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  • 1. The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htmBIJ18,4 Construction plant breakdown criticality analysis – part 1: UAE perspective472 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, attributedBenchmarking: An International from the breakdown parameters derived from the previous history of the work records/environment.JournalVol. 18 No. 4, 2011 Keywords Construction, Pareto analysis, Plant and equipment, Maintenance management, Breakdowns,pp. 472-489 Criticality analysis, United Arab Emiratesq Emerald Group Publishing Limited1463-5771 Paper type Technical paperDOI 10.1108/14635771111147597
  • 2. 1. Introduction Plant breakdownSystems are planned, controlled and maintained with the objective to meet customerrequirement with a predetermined quality level and maximize the utilization ofavailable production capacity. As time passes, the machines age and un-plannedfailures 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 473are normally classified as corrective/break down maintenance includes all actionsperformed 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 operatingequipment to restore it to a better condition. A maintenance strategy is a structuredcombination 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 infrastructuredevelopment and allied activities are rapid. With an increase in the Governmentsspending on infrastructural projects such as roads, ports and airports, UAE is firmly atthe centre of the dynamic construction arena. In the year 2008-2009 there were1,248 projects valued about 931 billion dollars under progress. The construction is thethird 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 MonitorInternational (BMI) forecasts that the contribution of construction to the economy willreturn to levels of over 10 percent of GDP in the years 2010 and 2011 (BMI, 2009). Morethan 35 percent of the world’s heavy construction equipment and 25 percent of world’stower crane population are in the UAE. Construction companies in the UAE generally execute the construction projectsalways on an accelerated pace. Projects in the UAE are generally unique natured withhigh level of risk, highly fragmented, competitive and with more numbers of challenges.The construction companies need to utilize their available resources efficiently andeffectively to meet the project requirements and deadlines without sacrificing the qualityand safety. A huge worker force is required to complete these projects in time. A frequentchange imposed by the client and the Engineer, adds up to the existing problems andcauses lot of work disruption and cost overrun. Use of construction equipment easesthese problems to a great extent and helps the clients complete the project in thestipulated period. Construction projects are awarded to clients based on their past performance and theinfrastructure facilities owned by them. Construction plant and equipment contribute tothe success by providing enhanced output, reduced manpower requirement, ease ofwork and timely completion of the project. The inter dependant activities in constructionindustry requires the continuous working of all the machineries at all times withoutinterruption on the projects for better progress, productivity, and profits (Geert andLiliane, 2002). The machinery dependency rate has become very high due to fast trackprojects in the present time (John, 2002). The challenges faced in the constructionindustries includes logistics management, horizontal/vertical transportation, materialhandling, execution methods, interruptions, delays, prolonged duration of projects,finishing trades, infrastructure requirements. Introduction of Construction plant andmachinery helps in minimizing the chaos and confusion created due to the above
  • 3. BIJ problems and cut down monetary losses. In construction industries, despite predictive18,4 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 a474 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 downof frequent plant and machinery breakdowns, the associated maintenance costs continueto increase which directly affects the optimization of plant utilization throughout theconstruction sector (Oloke and Edwards, 2001). The literature contains numerous suggested maintenance policies/strategies, whichcan 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 numberof failures between perfect maintenance (Sheu et al., 1995). Periodic PM policies – ThePM 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). Thisreference looks to be odd related to our breakdown maintenance arguments. But it doesstate 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 analysismethods 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 model18,4 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 collected476 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 breakdowninto 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 BMCand BSC in the system. The current paper reports on identifying the BMC and BSC, whilethe next paper will report on the overall performance of the proposed maintenancestrategy. The next presents the proposed research methodology of break downmaintenance for construction plant management. 