RAM ANALYSIS OF SOME PROCESS INDUSTRIES: A · PDF fileRAM ANALYSIS OF SOME PROCESS INDUSTRIES:...

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Int. J. Mech. Eng. & Rob. Res. 2014 Pardeepkumar et al., 2014

RAM ANALYSIS OF SOME PROCESS INDUSTRIES:A CRITICAL LITERATURE REVIEW

Pardeepkumar1*, Rahul Kumar2, Parth Dahmani2 and Deepak Narula2

*Corresponding Author: Pardeepkumar,[email protected]

In today’s world of the automation, it is impossible to survive without reliable systems. Thus, allthe working engineering systems are expected to remain operative with the maximum efficiencyfor the maximum duration, i.e., reliable operation. The importance of reliable operation has beenrealized in large complex process industries such as chemical, sugar, textile, paper plants andfertilizer plants, etc. In these process plants to achieve the high availability and productivity, it isnecessary that all systems/subsystems remain in upstate for a longer duration of time. However,these systems/subsystems are subjected to random failures due to poor design, lack of operativeskills and wrong manufacturing techniques, etc., causing heavy production losses. These failedsystems can be brought back to their operating states after repair or replacement in minimumpossible down time. The plant working conditions and the repair strategies play an importantrole in maintaining the operating systems, operative for maximum duration, i.e., optimal systemavailability. This can be accomplished only through performance evaluation and analysis of allthe operating systems of the plant. The system performance can be quantified in terms of theavailability if the operating system is modelled mathematically and analysed in real workingconditions. It can be further optimized by means of some advanced optimization techniques.

Keywords: Modeling, Reliability, Steady state availability, Maintainability, Performanceevaluation

INTRODUCTIONWith the emerging demand of automation inthe various industrial segments, the highcapital investment is required for installing theproduction plants especially process plants likechemical, sugar, thermal, paper and fertilizer,

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Int. J. Mech. Eng. & Rob. Res. 2014

1 Department of Mechanical Engineering, N.I.T. Kurukshetra, Haryana, India.2 Department of Mechanical Engineering, ACE Ambala, Haryana, India

etc. It is essential to have high productivity andmaximum profit from process plants for theirsurvival. To achieve this end, availability andreliability of equipment in process must bemaintained at the highest order. Unfortunately,this is not the case because failure is inevitable

Review Article

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even though it can be minimized by propermaintenance, inspection, proper training to theoperators, motivation and by inculcatingpositive attitude in the workmen. Theperformance of any system also affects itsdesign quality and the optimization tools used.Thus the performance of a system may beenhanced by proper design, optimization atthe design stage and by maintaining the sameduring its service life. Proper maintenanceplanning plays a prominent role in reducingproduction costs. Increasing availability ofmanufacturing systems and improving thequality also help a lot in the productivityenhancement.

In process industries too, such as Chemical,Paper, Sugar, Brewery, Cement, Foodprocessing, Thermal power plants andFertilizer plants to achieve the goal of highsystem availability and to utilize maximum plantcapacity, it is necessary that its varioussubsystems should remain in upstate for alonger period. However, these systems orsubsystems are subjected to random failures.Depending upon the nature and extent offailure, it may result into reduced or zeroproduction. No doubt that failed systems canbe brought back to their operating state afterrepairs or replacements of some of thecomponents. But, in this case the factoryoperating conditions and also the repairpolicies adopted in the organization play animportant role in maintaining the systemoperative with full capacity for maximumduration. Designing such a process, from theview point of high system availability, wouldnecessitate a prior knowledge of the systembehavior, available repair facilities, etc.Analysis and modeling of such systems may

prove beneficial in evaluating the performanceof subsystems and the degree of interactionbetween the constituent subsystems. Thiswould also yield a framework for system designbased on availability and optimum repairpolicy.

Reliability, Availability andMaintainability ConceptsReliability: It is concerned with the probabilityand frequency of failures (or more correctly,the lack of failures). A commonly used measureof reliability forrepairable systems is the MeanTime Between Failures (MTBF). Theequivalent measure for non repairable itemsis Mean Time To Failure (MTTF). Reliability ismore accurately expressed as a probability ofsuccess over a given duration of time. Thereliability of a component for a period t iscalculated as: R(t) = e–at Where, a is the meanfailure rate. t is Burn in period Useful life periodWear out period Time.

