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doi: 10.1016/j.pnucene.2003.12.001
FUZZY FMEA APPLIED TO PWR CHEMICAL AND VOLUME CONTROL SYSTEM
Antonio C6sar Ferreira Guimar~es* and Celso Marcelo Franklin Lapa a
Instituto de Engenharia Nuclear (IEN-CNEN) - Divis~o de Reatores / Programa de P6s-Gradua@o, Ilha do Fund~o s/n, Rio de Janeiro, Brazil, Zip Code: 21945-970, Po. Box: 68550
In recent study, the effect of aging on the Chemical and Volume Control System (CVCS) of a pressurized water reactor (PWR) has been evaluated (Grove and Travis, 1995). Since the CVCS provides many normal and emergency operating functions, it is important to understand the effect of aging in order to detect and correct these instances prior to component failures. A detailed review of the Nuclear Plant Reliability Data System (NPRDS) and Licensing Events Report (LER) databases for the 1988-1991 time period, together with a review of industry and NRC experience and research, indicate that age-related degradations and failures have occurred. These failures had significant effects on plant operation, including reactivity excursions, and pressurizer level transients. The majority of these component failures resulted in leakage of reactor coolant outside the containment.
Grove and Travis (1995) visited a representative plant of PWR design Westinghouse to obtain specific information on system inspection, surveillance, monitoring, and inspection practices. The results of these visits indicate that adequate system maintenance and inspection is being performed. In some instances, the frequencies of inspection were increased in response to repeated failure events. Also, a parametric study was performed to assess the effect of system aging on Core Damage Frequency (CDF). This study showed that as MOV operating failures increased, the contribution of the High Pressure Injection to CDF also increased. Failure mode and effects analysis (FMEA) is an important technique (Stamatis, 1995) that is used to identify and eliminate known or potential failures to enhance reliability and safety of complex systems and is intended to provide information for making risk management decisions. A modified failure mode and effects analysi s (FMEA) and knowledge base system (KBS) are proposed here to estimate the risk using scores from experts. Fuzzy logic systems (Zadeh, 1987) is a name for the systems which have relationship with fuzzy concepts (like fuzzy sets, linguistic variables, and so on) and fuzzy logic. The most popular fuzzy logic systems in the literature may be classified into three types: pure fuzzy logic systems, Takagi and Sugeno's fuzzy system, and fuzzy logic systems with fuzzifier and defuzzifier (Wang, 1993). The methodology used in this paper was the fuzzy logic systems with fuzzifier and defuzzifier, as used in the most investigations e.g. Pillay and Wang (2003), Xu et al. (2002) and Guimarfies (2003).
The knowledge-based fuzzy systems allows for descriptive or qualitative representation of expressions such as "remote" or "high", incorporate symbolic statements that are more natural and intuitive than mathematical equation. A direct method with one expert (Klir and Yuan, 1995) was used to aggregate opinion of individual expert.
This work investigates the potential application of knowledge-based fuzzy systems in a case study. The Chemical Volumetric Control System (CVCS) was chosen for this study.
2. DESCRIPTION OF CHEMICAL VOLUME CONTROL SYSTEM (CVCS)
A simplified CVCS system schematic is shown in Figure 1. The primary sub-systems included in this study are:
• volume control storage tank, • boric acid supply, • charging pumps, and • Reactor Coolant Pump (RCP) seal water injection
Most of the components are located outside of containment, so aging degradations may result in external leakage of the reactor coolant. To fully understand the effect of system aging, specific information on the system's operating characteristics, material and design function is presented in the next description.
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The Chemical and Volume Control System (CVCS) for Pressurized Water Reactor (PWR) Westinghouse plant provides both normal and emergency operation functions. During normal operation, the three primary functions are to purify the reactor coolant, control reactor coolant system (RCS) inventory (pressurizer level control), and provide the reactor coolant pump (RCP) seal water injection. During an emergency, the primary functions for the majority of PWR plants are to provide high-pressure safety injection, RCP seal injection, and emergency boration. In addition, the containment isolation system, which includes CVCS valves, isolates the letdown and charging lines.
The typical system design used in the majority of plants is shown schematically in Figure 2. More detail about description of CVCS system can be found in Grove and Travis (1995).
194 A. C. E Guimar6es and C. M. F. Lapa
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3. DESCRIPTION OF FUZZY INFERENCE SYSTEM APPROACH
The "pure fuzzy logic system" is the system where the fuzzy rule base consists of a collection of fuzzy IF-THEN rules, and the fuzzy inference engine uses these fuzzy IF-THEN rules to determine a mapping from fuzzy sets in the input universe of discourse U C R " to fuzzy sets in, the output universe of discourse V C R based on fuzz3; logic principles. ~rhe fuzzy IF-THEN rules are of the following form:
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T H E N y is G l (1)
Fuzzy FMEA 195
where Fli and G t are fuzzy sets, x = (xl,. ..... , Xn) T ~ U and y ~ V are input and output linguistic variables, respectively, and l = 1,2, .. . , M. Practice has shown that these fuzzy IF-THEN rules provide a convenient framework to incorporate human experts' knowledge. Each fuzzy IF-THEN rule (Eq. 1) defines fuzzy set F~ x ... xF ln ~ G 1 in the product space U x V. In order to use the "pure fuzzy logic system" in engineering systems where inputs and outputs are real-valued variables. The most straightforward way is to add a fuzzifier to the input and a defuzzifier to the output of the pure fuzzy logic system. The fuzzifier maps crisp points in U to fuzzy sets in U, and the defuzzifier maps fuzzy sets in V to crisp points in V. The fuzzy rule base and fuzzy inference engine are the same as those in the pure fuzzy logic system. In the literature, this fuzzy logic system is often called the fuzzy logic controller since it has been mainly used as a controller. It was first proposed by Mamdani and Assilian (1974), and has been successfully applied to a variety of industrial process and consumer products. A detailed description of this fuzzy logic system can be found in Wang (1993).
4. APPLICATION OF THE PROPOSED APPROACH TO CVCS
Initially, an "expert" with knowledge domain on the analyzed system was adopted. The selected specialist belongs to the reliability engineering and can participate in other domains. A traditional FMEA using the risk priority number (RPN) ranking system is carried out in the first moment. Mathematically, RPN is represented as:
R P N = O x S x D (2)
where, "O" represents the probability of occurrence, "S" represents the severity of the failure and "D" represents the probability of not detecting of the failure. The values for O, S, and D are obtained by using the values scaled presented in Table 1 (Xu et al., 2002 and Pillay and Wang, 2003). The expert in the FMEA analysis was the same as in the proposed.
Table 1 - Traditional FMEA scales for RPN.
Occurrence (O)
Severity (S)
Not Detection (D)
Rating Possible failure rate (operating days) Probability Range (%)
As presented in Figure 2, the primary functions of the CVCS are letdown, purification, boration and chemical addition, boron regeneration, charging, RCP seal injection and safety injection. The system consists of the mechanical components (pumps, valves, heat exchangers, volume control tanks, and de!onizers), instrumentation, and controls, necessary to perform these functions.
A Failure Modes and Effects Analysis (FMEA) was performed to determine the effects of failure of the major system components. Each FMEA included the following items: (a) Failure Mode: the basic manner(s) which a component may fail or cease to perform as design. The failure modes for these components were consistent with those used in industry reliability; (b) Failure Cause: the particular type of degradation mechanisms, which may cause the component to failure (stresses); (c) Failure Effects: the effects on the CVCS system due to the component failure; (d) Detection Methods: functional indicators or system and plant operating characteristics, which would alert the operator of component degradation and/or failure.
An important system function in many PWR plants is to provide High Pressure Injection under certain accident conditions. Since this function was previously evaluated (Meyer, 1989), it was not included in these FMEAs. However, it is important to recognize that many of the CVCS components that provide reactor charging are used for High Pressure Injection. Aging degradation and failures of these components, which result from normal plant operation, will also affect their ability to provide high-pressure injection. It is essential that system aging be understood, and detected, before it results in the inability of the system to perform its safety-related function.
The FMEA analysis for Chemical Volumetric Control System (CVCS) is summarized in Appendix 01. Grove and Travis (1995) developed the FMEA and the "expert" the numbers for O, S and D.
4.1 - Fuzzy membership function
Making use of the toolbox simulator of Matlab (2000), the expert was invited to define each membership function and the values in the universe of discourse using the interpretations of the linguistic terms described in Table 2 (Pillay and Wang, 2003). The expert chose the triangular membership function (a, b, c). After that, the following question may be answered by the expert: "Which elements x (a,b,c) have the degree of membership ~ a = zero, ab = one and ~tc= zero". Direct methods with one expert (Klir and Yuan, 1995) were used. The five linguistic terms describing the input are Remote (R), Low (L), Moderate (M), High (H) and Very High (VH), and for output are lowly (LL), Low (L), Fairly Low (FL), Moderate (M), Fairly high (FH) and High (H). The membership functions of the five linguistic terms to input are shown in the Figure 3. The six membership functions for output in Figure 4. The graphical representation of membership function to occurrence, severity and not detection are identical and only one, occurrence was shown.
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198 AI C. E Guimar6es and C. M. F. Lapa
4.2 - Fuzzy rule base application
Since there are three factors, occurrence, severity and not detection, and five linguistic terms describing each factor, the total number of rules is 125. The total number of rules in the fuzzy rules base is reduced to 14 rules for this system analyzed with extensive FMEA after some simplifications to reduce the numbers of rules of the fuzzy rule base. These rules can estimate the results and are presented
1. If (Occurrence is M) and (Severity is VH) and (Not detection is VH) then (Risk is H) (1)
2. If (Occurrence is M) and (Severity is H) and (Not detection is M) then (Risk is H) (1)
32 If (Occurrence is M) and (Severity is M) and (Not detection is M) then (Risk is FL) (1)
4. If (Occurrence is L) and (Severity is VH) and (Not detection is M) then (Risk is FL) (1)
5. If (Occurrence is L) and (Severity is H) and (Not detection is H) then (Risk is FL) (1)
6. If (Occurrence is M) and (Severity is M) and (Not detection is L) then (Risk is L) (1)
7. If (Occurrence is M) and (Severity is H) and (Not detection is L) then (Risk is L) (1)
8. If (Occurrence is L) and (Severity is M) and (Not detection is M) then (Risk is L) (1)
9. If (Occurrence is L) and (Severity is M) and ~Not detection is M) then (Risk is FL) (1)
10. If (Occurrence as L) and (Severity is R) and (Not detection is L) then (Risk is LL) (1)
11. If (Occurrence as L) and (Severity is L) and (Not detection is L) then (Risk is LL) (1)
12. If (Occurrence as L) and (Severity is M) and (Not detection is L) then (Risk is L) (1)
13. If (Occurrence as R) and (Severity is M) and (Not detection is M) then (Risk is LL) (1)
14. If (Occurrence is R) and (Severity is VH) and (Not detection is M) then (Risk is FL) (1)
Fuzzy FMEA
Table 2 - Interpretations of the linguistic terms for developing the fuzzy rule system.
In the Table 3 are presented the results for RPN and fuzzy approach. "ID" is component event, RPN and Fuzzy are the risk numbers and Rankingrpn and Ranking_fuzzy are the ranking of RPN and fuzzy methodology, respectively. For example, the components 33, 52, 53, 54, 66, 68, and 69 produce a result of 144 for RPN method and the same Rank_rpn of value 12. Fuzzy approach produces for component 66 the highest risk priority number followed of events 33, 52 and 53 and the lowest risk priority number for events 33, 52 and 53. For events 9, 10, 11 and 12, the defuzzified ranking is 2 and the four events should be given the same fuzzy risk priority number 5. The RPN method, produces a result of 45, 75, 60 and 100 for events 9, 10, 11 and 12, respectively. This means that event 12 has the highest priority followed by event 12, 10 and 9, respectively. High level of uncertainty in the safety analysis data could be representing a problem.
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6. CONCLUSION
In this paper a new approach was proposed using the "Fuzzy Inference System" (FIS) applied to estimate "Fuzzy Risk Priority Number" (FRPN) using the expert opinion for quantify linguistic variables. This article introduces in the nuclear area a capable methodology to subsidize new reactor projects through of the fuzzy identification of critical systems and components and possible failure modes.
Another important contribution of this approach to nuclear area is propitiate the building of a linguistic knowledge bases for studies of extension of residual life in aging components. Is important to mention that, the population of commercial nuclear power plants has matured and its principal safety and operational components are under aging process. By the year 2014, forty-eight of the wide world plants will have been operating for forty years (design life expectance).
In this paper, to exemplify the methodology, the extensive Failure Mode & Effects Analysis (FMEA) of Chemical and Volume Control System (CVCS) was used as nuclear power plant application. This was a simple and complete example, where a reduced number of rules in the knowledge base ware necessary to mapping all analysis situations.
According to Table 3, different ranking are obtained using the fuzzy approach proposed in a case of the same value of RPN but with different values assigned for occurrence, severity and not detection. The risk implication may be very different (Bem-Daya and Raouf, 1993 and Gilchrist, 1993). The advantages of the proposed fuzzy rule base for application to FMEA of CVCS can be summarized as follows:
• This fuzzy approach combines (i) expert knowledge and experience for use in an FMEA study,
and (ii) can be used for systems where safety data is unavailable or unreliable.
Converting the scale of RPN traditional in (i) variable linguistic with values defined as input by expert is the great situation and (ii) not force precision and use the system by people without knowledge about interpretations of these linguistic terms. Permitting to use the fuzzy system in a simple way.
ff some change is made in part of system component or sub-component of system analyzed as result of FMEA study, new ranking results after improvements can be obtained so quickly using the "Fuzzy Inference System" (FIS).
In accordance with Grove and Travis (1995) the results of this NPAR (Nuclear Plant Aging Research program) study show that aging degradation and failures has occurred in the CVCS. These failures have not prevented the system from responding as designed in an emergency, but have resulted in normal plant operation perturbations. These occurrences have resulted in unnecessary actuation and operation of other system components in response, cause unnecessary stresses. The results of the plant visits indicate that significant attention is being concentrated on the CVCS, and that maintenance practices are being employed in response to specific component failure histories. However, the larger number of failure events reported to the databases (NPRDS and LER),
204 A. C. E Guimaraes and C. M. F. Lapa
indicating that: system failures are still occurring, highlights the need for continued attention to the Operation and aging of the system.
Future work for risk is the "control system" that can be developed with "Simulink" using FIS and the factor values; occurrence, severity and not detection, where recommendations are used for control factors values.
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l
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Fuzzy FMEA 205
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