4773. Research methodologyPareto analysis is a statistical technique in decision making that is used for selection ofa 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 ofdoing 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 drawnusing 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, whileanother 20 percent of the personnel will deliver 80 percent of our entire production. Inessence, the problem-solver estimates the benefit delivered by each action, then selectsa number of the most effective actions that deliver a total benefit reasonably close tothe maximal possible one. Pareto analysis is a creative way of looking at causes ofproblems because it helps stimulate thinking and organize thoughts. However, it canbe limited by its exclusion of possibly important problems which may be smallinitially, but which grow with time. The purpose is to highlight the most important among a (typically large) set offactors. A Pareto chart provides facts needed for setting priorities. It organizes anddisplays information to show the relative importance of various problems or causes ofproblems. 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 discernthose problems that are of greatest importance or those causes that appear to account formost of the variation. Thus, a Pareto chart helps teams to focus their efforts where theycan have the greatest potential impact. The Pareto’s law is used as an effective tool for equipment maintenance managementin the areas of breakdown maintenance analysis, maintenance expenditure analysis,routine PM, critical analysis of maintenance lags, defect analysis on components, unsafepractice analysis and accident analysis. The past breakdown records of the firm is the input for the analysis. Figure 2 showsthe flow chart of the process of examining the breakdown maintenance record.The effective execution of the breakdown maintenance process depends on theuninterrupted, unambiguous, effective execution of breakdown maintenance function.The various contributors/breakdown factors, which influence the breakdowns on theplant and equipment, are listed based on their occurrence. These breakdown factorscontribute to the breakdown down hours and to the general overall breakdownpercentage of the target organization’s plant and equipment. The fish bone diagrams areconstructed to identify the factors responsible for the different types of breakdowns inthe construction plant. Based on their occurrences, these factors are given specific codescalled 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 analysis18,4 Breakdown data of the Projects/operators target company customer Inspection and records478 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 breakdownFigure 2.Construction plantbreakdown maintenance Dispatchingmanagement 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 breakdownwhich exclude transportation vehicles. The machineries mix included light equipment,heavy equipment, light machinery, heavy plant, and heavy machinery. Since lightequipment (290) is relatively smaller in size replacement is always possible. Lightequipments 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. Theselected equipment included, Wheel Loaders, Skid Steer Loaders, Back Hoe Loaders, 479Dumpers, Mobile Cranes, Forklifts, Compressors, Generators and Roller Compactors.The total number of machineries considered is 189. This represents 38.5 percent of thepopulation 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 theselected plant and equipment have been analyzed. The documents considered includethe breakdown registers, jobs cards, plant history cards, etc. The breakdown data of the selected nine machineries has been taken from the list oftotal 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 onthese machineries has been considered for the analysis. To determine the most criticalmachine in the system, the ratio of the number of breakdown to available machines iscalculated. The machine with the highest ratio is identified as the critical machine asindicated in Table I. Wheel loader is identified as the most critical machine with thehighest 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 aspunctures, tyre burst, etc. A systematic examination on the various breakdowns isperformed and are classified into one of the above four categories based on the majorfactor for failure as provided in Table II. About 44.75 percent of the breakdowns aredue to Mechanical failure followed by Hydraulic system failure (24.75 percent) and15.25 percent failures due to tyre burst, punctures, etc. System halting due to faultyelectrical system was found to be less when compared to the other kinds. Rectificationof electrical failures was usually mere replacement of the worn or fused system and thetime consumed was found to be less based on the inventory of the electrical item held inthe system, breakdown recovery time was found to be a function of the inventoryholding and was neglected for further investigation. A cause effect analysis (CEA) is performed to list out the various possible factorsthat could contribute to the different types of failures which can occur in the machinesystem/component and is shown in Figure 3. The mechanical failure constitutes bothmechanical and engine failures. The outcome of the CEA provided an insight into thepossible break down factors in the system. These breakdown factors revealed theirrelationships with the various components and their impact on the overall performanceof the machine. To effectively categorize the breakdowns in relation with theircomponents, various