Reliability Centered Maintenance Plan for the Utility Section of a Fertilizer Industry: A Case Study Muhammad Abid*, Suleman Ayub, Humza Wali, Muhammad Najam Tariq Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi, 23640, Swabi, KPK, Pakistan *abid@giki.edu.pk Abstract This paper provides an insight to the application of Reliability Centered Maintenance (RCM) and life data analysis. The objective is to formulate a Failure Management Policy for the utility section of a fertilizer industry. It focuses on how key elements of the RCM process can be combined to select appropriate policies for managing a system s failure modes and their consequences, using an RCM decision algorithm. The foremost objective of Reliability Centered Maintenance plan is to optimize reliability of physical assets while being cost effective. Furthermore in addition to traditional RCM, life data of the machinery is analyzed using empirical analysis of Weibull distribution. The estimates of the parameters of Weibull distribution are found using Median Rank Regression (MRR) method. Each machine is then matched with one of the six most widely accepted patterns of failure. In the end these two techniques i.e. RCM and Weibull Analysis are integrated in a manner to formulate an optimized Failure Management Policy. The plan is in compliance with RCM SAE JA1011 standard. example, deductions and results obtained can be applied to the all the systems of ammonia/urea complex plant. II. THEORETICAL BACKGROUND A. Failure Curves Failure rate or hazard rate is defined as probability density function divided by reliability [2]. There are a total of six failure curves associated with most of the equipment installed in an Industry which are shown in Fig. 1. The shape of the failure curve allows us to identify whether the failure mode was an early life or infant mortality stage failure, a randomly induced failure or due to wear out and aging. These curves are formed by plotting failure rate of equipment with respect to time. Keywords: RCM, Reliability Engineering, Failure Management Policy, Life data analysis. I. INTRODUCTION Utility plant is a very important and critical section of a Fertilizer Industry. The utility plant provides services to the ammonia/urea complex and other areas. The main products of the company are ammonia and urea. This paper aims at formulating a maintenance plan based on RCM methodology for the utility plant machinery. Application of RCM can improve reliability and availability while minimizing the downtime and maintenance cost. Reliability centered Maintenance is a process used to determine what must be done to ensure that any physical asset continues to do what its users wanted it to do in its present operating context [1]. RCM philosophy employs preventive maintenance, predictive maintenance (PdM), real-time monitoring (RTM), run-to-failure (RTF) and proactive maintenance techniques, in an integrated manner to increase the probability that a machine or component will function in the required manner over its design life cycle with a minimum of maintenance [1]. Although it has been established that we need to implement either of the above mentioned five maintenance techniques but the real challenge is to find the correct and the most effective combination of each, for different equipment, with different failure curves. In this paper we have taken Condensate recovery and boiler feed water system as a working Figure 1 Six Failure Patterns as Identified by Nolan and Heap. [3] 9
B. Weibull Distribution Weibull Distribution is one of the most widely incorporated distribution which is used to carry out life data analyses in reliability engineering..it s probability density function (PDF) or f(t) is given by [4]: Where: ( ) ( ) β is the Weibull shape parameter which determines the shape of the Weibull plot. α is the scale parameter which represents the characteristic life at which 63.2% of the population is expected to fail. "γ" is the location parameter or failure free life. t is the time. The Weibull Distribution is said to be two-parameter distribution if γ=0 [5]. Keeping all the parameters constant and varying β can give different failure curves [6]. β<1 Early life or infant mortality stage failure. β=1 Constant failure rate curve. β>1 Wear out or failure due to age curve. III. RELIABILITY CENTERED MAINTENANCE For an effective RCM plan the following tools and techniques were applied. A. Selection of system System selected for the implementation of RCM plan is Utility Section of a Urea Plant. (1) B. Selection of subsystem Following Subsystems from the plant are selected: 1) Condensate Recovery and boiler feed water system. 2) Electricity Generation System. 3) Fire Water System. 4) Plant and Instrument Air system. Condensate Recovery and boiler feed water system is selected as a case study for this paper. Same steps and maintenance decisions combinations can be applied to the rest of the systems. Fig. 2 shows the functional block diagram for condensate recovery and boiler feed water system. C. Acquiring the failure data Complete failure data was acquired from the plant using the Enterprise Resource Planning (ERP) software AS 400, produced by IBM. D. Identification of functions The function of the Condensate recovery and feed water system is to supply high-pressure water to the boiler. The feed water is physically and chemically de-aerated, further preheated in steam Boiler feed water heater and distributed to the process steam Boilers and miscellaneous users. Under normal operating conditions two of the three 50% capacity High pressure Feed water Pumps (P402 A B and C) take suction from the de-aerator storage section and pump feed water to the Steam Boiler Feed water Heater (E- 403, which is not a part of this system). The System automatically maintains the proper flow to the boiler drum. A minimum head of 1850 ft. is required from the pumps rated at 615 gpm, these pumps have an initial capability of 1950 ft., and 100 ft. is the margin for deterioration after which the pump will be repaired or replaced. Figure 2 Condensate Recovery and Boiler Feed water system Schematic diagram [7]. 10
Figure 3 Condensate Recovery and Boiler Feed water system functional block diagram The boiler feed water system is a flow process in compliance with ISO 9001. This system operates for 24 hrs. per day and has a redundant pump (P402 C). All spare parts of a pump are available in the warehouse [7]. E. Selection of Critical Equipment Following critical equipment were selected. a) Pump (P-402A) b) Pump (P-402B) c) Pump (P-402C) d) Turbine (TP-402AB) e) Motor (MP-402C) Criticality of the equipment was based on the functionality and importance of the functions performed by the above mentioned equipment. P-402 A and B are run by TP 402AB which is also as critical as P402 A and B. Pump P402 AB and TP 402AB are the most critical and work all the time to deliver the feed water, while the P 402C is a standby pump and is not as critical as P402 A/B. MP 402C is used to run P 402C in standby operations. F. Actuarial Analysis of the failure Data The Weibull cumulative distribution function (CDF), denoted by F w (t) is given by: (2) Since for our case the failure free life γ=0 [5] therefore the analysis will become two-parameter Weibull analysis: And the Reliability is given by: (3) ( ) (4) The linear form of CDF equation is given by: ( ( )) (5) For the estimation of Weibull parameters there are two widely used methods to calculate Cumulative Density Function which are given below: a) Median Rank Method using Benard s Formula [8]. b) Kaplan-Meier Estimation. Since Kaplan-Meier estimation requires a large data size to give a useful plot therefore Median rank method using Benard s Formula is utilized in our analyses [9]. F w (t) can be estimated by using Benard s Formula [8] for median rank estimator which is given by: (6) Weibull parameters can be estimated by the following methods: a) Maximum likely hood estimation (MLE). b) Rank regression on X. c) Rank regression on Y. Rank regression on X is used for estimation since MLE is used for censored data whereas the failure data obtained for critical equipment is complete data and regression generally works best for small data sizes (10-11 samples in our case). Rank regression on Y is not used because uncertainty is in time to failure which changes with sample to sample and it is on x-axis of the plots. 11
Therefore Regression on X is used which is given by [9]: ( ( )) (7) Comparing the equation with the equation of Straight line i.e. Hence ; ( ( ( ( )); ; ( ) ( ) )) As β (shape parameter) is less than 1 it suggest an early failure i.e. infant mortality. Hazard rate decreases exponentially right from the start as shown in Fig. 5. G. P-402A and P-402B Analysis For pump 402A β (Shape parameter) is 0.78 and α (Scale parameter) is 577 days which is shown in Fig. 4. Hazard rate is very high in the beginning due to infant mortality and decreases with time. P402B follows the same distribution and model as of P402 A and has a β = 0.85 and α =338 days. Similar results were found of P402B. H. TP-402A/B Analysis For Turbine TP402A/B β is 3.58 and α is 1662 days as shown in Fig. 6. As β is greater than 1 it suggest failure due to wear out. Hazard rate increases exponentially, at approximately 500 days as shown in Fig. 7. The reliability of TP 402A/B decreases exponentially after 400 days, hence scheduled restoration task at approximately after 400 days is recommended as shown in reliability curve Fig. 8. I. P402-C and MP402-C Analysis (Standby System): For Standby systems we perform failure finding tasks. For these tasks failure finding interval (FFI) [10] is determined which is given below: For Standby Systems an availability of 90% is required therefore unavailability is 10%. From the given data MTBF = 100 days (8) Figure 4 shows the Probability-Weibull plot. Shape and scale parameters for P-402A are estimated 12
Figure 5 shows the failure rate or hazard rate plot for P-402A Figure 6 shows the Probability-Weibull plot. Shape and scale parameters for TP-402A/B are estimated. 13
Figure 7 shows the failure rate or hazard rate plot for TP-402AB. Figure 8 shows the reliability plot for TP-402A/B. 14
