HH2 Hard steering
HH5 Steering vibration
HH8 HIIS Steering pump pressure reduced
HH9 Steering wheel is sluggish
II3 Tyre uneven wear
JJ1 JJ1 Starting trouble
JJ2 Engine not cranking will not start
JJ3 JJ3 Engine cranking will not start
JJ4 JJ4 Entire electrical function not work
JJ5 Speed variation (electronic control sensors)
JJ6 JJ6 Excessive noise develop (engine/genset)
JJ7 Voltage fluctuation/dropped
JJ8 JJ8 Wiring/circuit not functioning
KK1 KK1 Exhaust system leak
KK2 Air conditioning system failure
LL1 LL1 Lack of power in all hydraulic functions
LL2 LL2 All hydraulic rams slow to operate
LL4 LL4 Poor performance slow operating speed
LL5 LL5 Ram creep
LL6 LL6 Hydraulic oil loss – leaking section – seals
LL7 Electrical detent will not hold
LL8 LL8 Mechanical detent will not hold

14. Wheel loader 2005
Code hrs per cp
80 120%
Mobile crane 2005
Code hrs per cp
250 120%
Plant breakdown
DD4 60.5 63% 63% 100% LL5 195 54% 54%
60 200 100%
GG7 26 27% 90% 80% EE17 77 21% 75%
AA3 6 6% 96% 40 60% hrs AA8 30 8% 83% 80%
150
CC8 4 4% 100% 40% cp LL2 20 6% 89%
20 60% hrs
20% LL4 19 5% 94% 100
0 40% cp
0% JJ1 8 2% 96%
DD4 GG7 AA3 CC3
EE12 4.5 1% 97% 50
20%
GG7 4 1% 98%
0 0%
LL6
JJ2 2.5
3 1% 99%
1% 100%
LL5 EE17 AA8 LL2 LL4 JJ1 EE12 GG7 LL6 JJ2
485
Wheel loader 2006 Mobile crane 2006
Code hrs per cp Code hrs per cp 300 120%
600 150%
DD12 480 82% 82% LL8 250 46% 46% 250 100%
GG7 28.5 5% 86% 400 100% LL6 109 20% 66% 200 80%
JJ2 24 4% 91% hrs JJ8 55 10% 76%
200 50% 150 60% hrs
HH2 20 3% 94% cp GG7 44 8% 84%
100 40% cp
JJ8 14 2% 96% 0% LL5 32.5 6% 90%
0 50 20%
GG5 11.5 2% 98% DD12GG7 JJ2 HH2 JJ8 GG5 LL6 AA1 22 4% 94%
AA3 20 0 0%
LL6 10 2% 100% 4% 98%
AA5 10 2% 100% LL8 LL6 JJ8 GG7 LL5 AA1 AA3 AA5
Wheel loader 2007 Mobile crane 2007
Code hrs per cp Code hrs per cp 60 120%
350 120%
EE1 297 35% 35% LL4 50 16% 16%
300 100%
EE16 180 21% 56% GG7 48.5 16% 32%
50 100%
AA2 103 12% 68% 250 JJ8 47 15% 47%
80%
GG7 99 12% 80% 200 AA3 46 15% 62%
60% 40 80%
JJ1 75 9% 89% 150 hrs DD1 28 9% 71%
LL6 28 3% 92% 40% cp KK1 13 4% 75%
100
GG6 20 2% 94% JJ3 12 4% 79% 30 60%
20% hrs
19 2% 96% 50 LL8 82%
JJ8 10 3%
cp
JJ3 17 2% 98% 0 0% AA8 10 3% 85%
20 40%
EE1
EE16
AA2
GG7
JJ1
LL6
GG6
JJ8
JJ3
DD8
JJ4
AA8
DD8 6 1% 99% JJ1 9 3% 88%
JJ4 4 0% 100% LL7 8 3% 91%
AA8 3 0% 100% LL6 8 3% 94% 10 20%
LL1 7 2% 96%
KK2 6 2% 98% 0 0%
LL4
GG7
JJ8
AA3
DD1
KK1
JJ3
LL8
AA8
JJ1
LL7
LL6
LL1
KK2
AA2
JJ2
LL2
AA2 3 1% 99%
JJ2 2 1% 99%
LL2 2 1% 100%
Wheel loader 2008 Mobile crane 2008
Code hrs per cp Code hrs per cp 400 120%
400 120%
DD11 330 45% 45% 100% AA4 350 39% 39%
300 350
AA3 150 21% 66% 80% JJ8 150 17% 55% 100%
GG7 112 15% 200 60% 300
81% DD5 104 11% 67%
40% hrs 80%
JJ1 70 10% 91% 100 AA1 75 8% 75% 250
BB2 40 5% 96% 20% cp LL5 73 8% 83% 200 60% hrs
0 0%
LL6 16 2% 99% JJ1 56 6% 89%
7
11
2
3
1
2
6
3
150
G
cp
JJ
A
JJ
BB
LL
BB
D
G
7
A
BB3 1% 100% GG7 29 3% 92% 40%
D
JJ2 3 0% 100% AA8 25 3% 95% 100
GG4 13 1% 50
20%
Figure 6.
12 1%
JJ2
0 0% Pareto analysis for wheel
AA4
JJ8
DD5
AA1
LL5
JJ1
GG7
AA8
GG4
JJ2
DD8
LL6
DD8 10 1%
LL6 8 1% loader and mobile crane
could be that all the four considered equipments fall under heavy lifting/earth
moving/material handling system machines and are used to load/unload and
transport materials. The BSCs common to all machines are listed in Table VI. Failure
due to tyre puncture and allied problems contribute to maximum failure. A strategy plan
to minimize this type of failure is proposed. With the identification of these most
contributing BMC’s and BSC’s, it is further proposed to develop an “overall breakdown
maintenance management”, where in dedicated and focused / targeted breakdown
maintenance teams are formulated. The size and the number of breakdown teams and
other resources depend on the occurrence of BMC and BSC in the system. There need to
be an added approach of further enlightening the BMC to BSC into more details. The
results of implementing further dedicated breakdown maintenance management
approach will be presented in part II of this article.

15. BIJ Backhoe loader 2005
Code hrs per cp
Forklift 2005
Code hrs per cp
18,4 LL4 50 67% 67%
60
50
120%
100%
GG3 40 98% 98%
50
40
101%
100%
LL2 20 27% 93% GG7 1 2% 100%
40 80%
GG7 5 7% 100% 30 99%
30 60% hrs hrs
20 98%
20 40% cp
cp
10 97%
10 20%
486 0
LL4 LL2 GG7
0% 0
GG3 GG7
96%
Backhoe loader 2006 Forklift 2006
Code hrs per cp Code hrs per cp 120%
90 120% 50
DD10 80 34% 34% HH2 25 29% 29%
80
JJ8 70 30% 64% 100% EE1 20 23% 52% 25 100%
70
LL6 40 17% 81% LL5 18 21% 72%
DD11 10 4% 85% 60 80% JJ8 11 13% 85% 20 80%
EE16 10 4% 89% 50 JJ2 7 8% 93%
60% hrs 15 60% hrs
AA6 10 4% 93% 40 GG5 4 5% 98%
DD8 6 3% 96% cp GG7 2 2% 100% cp
30 40% 10 40%
JJ3 6 3% 98%
20
GG7 4 2% 100% 20% 5 20%
10
0 0% 0 0%
DD10
JJ8
LL6
DD11
EE16
AA6
DD8
JJ3
GG7
HH2 EE1 LL5 JJ8 JJ2 GG5 GG7
Backhoe loader 2007 Forklift 2007
Code hrs per cp Code hrs per cp
16 120% 50 120%
GG7 15 65% 65% JJ1 84 76% 76%
14 80
AA8 5 22% 87% 100% GG7 15 14% 89% 100%
12 70
LL6 3 13% 100% 80% AA2 8 7% 96% 60 80%
10
LL6 4 4% 100% 50
8 60% hrs 60% hrs
40
6 cp cp
40% 30 40%
4
20
20% 20%
2 10
0 0% 0 0%
GG7 AA8 LL6 JJ1 GG7 AA2 LL6
Backhoe loader 2008 Forklift 2008
Code hrs per cp Code hrs per cp
60 120% 50 120%
GG7 52 41% 41% AA2 46 71% 71%
LL8 26 21% 62% 50 100% EE16 10 16% 87% 100%
40
BB3 20 16% 78% 40 80% GG8 8.5 13% 100% 80%
CC11 16 13% 90% 30
30 60% hrs 60% hrs
AA2 8 6% 97%
20
EE16 4 3% 100% 20 40% cp 40% cp
10 20% 10 20%
Figure 7.
0 0% 0 0%
Pareto analysis for back GG7 LL8 BB3 CC11 AA2 EE16 AA2 EE16 GG8
hoe loader and fork lifter
BMC contributor for 100 percent Critical BMC identified based
S. no Equipment breakdown (identified) on Pareto analysis
1 Mobile crane 27 15
2 Wheel loader 22 8
Table IV. 3 Back hoe loader 16 8
Pareto analysis 4 Fork lift 13 7
on the BMCs Total BMC 78 38

16. Related BSCs influencing the BMC (no of occurrences of the failure mode
Plant breakdown
BMC (no of failures) in bracket)
Wheel loader
DD12(82) D26(82)
DD4(63) D18(63)
GG7(58) G1(11) G2(12) G3(16) G4(14) G5(4) G6(1) 487
DD11(45) D22(45)
EE1(35) E5(2) E7(1) E8(33)
EE16(21) E2(21)
AA3(21) A38(21)
AA2(12) A43(12)
Forklift
AA2(71) A43(71)
EE1(23) E7(23)
EE16(16) E3(16)
GG3(98) G6(98)
GG7(14) G2(7) G4(7)
HH2(29) H12(29)
JJ1(76) J1(25) J10(2) J14(49)
JJ8(13) J24(4) J25(9)
LL5(21) L6(21)
Back hoe loader
AA8(22) A55(22)
BB3(15) B20(15)
DD10(34) D21(34)
GG7(105) G1(61) G2(18) G3(5) G4(14) G6(6)
JJ8(30) J8(30)
LL4(67) L5(67)
LL8(23) L11(23)
LL6(17) L2(17)
Mobile crane
AA1(6) A6(6)
AA3(15) A3(15)
AA4(29) A53(29)
AA8(8) A30(8)
DD1(9) D6(9)
DD5(8) D6(8)
EE17(21) E4(11) E28(10)
GG7(24) G3(11) G4(11) G5(2)
JJ8(37) J22(22) J36(8)
JJ3(4) J26(3) J31(1)
LL5(54) L4(36) L6(18)
LL8(49) L11(49)
LL6(47) L2(1) L15(5) L16(34) L18(6)
LL4(16) L8(16) Table V.
KK1(4) K4(2) K8(2) Cumulative effect
analysis of BMC
Note: Cumulative Percentage over four years 2005-2008 period and related BSC

17. BIJ
Identified No of failures Contribution on total
18,4 BSC associated with Description of the break down sub code failure (%)
A7 24 Cooling water leak radiator service 8.77
A43 83 Radiator serviced 10.90
G2 37 Tyre puncture and deflate due to other 24.88
488 G4 46 reasons 17.06
Table VI. G6 105 7.58
BSCs common to all G1 72 19.67
the four machines G3 32 5.69
in the system L11 23 Hydraulic pump problems 5.45
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About the authors
P.B. Ahamed Mohideen is pursuing research in the field of breakdown maintenance management
to construction plant and equipment. He is a research scholar with Birla Institute of Technology
and Science, Pilani, India. Presently he is working as Asst. General Manager – Plant,
with ETA-Ascon Group, a multinational organization at Dubai, UAE. He is acquainted with a
good amount of knowledge of the construction equipment and their performance in the
region. P.B. Ahamed Mohideen is the corresponding author and can be contacted at:
pbahamed@gmail.com
M. Ramachandran is presently the Director of BITS Pilani, Dubai Campus. He has contributed
a great amount of research work and support on energy management studies. He is associated with
many international journals. He has published many articles and papers in this field.
Rajam Ramasamy Narasimmalu is a Faculty at CIT, India. His research interests are
Push-Pull hybrid models and the impact of layer manufacturing on supply chain management
(SCM) and layer manufacturing in general. Prior to his academic experience he also served the
industry. He has published many technical articles in his field of interest.
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