codes namely BMC and breakdown sub codes (BSC) weredeveloped. To identify the BSC’s, second level CEA is performed on the identified BMCas 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 hourshas 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 ratio1 Wheel loader 2 9 4.5 2 18 9 2 44 22 3 35 11.66 106 11.792 Mobile crane 6 14 2.33 4 14 3.5 4 25 6.25 5 20 4 73 4.023 Back hoe loader 2 3 1.5 2 16 8 2 5 2.5 5 10 2 34 3.504 Fork lift 29 36 1.24 31 53 1.70 39 85 2.17 97 79 0.81 253 1.485 Skidsteer 4 8 2 11 15 1.36 11 13 1.18 14 9 0.64 45 1.296 Genset 5 2 0.4 4 8 2 4 7 1.75 4 3 0.75 20 1.207 Dumper 21 23 1.09 19 26 1.36 19 23 1.21 19 20 1.05 92 1.188 Air compressor 14 20 1.42 21 26 1.23 21 17 0.80 21 17 0.80 80 1.079 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 breakdownattributed to the following factors: performance of the engine, and also due to thecoolant 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 towear and tear on the engine. From the Figure 4, the cause of the engine failure 481Failure type 2005 2006 2007 2008 AverageMechanical failure 63 87 29 0 44.75 Table II.Hydraulic failure 4 2 30 63 24.75 Percentage of differentTyre bust and allied failure 27 7 13 14 15.25 types of failure inElectrical 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 wheel18,4 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 of482 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 waterFigure 5. Pump leak leak through Radiator leakBSC 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 BMCAA3 AA3 Coolant oil excessive consumedAA1 AA1 Engine major overhaulingAA11 AA11 Engine not cranking will not startAA2 AA2 Engine over heatingAA4 AA4 Engine low oil pressureAA5 AA5 Engine oil excessive consumedAA6 Engine vibrationAA7 AA7 Engine knocking noiseAA8 AA8 Engine speed variationAA9 Improper colour of exhaustBB1 BB1 Fip, injector calibrationBB2 Engine knocking soundBB3 BB3 Engine cranks but did not startBB4 BB4 Engine hard to startBB5 Engine speed variationBB6 BB6 Engine vibrationBB7 BB7 Engine emits white smokeBB8 BB8 Lack of powerBB9 Excessive fuel consumptionCC1 CC1 Grinding noise when changing gearCC11 CC11 Gear engine problemCC2 CC2 Gear box noising while travellingCC4 Gear shift difficultiesCC6 CC6 Machine does not drive in any gearCC8 CC8 Excessive oil consumptionDD1 DD1 Clutch slipping when clutch applyDD10 DD10 Crown wheel noiseDD11 DD11 Differential oil lossDD12 DD12 Gear cannot engage 4Wd/2Wd/ reverseDD2 Clutch pedal hardDD3 Clutch judderingDD4 DD4 Noise while travellingDD5 DD5 Machine not achieve maximum speedDD8 Loss of oilDD9 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 BMCEE1 EE1 Brake is ineffectiveEE12 Air pressure dropEE16 EE16 Operating force of brake pedal is too lightEE17 EE17 Operating force of brake pedal is too heavyGG3 GG3 Un even tyre wearGG4 Tyre one side wear toe inGG5 Tyre one side wear toe outGG6 Wheel hubs lubrication leakageGG7 GG7 Speed too slowly (tyre air loss)GG8 Wheel excusive noise while brakeHH1 Excessive play in the steeringHH2 HH2 Hard steeringHH5 Steering vibrationHH8 HIIS Steering pump pressure reducedHH9 Steering wheel is sluggishII3 Tyre uneven wearJJ1 JJ1 Starting troubleJJ2 Engine not cranking will not startJJ3 JJ3 Engine cranking will not startJJ4 JJ4 Entire electrical function not workJJ5 Speed variation (electronic control sensors)JJ6 JJ6 Excessive noise develop (engine/genset)JJ7 Voltage fluctuation/droppedJJ8 JJ8 Wiring/circuit not functioningKK1 KK1 Exhaust system leakKK2 Air conditioning system failureLL1 LL1 Lack of power in all hydraulic functionsLL2 LL2 All hydraulic rams slow to operateLL4 LL4 Poor performance slow operating speedLL5 LL5 Ram creepLL6 LL6 Hydraulic oil loss – leaking section – sealsLL7 Electrical detent will not holdLL8 LL8 Mechanical detent will not hold
  • 14. Wheel loader 2005Code hrs per cp 80 120% Mobile crane 2005 Code hrs per cp 250 120% Plant breakdownDD4 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% 150CC8 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 485Wheel 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 AA5Wheel 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 2008Code hrs per cp Code hrs per cp 400 120% 400 120%DD11 330 45% 45% 100% AA4 350 39% 39% 300 350AA3 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% 250BB2 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 ABB3 1% 100% GG7 29 3% 92% 40% DJJ2 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 cranecould be that all the four considered equipments fall under heavy lifting/earthmoving/material handling system machines and are used to load/unload andtransport materials. The BSCs common to all machines are listed in Table VI. Failuredue to tyre puncture and allied problems contribute to maximum failure. A strategy planto minimize this type of failure is proposed. With the identification of these mostcontributing BMC’s and BSC’s, it is further proposed to develop an “overall breakdownmaintenance management”, where in dedicated and focused / targeted breakdownmaintenance teams are formulated. The size and the number of breakdown teams andother resources depend on the occurrence of BMC and BSC in the system. There need tobe an added approach of further enlightening the BMC to BSC into more details. Theresults of implementing further dedicated breakdown maintenance managementapproach will be presented in part II of this article.