1 TABLE I. FAILURE MODES AND EFFECT ANALYSIS. Function (F) Condensate recovery system collects condensate and returns it to feed water system, which distributes feed water to process steam boilers and misc. users. Functional Failure (FF) (Loss of Function) A Failed to supply high pressure water to the boiler during startup, normal & emergency operations by achieving a differential head of 1950ft. Failure Mode (FM) (Cause of Failure) 1 P402A fails 2 P402B Fails J. Failure Modes and Effect Analysis An RCM process that conforms to SAE JA1012 states, All the failed states associated with each failure shall be identified [10]. A failure mode could be defined as any event which is likely to cause an asset (or system or process) to fail. A system can fail for many reasons, for the complete plant these failure modes or reasons can rise into thousands. For our system we have listed down the following main causes that prevent the system from performing its complete function. These causes could be drilled down further into the function failures of sub systems, but we have to decide on the level which is practically feasible for a particular industry. Once the failure modes are identified it then becomes possible to assess the root cause of these failures and decide on the relative maintenance techniques and intervals. Function failure that represents total failure can be identified easily, for our system a total failure will take place when either of pumps P402 A/B or P402 C fails. 3 4 5 TP402 A/B Fails P402C Fails (Standby) MP402C Fails (Standby) Partial failures may be many for example if only one pump fails, turbine fails of motor fails which is shown in Table 1. K. RCM tree Diagram and Decision Worksheet: RCM decision diagram (Fig. 9) integrates all the decision processes into a single framework. The RCM decision worksheet, Table 2 is filled with the help of RCM decision diagram and Table I (Failure modes and effect analysis). Furthermore the proposed tasks and interval are selected by considering Actuarial analysis of RCM methodology. From Table 1 it can be seen that equipment with early life failure curve are put on increased monitoring directly after commissioning and keeping a spare nearby may be helpful until the cause of failure is found and resolved. Root cause analysis of the equipment itself is recommended to find out the cause and eliminate it. For equipment with wear out failure a Scheduled restoration task is suggested so that potential failure could be avoided and hence making the whole maintenance cost effective. For Standby equipment a failure finding interval of 20 days is determined. IV. CONCLUSION All the techniques of maintenance including preventive, predictive and proactive techniques are currently being applied in the fertilizer plant under study. A new approach has been used in which RCM is integrated with life data analysis in order to accurately estimate the failure mode followed by each component of the system. Using this technique a better failure management policy is developed keeping in view the health of each equipment. This RCM plan helps to optimize reliability of the system while being cost effective and decreasing the system downtime. In addition determination of maintenance tasks and their intervals was achieved by the use RCM as shown in Table II. Figure 9 shows RCM Decision Diagram 15
TABLE II. RCM DECISION WORKSHEET TABLE Failed to supply high pressure water to the boiler during startup, normal & emergency operations by achieving a differential head of 1950ft p Consequence Evaluation H1 S1 F FF FM H S E O O1 N1 H2 S2 O2 N2 H3 S3 O3 N3 Default Action H4 H5 S4 Proposed Task Interval Can be done by 1 A 1 Y N N N Y N/A N/A N/A N/A N/A Scheduled On-condition task 1 day Technician 1 A 2 Y N N Y Y N/A N/A N/A N/A N/A Scheduled On-condition task 1 day Technician 1 A 3 Y N N N N Y N/A N/A N/A N/A Scheduled restoration task 400 days Technician 1 A 4 N N/A N/A N/A N N N Y N/A N/A Schedule Failure finding task 20 days Technician 1 A 5 N N/A N/A N/A N N N Y N/A N/A Schedule Failure finding task 20 days Technician V. ACKNOWLEDGMENT The authors acknowledge Ikram.Ahmad Khan, Dawood Hercules Fertilizers Limited for all cooperation for completing this study. VI. REFERENCES [1] J. Moubray, Reliability-centered maintenance., Vols. ISBN 978-0831131463., New York: Industrial Press, 1997. [2] P. MacDiarmid and S. Morris, Reliability Toolkit (Commercial Practices ed.), Rome; New York: Reliability Analysis Center and Rome Laboratory.. [3] F. S. Nowlan and H. F. Heap, Reliability-Centered Maintenance, San Francisco: Dolby Access Press, 1978. [4] R. B. Abernathy, The New Weibull Handbook, Houston: Dr. Robert B. Abernethy, 2004. [5] C.-c. Liu, "A Comparison between the Weibull and Lognormal Models used to Analyze Reliability Data. P.hd thesis," University of Nottingham, UK, Nottingham, 1997. [6] J. F. Lawless, Statistical Models and Methods of life, New York: J WILEY & SONS, 1982. [7] Dawood Hercules Fertilizers Ltd., Utility Plant Training Manual, Lahore: Technical Training Department, 2009. [8] M. S. K. A. H. P. G.R Pasha, "Empirical Analysis of The Weibull Distribution," Journal of Statistics, vol. 13, no. 1, pp. 33-45, 2006. [9] NIST/SEMATECH, e-handbook of Statistical Methods, 2005. [10] S. International, Evaluation Criteria for Reliability-Centered Maintenance (Rcm) Processes- General requirements. SAEJA1012, 2002. 16