Maintainability: It is defined as the measureof the ability of an item to be restored orretained in a specified condition. Simply stated,maintainability is a measure of how effectivelyand economically failures can be preventedthrough preventive maintenance and howquickly system operation following a failure canbe restored through corrective maintenance.Commonly used measures of maintainabilityin terms of corrective maintenance are theMean Time To Repair (MTTR) and a limit forthe maximum repair time. Maintainability is adesign parameter, while maintenance consistsof actions to repair or prevent a failure event.

Availability: In an industrial system, high plantavailability plays an important role in thedirection of industrial growth as the profit is

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directly related to the production volume whichdepends upon plant performance. To achievehigh system availability, proper maintenancemanagement system supported by adequateresources such as manpower, spares andmachines, etc. are required. Mathematically,the term availability is used to indicate theprobability of a system or equipment being inoperating condition at any time t. Theavailability of a system is a combined measureof both reliability and maintainability.

RAM ANALYSIS: A CRITICALREVIEWHistorical Background: Reliabil ityengineering has been in existence as adistinct discipline for nearly the past fivedecades. Even now, one finds that the subjectof repairable systems has not gained its dueimportance in reliability literature. A study ofthe development of this engineering disciplinedoes helps to identify the reasons for thisdeficiency and also understand the difficultiesinvolved in analysis of repairable systems asfar as their reliability, availability andmaintainability are concerned. Developmentof reliability has strong links with qualitycontrol and its development. In early 1920s,statistical methods were developed at BellTelephone Laboratories, USA, to solve theirquality control problems which provided thema basis for further developments in the areaof statistical quality control. Subsequently,quality control techniques were popularizedby American Standard Association andAmerican Society for Mechanical Engineersalong with Bell Laboratories. Themathematical theory of reliability took shapeand grew as a result of the demands ofdeveloping technology and in particular out

of the experiments with complex militarysystems during World War II (1939).

Ramin Process IndustriesTerje Aven (1990) presented some simpleapproximation formulae for the availability ofstandby redundant systems comprising similarunits that were preventively maintained. Theformulae were established using standardMarkov theory. A number of simulations hadbeen performed in order to evaluate theformulae. The simulations showed that theformulae gave very good approximations forvarious preventive maintenance regimes.

Hassett (1995) combined time varyingfailure rates and Markov chain analysis toobtain hybrid reliability and availabilityanalysis. However, combining thesetechniques can, depending on the size of thesystem, result in solutions of the Markov chaindifferential matrix equations that areintractable. The authors have identifiedsolutions that are tractable. Tractable solutionswere found for the l-component 2-state and the2-component 4-state configurations. Timevarying failure rates were characterized by ageneral polynomial expression. The generalpolynomial failure rate provides flexibility inmodeling the time varying failure rates thatoccur in practice.

Kumar (1997) carried out stochasticanalysis of the power generation and coalhandling systems in a thermal power plant withMarkov chains. They studied the performanceof the system in three states viz. good, reducedand failed. Taking constant failure and repairrates for each working unit, the mathematicalformulation was done using the Birth-Deathprocess. Expressions for steady state

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availability and the Mean Time BetweenFailures (MTBF) were derived. The graphswere given, depicting the effect offailure andrepair rates on the system availability.

Michelsen (1998) assessed the reliabilityanalysis as a useful tool for risk analysis andthe design of safety system. He proposed toencourage the use of reliability technology: i.e.,to better cooperation with industry. He focusedon specific problems as experienced by theindustry rather than system modeling;development of simple methods to be appliedby the people who own the problem. He usedRAM analysis means reliability-availability-maintainability for screening purposes.

Elegbede (2003) proposed a GA-basedapproach to solve a multi-objectiveoptimization problem and aimed at maximizingthe availability and minimizing cost ofrepairable parallel-series systems. In the firststep, they used the weighting technique totransform the problem into a problem of single-objective optimization. Next they relaxed theconstraints by mean of the technique of exactpenalty to reformulate the problem and to finda solution in the second step, they developedthe GA for which they have tried to provideguidance to adjust the parameters and controlthe fitness. For that purpose, they designedan experiment plan that shows its relevancefor the GA implementation.

Marzio Marseguerra (2004) proposed amulti-objective optimization approach, basedon genetic algorithms, which transparently andexplicitly account for the uncertainties in theparameters. The objectives considered (theinverse of the—expected system failureprobability and the inverse of its variance),

were such as to drive the genetic searchtoward solutions which were guaranteed togive optimal performance with high assurance.For validation purposes, a simple case studyregarding the optimization of the layout of apipeline was firstly presented. The procedurewas then applied to a more complex systemtaken from literature, the Residual HeatRemoval safety system of a Boiling WaterReactor, for determining the optimal STI of thesystem components. The approach providedthe decision maker with a useful tool fordetermining those solutions which, besidesbeing optimal with respect to the—expectedsafety behavior, allow a high degree ofassurance in the actual system performance.

Trivedi (2005) presented the application ofMarkov Decision Process (MDP) algorithm insearching for the optimal maintenance policyfor condition based maintenance, and theyhave also presented a joint optimization ofinspection rate and its correspondingmaintenance policy. Under a special casewhen the optimization objective is steady-stateavailability and the deterioration rate at eachfailure stage is the same, they found that theoptimal policy is a threshold-type maintenancepolicy.

Rajpal (2006) observed that there are manyfactors that affect the Reliability, Availability andMaintainability (RAM) of a complex repairablesystem. These factors include machinery (type,number of machines, age, arrangement ofmachines relative to each other, arrangementof components in the machine, inherent defectsin components), operating conditions (level ofskill and number of operating personnel,working habits, inter personnel relationships,absenteeism, safety measures, environmental

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conditions, severity of task assigned, shockloading), maintenance conditions(competence and strength personnel,attendance, working habits, safety measures,inter-personnel relationships, defectsintroduced by previous maintenance actions,effectiveness of maintenance planning andcontrol) and infra structural facilities.

Armen Kiureghian (2007) Closed-formexpressions were derived for the steady-stateavailability, mean rate of failure, mean durationof downtime and lower bound reliability of ageneral system with randomly andindependently failing repairable components.Component failures were assumed to behomogeneous Poisson events in time andrepair durations were assumed to beexponentially distributed. The results wereexpressed in terms of the mean rates of failureand mean durations of repair of the individualcomponents. These expressions provide aconvenient framework for identifying importantcomponents within the system and fordecision-making aimed at upgrading thesystem availability or reliability, or reducing themean duration of system downtime.

Faisal Khan (2008) presented a risk-basedmethodology to estimate optimal inspectionand maintenance intervals which maximize asystem’s availability. The methodology wascomprised of two steps: availability modelingand risk-based inspection and maintenancecalculations. The proposed methodology wasapplied to the steam generating system of anoil fired thermal power plant. The authorspresented a case study. The case studyinvolved the application of the method to asteam generating unit in a power generatingplant. The unit under consideration was further

subdivided into ten subsystems. Thesesubsystems are simulated using the proposedapproach to achieve a target availability of99.9%.

Ying Shen Juang (2008) proposed agenetic algorithm based optimization modelto improve the design efficiency. The objectivewas to determine the most economical policyof components’ Mean-Time-Between-Failure(MTBF) and Mean Time-To-Repair (MTTR). Healso developed a knowledge-basedinteractive decision support system to assistthe designers set up and to store componentparameters during the intact design processof repairable series-parallel system. Heutilized object-oriented program technique todevelop a knowledge system for theavailability design of series-parallel systems,which enabled the users to retrieve, modify andfine-tune similar designs from the systemdatabase.

Sharma (2008) presented the applicationof RAM analysis in a processindustry.Markovian approach is used tomodel the system behavior. For carrying outanalysis, transition diagrams for varioussubsystems were drawn and differentialequations associated with them wereformulated. After obtaining the steady statesolution the corresponding values ofreliability and maintainability were estimatedat different mission times. The computedresults were presented to plant personnel fortheir active consideration. The resultsproved helpful to them for analyzing thesystem behavior and thereby to improve thesystem performance considerably byadopting and pract icing suitablemaintenance policies/strategies.

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Sanjeev Kumar (2009) discussed theperformance evaluation and availabilityanalysis of ammonia synthesis unit of afertilizer plant. The fertilizer plant is a complexand repairable engineering system comprisesof various units viz. shell gasification andcarbon recovery, desulphurization, co-shiftconversion, decarbonation, nitrogen wash andammonia synthesis, etc. One of the mostimportant functionaries of a fertilizer plant isammonia synthesis unit. This unit consists offive subunits arranged in series and parallelconfigurations. For the evaluation ofperformance and analysis of availability, aperformance evaluating model had beendeveloped with the help of mathematicalformulation based on Markov Birth-Deathprocess using probabilistic approach. Thefindings of this paper are therefore, consideredto be useful for the analysis of availability anddetermination of the best possiblemaintenance strategies in a fertilizer plantconcerned.

Gupta (2009) assessed the reliability andavailability of a critical ash handling unit of asteam thermal power plant by making aperformance analysis and modeling, usingprobability theory and the Markov Birth-Deathprocess. After that, steady state probabilitieswere determined. Certain decision matriceswere developed, which provide variousavailability levels. The behavior analysis of thereliability module revealed that the availabilitydecreases with increasing failure rates, whileoperational availability improves with initialincreases in repair rates for differentsubsystems.

Khanduja (2010) dealt with themathematical modeling and performance

optimization for the paper making system in apaper plant using genetic algorithm. The papermaking system had been divided into fourmain subsystems, arranged in series andparallel. The mathematical formulation of theproblem was done using probabilisticapproach and differential equations aredeveloped based on Markov birth-deathprocess. These equations were then solvedusing normalizing conditions to determine thesteady state availability of the paper makingsystem.

Carazas (2011) presented a method forreliability and availability evaluation of HeatRecovery Steam Generator (HRSG) installedin combined cycle gas and steam turbinepower plant. The method’s first step consistedin the elaboration of the steam generatorfunctional tree and development of failuremode and effects analysis. The next stepinvolved a reliability and availability analysisbased on the time to failure and time to repairdata recorded during the steam generatoroperation. The third step, aiming at availabilityimprovement, recommended the fault-treeanalysis development to identify componentsthe failure (or combination of failures) of whichcan cause the HRSG shutdown. The availabilityand reliability of the HRSGs presented in thestudy reflect on-site behavior, including theeffects of changes in feed water systemsmaintenance policy.

Kumar (2011) discussed the mathematicalmodeling and performance optimization ofCO2 cooling system of a fertilizer plant usinggenetic algorithm. Differential equations hadbeen derived based on Markov Birth-Deathprocess using probabilistic approach. Theseequations were then solved using normalizing

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conditions to determine the steady stateavailability of the CO2 cooling system.

Sinha (2011) stated that Brake-by-wire isone such system that interfaces with activesafety features built into an automobile, andwhich in turn is expected to provide fail-operational capabilities. In this paper, buildingup on the basic concepts of fail-silent and fail-operational systems design, the authorproposes a system-architecture for a brake-by-wire system with fail-operational capabilities.

Kajal (2012) dealt with the performanceoptimization for skim milk powder unit of a dairyplant at National Dairy Research Institute(NDRI), Karnal using Genetic Algorithm (GA).The failure and repair rates of the subsystemswere taken from maintenance history sheets,which followed the exponential distribution. Themathematical formulation was carried outusing probabilistic approach and the Markovbirth – death process was used to develop thedifference differential equations. The steadystate availability expression had been derivedusing normalizing conditions. The optimalvalues of failure/repair rates of each subsystemof the skim milk powder unit had beenevaluated using Matlab 7.1 GA tool. Thesteady state availability obtained from Markovanalysis was also compared with the optimalavailability calculated through GA tool. So, thefindings of the paper will be highly useful to theplant management for developing propermaintenance strategies which can beimplemented in order to enhance systemperformance.

RESULTSIt has been found that a lot of work has beendone in the field of Reliability Engineering

particularly in Thermal Power Plant, PaperIndustry, Fertilizer Industry, Sugar Industry,Nuclear Power Plant and Oil Refinery etc.Some research work has also been carriedout on availability analysis of variousprocesses industries. But very limited workhas been done in the field of reliability andavailability analysis with variable failure andrepair rates. A number of studies have beencarried out to analyze the availability andreliability using different techniques. Some ofthe techniques used are:

• Failure Mode Effect and Analysis

• Markov Models

• Reliability Block Diagram method

• Multi-objective Optimization Approachbased on Genetic Algorithm

• Fault Tree Analysis

• Petri Nets

• Artificial Neural Networks

• Extended Block Diagram method

• Monte Carlo Simulation Technique, etc.

But, still, there is great scope of futureresearch in this field as limited studiesregarding development of Decision SupportSystem have been carried out in processindustries. The identified gaps in the literatureprovide an opportunity of research in the fieldof reliability. There is vast scope of researchin this area due to fact that it encompasses awide range of activities in the followingresearch areas:

• Reliability and Availability Modeling

• Maintenance Cost Optimization

• Maintenance Policy Selection

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FUTURE SCOPEWe intend to further analyse the variousmethods and models so far proposed. So thatwe could suggest improvements in the existingmodels and also in the various methodologiesused to assess reliability.