  • 15. BIJ Backhoe loader 2005 Code hrs per cp Forklift 2005 Code hrs per cp18,4 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 GG8hoe 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 8Table IV. 3 Back hoe loader 16 8Pareto analysis 4 Fork lift 13 7on the BMCs Total BMC 78 38
  • 16. Related BSCs influencing the BMC (no of occurrences of the failure mode Plant breakdownBMC (no of failures) in bracket)Wheel loaderDD12(82) D26(82)DD4(63) D18(63)GG7(58) G1(11) G2(12) G3(16) G4(14) G5(4) G6(1) 487DD11(45) D22(45)EE1(35) E5(2) E7(1) E8(33)EE16(21) E2(21)AA3(21) A38(21)AA2(12) A43(12)ForkliftAA2(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 loaderAA8(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 craneAA1(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 BMCNote: Cumulative Percentage over four years 2005-2008 period and related BSC
  • 17. BIJ Identified No of failures Contribution on total18,4 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 24.88488 G4 46 reasons 17.06Table VI. G6 105 7.58BSCs common to all G1 72 19.67the four machines G3 32 5.69in the system L11 23 Hydraulic pump problems 5.45 References BMI (2009), United Arab Emirates Infrastructure Forecast Report Q3/2009. Canter, M.R. (1993), Resource Management for Construction, The Macmillan Press, London. ` Curcuru, G., Galante, G. and Lombardo, A. (2010), “A predictive maintenance policy with imperfect monitoring”, Reliability Engineering & System Safety, Vol. 95 No. 9, pp. 989-97. Gebraeel, N., Lawley, M., Li, R. and Ryan, J.K. (2005), “Residual-life distributions from component degradation signals: a Bayesian approach”, IIE Trans, Vol. 37 No. 6, pp. 543-57. Geert, W. and Liliane, P. (2002), “A framework for maintenance concept development”, International Journal of Production Economics, Vol. 77 No. 3, pp. 299-313. Henry, T.A. (1993), “The management of condition based maintenance”, International Journal of Plant Engineering, Vol. 37 No. 1, pp. 27-31. Hisham, B.J. (2003), “Plant Maintenance strategh – key for enhancing profitability” available at: www.maintenanceresources.com/referencelibrary/ezome/chemclean.htm John, M. (2002), “Maintenance management-a new paradigm, 1995”, available at: www.aladon.co. uk/11pp.html Laura, S. (2003), “Linking maintenance strategies to performance”, International Journal of Production Economics, Vol. 83 No. 1, pp. 45-64. Lu, C.J. and Meeker, W.Q. (1993), “Using degradation measures to estimate a time-to-failure distribution”, Technometrics, Vol. 35 No. 2, pp. 161-74. Meselhy, K.T., ElMaraghy, W.H. and ElMaraghy, H.A. (2010), “A periodicity metric for assessing maintenance strategies”, CIRP Journal of Manufacturing Science and Technology, Vol. 3, pp. 135-41. Mishra, R.C. and Pathak, K. (2002), Maintenance Engineering and Management, Prentice-Hall, New Delhi. Nakagawa, T. (1986), “Periodic and sequential preventive maintenance policies”, Journal of Applied Probability, Vol. 23 No. 2, pp. 536-42. Oloke, D. and Edwards, D.J. (2001), “A Data Capture Mechanism for Modeling Construction Plant Breakdown and Maintenance Costs”, Plant Management Research Group, University of Wolver Hampton, UK. Randy, R. and George, B. (1988), “Maintenance management concepts in construction equipment curricula”, Journal of Construction Education, Vol. 3 No. 2, pp. 102-17.
  • 18. Sawhney, R., Kannan, S. and Li, X. (2009), “Developing a value stream map to evaluate Plant breakdown breakdown maintenance operations”, International Journal of Industrial and Systems Engineering, Vol. 229, p. 240.Sheu, S.H., Griffith, W.S. and Nakagawa, T. (1995), “Extended optimal replacement model with random minimal repair costs”, European Journal of Operational Research, Vol. 85 No. 3, pp. 636-49.Susan, L., Yu-Chen, T. and Huitian, L. (2007), “Predictive condition-based maintenance for 489 continuously deteriorating systems”, Quality Reliabity Eng Int, Vol. 23, pp. 71-81.Watts, B. (1994), Business Finance, Mac Donald and Evans, London.Xiao-Gao, L. (1995), “A replacement model with overhauls and repairs”, Naval Research Logistics, Vol. 42 No. 7, pp. 1063-79.About the authorsP.B. Ahamed Mohideen is pursuing research in the field of breakdown maintenance managementto construction plant and equipment. He is a research scholar with Birla Institute of Technologyand 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 agood amount of knowledge of the construction equipment and their performance in theregion. 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 contributeda great amount of research work and support on energy management studies. He is associated withmany international journals. He has published many articles and papers in this field. Rajam Ramasamy Narasimmalu is a Faculty at CIT, India. His research interests arePush-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 theindustry. He has published many technical articles in his field of interest.To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints