Australian Journal of Basic and Applied Sciences

Transcription

1 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: AENSI Journals Australian Journal of Basic and Applied Sciences ISSN: Journal home page: wwwajbaswebcom Reliability and Maintainability Assessment of the Steam Turbine Instrumentation System for optimization Operational Availability System at Fertilizer Plant Ali Musyafa, 2 Ronny D Noriyati, 3 Silvana R Dacosta, 4 S Komayadi,2,3,4 Department of Engineering Physics, Faculty of Industrial Technology, Sepuluh Nopember Institute Of Technology, Surabaya, INDONESIA A R T I C L E I N F O Article history: Received 25 June 24 Received in revised form 8 July 24 Accepted 25 July 24 Available online 2 August 24 Keywords: Steam turbine, Failure Mode and Effects Analysis, Reliability, Predictive maintenance, Risk Priority Number A B S T R A C T Predictive maintenance based on condition aimed to evaluate the condition of components with periodic monitoring and sustainable The purpose of predictive maintenance is to perform maintenance "just in time" before the component is damaged when running the function Reliability evaluation method based on the Failure Mode and Effect Analysis (FMEA) for the determination of the components of the instrumentation system at the steam turbine which has a Risk Priority Number (RPN) The component that has the highest RPN is speed transmitter and control valve with a value of 6 Practice to evaluate the reliability of the steam turbine instrumentation system, the obtained value of R ( at t = 7 days is,833 When an evaluation of the Predictive maintenance (PM) and PM Breakdown maintenance at intervals of every 5 days with a value of R m ( over 8 can be calculated that the maintenance costs of US $ 5,252, AENSI Publisher All rights reserved To Cite This Article: Ali Musyafa, Ronny D Noriyati, Silvana R Dacosta, S Komayadi, Reliability and Maintainability Assessment of the Steam Turbine Instrumentation System for optimization Operational Availability System at Fertilizer Plant Aust J Basic & Appl Sci, 8(3): 32-39, 24 INTRODUCTION Predictive maintenance in the industry is an important activity to maintain the operational capability of the plant in order to work as expected, without any interruption of the damage that was never predicted Needs planned maintenance process consists of detection, identification and anticipation of the plant to be identified early before experiencing failure Prevention against damage can be done, so it does not significantly affect plant operations (Robin, E, et al, 996) Plant reliability is assessed steam turbine instrumentation systems Phase studies include measurement and calculation factors that affect the reliability of the system Failure data between time instrumentation used for predictive maintenance The data required is the mean time between failure (MTTF) of the recording process of care during the shutdown, turn around plant and mean time between failure (MTBF) turbines during operation Then performed a qualitative analysis through the method of effect Analysis FMEA Failure Mode (Ebeling, Charles E, 997) Furthermore, determining the right time to perform maintenance based on qualitative and quantitative analysis The purpose of the study is to determine the appropriate maintenance time for the implementation of predictive maintenance system in the steam turbine unit instrumentation ammonia fertilizer plant based on qualitative and quantitative analysis With the implementation of the PM on the system, the obtained operational cost savings and system reliability can be improved Predictive Maintenance or Condition Based Maintenance Preventive Maintenance is included in combination with Time Based Maintenance According to Time Base Maintenance and overhaul done at any given time interval without seeing the condition of the equipment, whether the equipment is still in good condition or not With the implementation of Predictive Maintenance, the short remaining time can be extended Implementation of PM in the complex system that can increase reliability, obtained through the implementation of programs that a comprehensive PM The application program can also reduce the effects of PM usage / replacement of components that go beyond the limits of life, which in turn has a major influence on the continuity of operation of the system life cycle Maintenance activities aimed at maintaining or restoring the Corresponding Author: Ali Musyafa, Department of Engineering Physics, Faculty of Industrial Technology, Sepuluh Nopember Institute Of Technology, Phone: / musyafa@epitsacid, Kampus ITS Keputih, Sukolilo, Surabaya Indonesia, 6

2 Reliability 33 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: function of the equipment in order to function as planned PM is a maintenance strategy that can help determine the operating condition of equipment, so the right time to perform maintenance can be predicted MATERIAL AND METHODS Equipment condition based maintenance, is an attempt to evaluate the condition of the equipment is done by continuous monitoring Purpose PM performs timely maintenance before the equipment failed The activity was in contrast to the time-based maintenance, where equipment gets maintenance treatment, although it does require Time-based treatments are not effective, especially in the identification of issues and financing If R ( is the reliability function without preventive maintenance, T is the time interval preventive maintenance and Rm ( System reliability with PM is; t RT Rt nt untukt t T R m 2 () R (T) is the probability that the first resistance preventive maintenance and R (t-nt) the probability of resistance during the term t-nt is determined from the initial conditions; R m n t T ) t nt) with nt t ( n ) T (2) As a benchmark for further study on Predictive Maintenance can be used graphics system reliability Predictive Maintenance experience is shown in Figure PM, t n) CumulativePM, Rm ( NoPM, Fig : Reliability system with periodic preventive maintenance (Robin, E, et al, 996) Failure Cause that is used in the preparation of the FMEA is to identify the Root Cause of each failure mode Failure cause is the cause of the failure mode Failure Effect is a further step in the preparation of FMEA is the determination of the effects on the failure mode Failure effects are grouped into three namely; component level, system level and plant level RPN is the final step in the preparation of the FMEA for the determination of severity, occurrence and detection (SOD) SOD is used to determine to the NDP and failure mode analysis used Reliability depends on the hazard rate functions [ (] and, Pr{ T t} (3) Where R (, R () =, and For a value of t, R ( is the probability that the time to failure is greater than or equal to t If it can be determined: F( Pr{ T t} where : F() =, dan lim F( So that F ( is the probability of occurrence of a failure before time t By looking at the R ( as a function of reliability and F ( as the cumulative distribution function (CDF) of the distribution of the failure, then the third function can be determined by: df ( d f ( dt dt t (5) Called the probability density function (PDF) This function describes the shape of the distribution of failure In the PDF, f ( has two characters: (4)

3 34 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: f ( And By using PDF, f ( then, f ( dt (6) t ' F( f ( t ' ) dt (7) And t ' f ( t ' ) dt (8) Reliability function and CDF both describe a large area under the curve f ( And if the area under the curve is equal to, then the reliability and failure probability is determined by: R (, F ( Functions R ( is used when calculating the reliability and the function F ( is used when calculating the probability of failure The rate of failure, in addition to the probability function, other functions called by the failure rate or hazard rate function is always used for reliability This function provides instant results of failure (at time Pr{ t T t t} t (9) The probability of failure in the time interval from t to t + Δt where the system can survive at time t is t Pr{ t T t t} () where t () Is the conditional probability of failure per unit time (failure rate), the use of: [ t ] d f ( ( lim (2) t t dt Where λ ( is the instantaneous hazard rate or failure rate function Failure rate function λ ( provides an alternative in the determination of the distribution of failure For some cases the failure rate can have three characteristics, namely IFR, DFR, and depending on the circumstances CFR λ ( Hazard rate can determine the particular unique reliability function as follows: d ( dt (3) t ' exp ( t ' ) dt (4) Continuous distribution consists of an exponential distribution, normal, we bull, and lognormal We bull distribution has been widely used in engineering reliability as a model of survival in electrical components and systems reliability engineering (Robin, E, et al, 996) We bull distribution is used for the two conditions, namely failure rate Decreasing Failure Rate (DFR) and the increasing failure rate (IFR) and solid opportunities Functions (probability density functions, PDF) waybill distribution mathematically written: t t f ( exp Where: = scale parameter (scale parameter), = shape parameter (shape parameter), = parameter locations (locations parameters) (5)

4 35 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: In a series arrangement of equipment, every component must function so that the system can work Being in parallel and redundant at least there should be one component that functions that the system is working Pareto analysis is used to determine priorities of components that need more attention The next set of components that have the highest RPN for breakdown analysis and predictive maintenance Steam turbines can be divided into 5 which include: Utilities to limit the function of distributing power and process steam generating ammonia Front-end processing functions to limit natural gas into gas shift % of design capacity in accordance with the composition of H2, N2, CH4 and CO2 with limits (max) = :44% and CO (max) = :23% in temperature and pressure Middle end with restrictions function purified gas shift of CO and CO2 content in accordance with a design capacity of % maximum operating limit (CO + CO2) = ppm at a certain temperature and pressure Back-end processing functions with restrictions anhydrous ammonia synthesis gas into liquid % of design capacity in accordance with the composition, temperature, and pressure NH3 storage function storing and distributing liquid NH3 at a certain pressure with 8% of the level of the tank Serves as a steam turbine driving the compressor refrigerant and a critical component in the ammonia plant The steam turbine instrument system include: a hand controller indicator, speed transmitter, speed indicator, hydraulic speed governor, pressure indicator, pressure transmitter, high vibration transmitter, vibration probes, and control valve Determination of the Risk Priority Number (RPN), Based on Failure Mode and Effect Analysis (FMEA), it can be determined the value of the Risk Priority Number (RPN) for instrumentation systems GT-2 steam turbine comprising Indicator Hand Controller, Transmitter Speed, Speed Indicator, Hydraulic Speed Governor, pressure transmitters, pressure indicators, vibration probes, high vibration transmitter, and Control Valve Pareto Analysis, Based on the data RPN steam turbine instrumentation system components during operation can be determined NDP components of its most high Minitab software 4 Evaluation steam turbine reliability using FMEA method in speed control systems, instrument bearing, and control valve steam turbine Determination of Time between Failures through data improvement Steam Turbine 2 GT card G/GT-2 taken from history for ten years Distribution Test, Based on history data to test data distribution time between failures of software components with waybill reliasoft The following steps are built in the distribution of the test as follows: First step: determine the type of data that will be tested distribution A few choice in entering data; Individual of data, grouped data, the data and grouped Individual suspension data The second step is to enter the data that will be tested distribution and determine the type of data that will be used (failed or suspension) For suspension of data in the first step an individual should choose the data grouped suspension or suspension The third step is starting to test the distribution with distribution wizard and select the option when the window appears as above followed by selecting the option next step Step three can be seen in the picture below that shows the distribution of test results the first column is AVGOF test parameters (average goodness of fi where the greater value in this column indicates the mismatch distribution test results The second column is the test parameters AVPLOT (average of plots fi that indicates the size of the value used for plotting the distribution of test results The third column is the value of the parameter LKV test (likelihood function) and the fourth column is the sizzle (secession array) is the value of the test result distribution decisions Step four can be seen as shown below each column has given rank for each distribution The first column shows the GOF ranking, the second column shows the plot of the ranking, the third column shows the likelihood ranking and the fourth column shows the weighted average value of the rank specified for each distribution From the fourth column, the smallest value is the best value for the distribution of test results The fifth step is the last step at the same time deciding which distribution will be selected by the software which will be obtained in accordance with the distribution parameters have been determined Testing this distribution includes 2 parameter Weibull distribution, Weibull distribution parameters 3, parameter exponential distribution, distribution exponensial 2 parameters, normal distribution and lognormal distribution Of the six tested distribution is the distribution that has the lowest value of the distribution is chosen for further analysis Estimation of the distribution is determined by three parameters of the test; Average Goodness of fit (AvGOF), Average of plot fit (AvPlo and Likelihood Function Value Of the three parameters theoretically lowest value is the best value for the inter-failure time data on purpose Improved Reliability and determination of appropriate maintenance schedule on the steam turbine is done with predictive maintenance (PM) Reliability value can be determined by AM turbines on the time interval between PM (T) The time interval used to

5 Count Percent 36 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: determine the appropriate maintenance schedule PM time interval turbine speed control 5 starts every day with a maintenance schedule T5, T3, T45, T6, T75, and T9 RESULTS AND DISCUSSION Based on SBS diagram of ammonia, the steam turbine GT-2 is one of the critical plant equipment If the turbine failures experienced unplanned shutdown the plant, therefore, the reliability analysis is done to anticipate the exact time maintenance Relative risk of failure and consequently determined by three factors, namely: The seriousness or severity (severity): Consequences of failure that should happen to him Genesis (Occurrence): Possible or frequency of occurrence of a failure Detection (Detection): The possibility of failure is detected before the effect of the result occurred Each failure mode and effect of potential given the value for each of these three factors, starting -5 scale, low to high Determination of the Risk Priority Number is based on the multiplication value (rating) of these three factors, namely (severity x occurrence x detection) for each failure mode and effect potential Highest Risk Priority Number Mode should receive attention first, special attention is given to major severity rating and catastrophic (4 and 5), regardless of rating Risk Priority Number Steps taken for determination of FMEA include; The review process, discussion of potential failure modes, potential make a list of failure modes, determining the values of the severity of each consequence, Determining the occurrence of each failure mode, each as a result of the failure detection Determining, Calculating the Risk Priority Number to each failure mode, prioritize failure modes that need to be receive corrective action, acts to eliminate high risk failure modes and calculate the RPN Pareto analysis can be determined by the component that has the highest priority Based on the analysis Pareto chart the component that gets top priority is the component with the greatest percentage of the control valve and speed that the transmitter Pareto Chart of Komponen Komponen Control valve Speed transmitter Hydraulic speed governor High vibration transmitter Vibration probe Hand indicator controller Speed indicator Other Count Percent 27,2 27,2 9, 6,8 6,8 5,4 4,8 2,7 Cum % 27,2 54,4 73,5 8,3 87, 92,5 97,3, Fig 2: Pareto chart components Reliability Evaluation of Steam Turbine, Based on the history card rotating equipment kaltim- then the maintenance history data obtained GT-2 steam turbine as a whole By performing quantitative analysis on the history data are obtained distribution with parameter β = 9989; γ = -8949; and η = and steam turbine reliability function is as follows:,9989 t 8,949 R ( exp 6,5258 Evaluation of data distribution between failures speed control on the data obtained from the history with the history card data between the failures to control speed control includes hand indicating, high-speed switches and hydraulic speed governor Testing the distribution of time between failures speed control using the program package, in order to obtain the distribution of inter-failure time data corresponding to the speed control 3 weibull distribution with parameter β = 694; γ = ; and η = Solid Opportunity Evaluation Function (PDF) Speed Control, under 2:2 equations with parameters that have been obtained as follows β = 694; γ = ; and η = so we can determine the function of solid opportunities (PDF) for the speed control is as follows:,694, 694 t 36,4375 t 36,4375, ,5338 f ( exp 642, ,5338 Reliability Evaluation Function Speed Control, Based on the distribution of time between failures speed control 3 then follow weibull distribution with parameters that have been obtained are β = 694; γ = ; and η = reliability function can be defined by equation 2:2 as follows:

6 37 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: 32-39,694 t 36,4375 R ( exp 642,5338 Failure Rate Function Evaluation (Failure rate) Speed Control, Based on the distribution of time between failures speed control 3 then follow weibull distribution with parameters that have been obtained are β = 694; γ = ; and η = Failure Rate (Failure Rate) can be defined by equation 2:22 as follows:,694,694 t 36,4375 ( 642, ,5338 Evaluation of Data Distribution Between Failure Control Valve, Based on the data between component failure over the testing distribution of time between failures for the control valve using software rely soft weibull + + version 4 and found that the data distribution time between failure for the control valve is best suited to distribution weibull 2 with parameter β = 5822 and η = Solid Opportunity Evaluation Function (PDF) Control Valve, under 2:2 equations with parameters that have been obtained are β = 5822 and η = solid so we can determine the function of opportunity (PDF) for the control valve is as follows: 5,822 5, 822 5,822 t t f ( exp 32, , ,3683 Evaluation of Reliability Control Valve Function, Based on the distribution of time between failures control valve which follows the Weibull distribution with parameters 2 has been obtained that β = 5822 and η = reliability function can be defined by equation 2:2 as follows: 5,822 t R ( exp 32,3683 Failure Rate Function Evaluation (Failure Rate) Control Valve, Based on the distribution of time between failures control valve 2 which follows the weibull distribution with parameters that have been obtained are β = 5822 and η = Failure Rate (Failure Rate) can be formulated by 2:22 For the following equation: 5,822 5,822 t ( 32, ,3683 Evaluation of Data Distribution between Failure Instrument Bearings of history data on the history card then obtained data between failure for instrument bearing that includes vibration probes and high vibration transmitter are as follows: Based on the data between component failure over the testing distribution of time between failures for instrument bearing using software rely soft weibull + + version 4 and found that the data distribution of time between failures for the most appropriate instrument bearings with 3 weibull distribution with parameter β = 224; η = ; and γ = Solid Opportunity Evaluation Function (PDF) Instrument Bearings By 2:2 equations with parameters that have been obtained are β = 224; η = ; and γ = so we can determine the function of solid opportunities (PDF) for bearing instruments is as follows: 2,24 2, 24 t 89,499 t 89,499 2,24 879,8497 f ( exp 879, ,8497 Reliability Evaluation Function Instrument Bearings, Based on the distribution of time between failures instrument bearings 3 then follow weibull distribution with parameters that have been obtained are β = 224; η = ; and γ = Reliability function can be defined by equation 2:2 as follows: 2,24 t 89,499 R ( exp 879,8497 Failure Rate Function Evaluation (Failure Rate) Instrument Bearings, Based on the distribution of time between failures instrument bearings 3 then follow weibull distribution with parameters that have been obtained are β = 224; η = ; and γ = Failure Rate (Failure Rate) can be formulated by equation 2:22 as follows: ( 2,24 879,8497 t 89, ,8497 2,24

7 38 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: Reliability Evaluation of Steam Turbine GT-2 with series configuration, Reliability Evaluation of Steam Turbine GT-2 series systems with Configuration Based on the P & ID (Piping and Instrumentation Diagram) GT-2 steam turbine, it can be evaluated using a series system reliability Based on the series circuit function can be determined based on the reliability of steam turbine system reliability Rs ( = R ( x R2 ( x x Rn (, then Rs ( = RA ( x RB ( x RC ( By inserting each derived system reliability function equation as follows: t exp 32,3683 5,822 t 36,4375 x exp 642,5338,694 t 272,549 x exp 969,9429 Evaluation of Reliability Steam Turbine GT-2 with PM, Reliability Evaluation of Steam Turbine GT- 2 with Predictive Maintenance (PM) Determination of the time interval predictive maintenance of steam turbines started every 5 days with maintenance schedule T5, T3, T45, T6, and T75 And T9 Reliability Evaluation of the PM on Time Interval T5 Value after PM reliability, Rm (, at t to 5 to 2 days with n =,2,3,, 2,523 R Tim 8 R 6 (t 4 ) Tim 8 9 e (day s) Fig 3: PM Charts for T-5, T-45 and T-9 s) can be calculated PM reliability with value Rm ( for the T5 can be seen in Figure 3 In the same way for an interval of time; T3, T45, T6, and T75 And T9, at t to 45 to 2 days with n =, 2, 3, do Predictive Maintenance costs, is very influential on the determination of the exact timetable PM Evaluate the cost of PM include loss of product; loss of opportunity when the production plant stopped production, labor, covering the cost of procurement of maintenance performed while the system security, and maintenance costs itself which includes the removal and installation of equipment instrument Downtime required when maintenance is 2 days Here are the results of the evaluation of the cost of predictive maintenance of steam turbines: Table : Evaluation of the cost of the steam turbine PM T(days) n Loss Product Labor cost Total 5 3 US$ 43,56 US$, US$ 5,252, US$ 43,56 US$, US$ 2,424, US$ 43,56 US$, US$,66, US$ 43,56 US$, US$,22, US$ 43,56 US$, US$ 88,2 Table 2: Prediction of breakdown without PM Prediction down Frequency down Loss Product Material cost Labor cost Total time time I 27 US$ 295 US$ 768 US$ US$ II 3 US$ 295 US$ 768 US$ US$ III 32 US$ 295 US$ 768 US$ US$ Calculation of Cost Breakdown, breakdown is the sum of the cost of product loss, labor cost, and the cost of materials purchased for each component Downtime required to perform the replacement of steam turbine components is 6 days So the calculation of the cost breakdown for the steam turbine GT-2 with and without PM (Table -3) Table 3: Prediction of breakdown with PM Predicts Frequency down Loss Product Material cost Labor cost Total down time time n = 3 23 US$,29,5 US$ 7,68 US$, US$ 28,238,434 n = 6 28 US$,29,5 US$ 7,68 US$, US$ 34,377,224 n = 4 3 US$,29,5 US$ 7,68 US$, US$ 38,6,498 n = 3 26 US$,29,5 US$ 7,68 US$, US$ 3,92,78 n = 2 26 US$,29,5 US$ 7,68 US$, US$ 3,92, R (t ) Tim 8 9 e (day

8 39 Ali Musyafa et al, 24 Australian Journal of Basic and Applied Sciences, 8(3) August 24, Pages: Conclusion: Components that have the highest RPN and needs more attention is the speed transmitter and control valve RPN value = 6 The expected value of the reliability R ( = 8 is achieved at t = 3 days In the series system reliability value of the turbine can be increased according to the expected R ( = 8 can be achieved at t = 7 days Evaluation by cost considerations PM Breakdown and maintenance costs obtained time interval between PM (T) (5 days) the time is right for PM It is based on the parameters of Reliability decreased after 7 days with reliability less than 8 while the failure rate) rises By applying PM at intervals of 5 days, then the cost of US $ 5,252,728 Prime Minister and the prediction of the breakdown will happen budgeted cost prediction is US $ 28,238,434 REFERENCES Ali Musyafa, Erna Zulfiana, 23 Risk Management and Hazard and Operability Study on Steam Turbine Power Plant Unit-5 in the Power Generation Paiton, East Java Indonesia, Advances in Natural and Applied Sciences, 7(5): 5-58 Dhillon, BS, 25 Reliability, Quality, and Safety for Engineers London: CRC Press Ebeling, Charles E, 997 An Introduction to Reliability and Maintainability Engineering 2 nd Edition, the McGraw-Hill Companies, New York Kececioglu, Dimitri, 99 Reliability Engineering Handbook Volume I, PTR Prentice Hall, Englewood Cliffs, New Jersey Levitt, Joel, 997 The Handbook of Maintenance Management Industrial Press Inc, 2 Madison Avenue New York Montgomery, Douglas C, 999 Introduction to Statistical Quality Control 6 th Edition United States of America Ramakumar, R, 993 Engineering Reliability: Fundamental and Applications Prentice Hall International, Inc New Jersey Robin E McDermott, Raymond J Mikulak, Michael R Beauregard, 996) The Basic of FMEA Resource Engineering INC, USA Robin, E, MCdermott, Raymond J Mikulak, Michael R Beauregard, 996 The Basic Of FMEA Resource Engineering INC, USA SINTEF, 29 Industrial Management Offshore Reliability Data Handbook 4 th Edition OREDA Participants, 2 Standards Association of Australia, Australian Standard: Risk Management AS/NZS 436