CONCLUSIONBy going through the literature survey we havefocused on the various views given bynumerous researchers on reliabilityengineering. Analysis of the various factors onwhich the reliability of systems depends hasbeen done. Some research work has alsobeen carried out on availability analysis ofvarious processes industries. But very limitedwork has been done in the field of reliabilityand availability analysis with variable failureand repair rates. The major finding of the studyis that the models under review reflect eitherinfinite or finite number of failures. Allexponential distribution based models reflectfinite failures and logarithmic distributionbased model reflect infinite failures.

REFERENCES1. Armen Der Kiureghian, Ove D Ditlevsen

and Junho Song (2007), “Availability,Reliability and Downtime of Systems withRepairable Components”, ReliabilityEngineering and System Safety, Vol. 92,pp. 231-242.

2. Carazas F J G, Salazar C H and Souza GF M (2011), “Availability Analysis of HeatRecovery Steam Generators Used inThermal Power Plants”, Journal ofEnergy, Vol. 36, pp. 3855-3870.

3. Chen D and Trivedi K S (2005),“Optimization for Condition-Based

Maintenance with Semi-Markov DecisionProcess”, Reliability Engineering andSystem Safety, Vol. 90, pp. 25-29.

4. Elegbede C and Adjallah K (2003),“Availability Allocation to RepairableSystems with Genetic Algorithms: A Multi-Objective Formulation”, ReliabilityEngineering and System Safety, Vol. 82,pp. 319-330.

5. Faisal I Khan, Mahmoud Haddara andLoganathan Krishnasamy (2008), “A NewMethodology for Risk-Based AvailabilityAnalysis”, IEEE Transactions onReliability, Vol. 57, pp. 103-112.

6. Gupta S, Tewari P C and Sharma A K(2009), “Simulated Model andPerformance Evaluation of Ash HandlingSystem of A Thermal Power Plant”, UdyogPragati, Vol. 33, No. 4.

7. Hassett T F, Dietrich D L andSzidarovszky F (1995), “Time-VaryingFailure Rates in the Availability &Reliability Analysis of RepairableSystems”, IEEE Transaction onReliability, Vol. 44, No. 1.

8. Kajal S and Tewari P C (2012),“Performance Optimization for Skim MilkPowder Unit of a Dairy Plant UsingGenetic Algorithm”, International Journalof Engineering, Vol. 25, pp. 211-222.

9. Khanduja R, Tewari P C, Chauhan R Sand Kumar D (2010), “MathematicalModeling and Performance Optimizationfor the Paper Making System of a PaperPlant”, Jordan Journal of Mechanical andIndustrial Engineering, Vol. 4, No. 4,pp. 487-494.

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10. Kumar S and Tewari P C (2011),“Mathematical Modeling andPerformance Optimization of CO 2Cooling System of a Fertilizer Plant”,International Journal of IndustrialEngineering.

11. Marzio Marseguerra, Enrico Zio and LucaPodofillini (2004), “Optimal Reliability/Availability of Uncertain Systems via Multi-Objective Genetic Algorithms”, IEEETransactions on Reliability, Vol. 53.

12. Michelsen Q (1998), “Use of ReliabilityTechnology in the Process Industry”,Reliability Engineering and SystemSafety, Vol. 60, pp. 179-181.

13. Navneet Arora and Dinesh Kumar (1997),“Availability Analysis of Steam and PowerGeneration Systems in the Thermal PowerPlant”, Microelectron. Reliob., Vol. 31, pp.795-799.

14. Rajpal P S, Shishodia K S and Sekhon GS (2006), “An Artificial Neural Network forModeling Reliability, Availability andMaintainability of a Repairable System”,Reliability Engineering and SystemSafety, Vol. 91, pp. 809-819.

15. Rajiv Kumar Sharma and Sunand Kumar(2008), “Performance Modeling in Critical

Engineering Systems Using RAMAnalysis”, Reliability Engineering andSystem Safety, Vol. 93, pp. 891-897.

16. Sanjeev Kumar, Tewari P C and SunandKumar (2009), “Performance Evaluationand Availability Analysis of AmmoniaSynthesis Unit in a Fertilizer Plant”,Journal of Industrial EngineeringInternational, Vol. 5, pp. 17-26.

17. Sinha P (2011), “Architectural Designand Reliability Analysis of a Fail-Operational Brake-by-Wire System fromISO 26262 Perspectives”, ReliabilityEngineering and System Safety ,Vol. 96, pp. 1349-1359.

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19. Ying-Shen Juang, Shui-Shun Lin andHsing-Pei Kao (2008), “A KnowledgeManagement System for Series-ParallelAvailability Optimization and Design”,Expert Systems with Applications ,Vol. 34, pp. 181-193.

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