A System for Nuclear Fuel Inspection Based on ... - IEEE Xplore

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 58, NO. 5, OCTOBER 2011

A System for Nuclear Fuel Inspection Based on Ultrasonic Pulse-Echo Technique Zieli Dutra Thomé, Wagner Coelho Albuquerque Pereira, João Carlos Machado, José Manoel Seixas, and William Soares-Filho

Abstract—Nuclear Pressurized Water Reactor (PWR) technology has been widely used for electric energy generation. The follow-up of the plant operation has pointed out the most important items to optimize the safety and operational conditions. The identification of nuclear fuel failures is in this context. The adoption of this operational policy is due to recognition of the detrimental impact that fuel failures have on operating cost, plant availability, and radiation exposure. In this scenario, the defect detection in rods, before fuel reloading, has become an important issue. This paper describes a prototype of an ultrasonic pulse-echo system designed to inspect failed rods (with water inside) from PWR. This system combines development of hardware (ultrasonic transducer, mechanical scanner and pulser-receiver instrumentation) as well as of software (data acquisition control, signal processing and data classification). The ultrasonic system operates at center frequency of 25 MHz and failed rod detection is based on the envelope amplitude decay of successive echoes reverberating inside the clad wall. The echoes are classified by three different methods. Two of them (Linear Fisher Discriminant and Neural Network) have presented 93% of probability to identify failed rods, which is above the current accepted level of 90%. These results suggest that a combination of a reliable data acquisition system with powerful classification methods can improve the overall performance of the ultrasonic method for failed rod detection. Index Terms—Neural network, non-destructive testing, nuclear fuel inspection, ultrasound.

I. INTRODUCTION

A

LONG the last 4 decades, Nuclear Pressurized Water Reactor (PWR) technology has been widely used for electric energy generation. During this period, the follow-up of the plant operation has pointed out the most important items to be monitored in order to optimize the safety and operational conditions. The problem of nuclear fuel failures is in this context, as well.

Manuscript received June 03, 2011; revised July 21, 2011; accepted July 27, 2011. Date of publication September 29, 2011; date of current version October 12, 2011. This work was supported by Eletrobras Termonuclear S.A., FUJB, Conselho Nacional de Pesquisa e Desenvolvimento—CNPq and Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro—FAPERJ (Brazil). Z. D. Thomé is with COPPE/UFRJ-Federal University of Rio de Janeiro, Rio de Janeiro, RJ, 21941-972, Brazil and also with Economy Institute-GESELFederal University of Rio de Janeiro, Rio de Janeiro, RJ, 22290-240, Brazil (e-mail: [email protected]). W. C. A. Pereira, J. C. Machado, and J. M. Seixas are with COPPE/UFRJFederal University of Rio de Janeiro, Rio de Janeiro, RJ, 21941-972, Brazil (e-mail: [email protected]; [email protected]; [email protected]). W. Soares-Filho is with the IPqM-Brazilian Navy Research Institute, Rio de Janeiro, RJ, 21931-090, Brazil (e-mail: [email protected]). Digital Object Identifier 10.1109/TNS.2011.2164557

Since the late 1980s, the nuclear industry perception of what constitutes an acceptable level of fuel reliability has shifted substantially. In the past, reactor startups with cores containing evidences of failed fuel rods were not uncommon. Nowadays, causes of fuel failures are being studied and long-term policies being adopted in order to have an operation with a defect-free core as a strategic objective. This shift in the procedure is largely due to recognition of the detrimental impact that fuel failures have on operating cost, plant availability, and radiation exposure [1], [2]. Therefore, with the increased emphasis on plant operation with zero leakage core, the defect detection in rods, before fuel reloading, has become more important. A common practice in the nuclear industry to evaluate the fuel integrity is the inspection of fuel assemblies (FA’s), at the end of each reactor cycle [3]–[5]. This procedure has two major goals: • detection and removal of the leaking FA’s, which could lead to high activity release or significant fuel loss in operation; • determination of the main failure causes for feedback and corrective actions, in operation, design, manufacturing or development. To estimate and characterize the number of occasional fuel defects, it is important to determine the integrity condition of each rod in the core. Numerous methods have been developed for estimating the number of failed rods by analyzing trends in the activities of individual fission products and using sophisticated models based on physical principles [6]–[11]. The methods commonly used to identify the assembly containing a leaking rod include visual inspection and time-intensive sipping operations, as well as the development of automatic procedures of leakage detection. Sipping is the most common technique used to locate failed FA’s in PWR’s. One of the sipping techniques, called in-mastsipping (IMS), installs the testing system on the refueling machine at the reactor building, to identify irradiated leaking FA’s. When the core is unloaded, each assembly is raised from the core to an upper position inside the machine mast. The differential pressure caused by the change in elevation promotes the release of fission products out of the defective rod. An air stream, continuously injected under the FA, drags the gaseous products to a gamma activity measurement unit. IMS test may be ineffective when large fuel failures occur and in such cases, most of the fission gas inside the rods may be released during the reactor operation [5]. If sipping is able to point out a leaking FA, there is the possibility to detect the specific failed rod for its removal from the

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THOMÉ et al.: A SYSTEM FOR NUCLEAR FUEL INSPECTION BASED ON ULTRASONIC PULSE-ECHO TECHNIQUE

respective assembly. This procedure allows the FA reuse in a context of nuclear fuel economy. Eddy current (EC) and ultrasonic testing (UT) are important categories of alternative tests used in the nuclear industry for that purpose. EC testing is performed on each individual fuel rod of a suspicious FA. It consists of removing each rod from a fuel element through a coil. The EC test has high levels of accuracy but implies in the disassembling of the FA. UT is a well-known non-destructive technique within the industry to locate failed FA’s and rods in PWR’s [12], [13]. The UT system inspects for water presence inside of discharged fuel rods, identifying the rod location, for fuel repair or further rootcause determination. Although occasional undercalls and overcalls occur, this technique has demonstrated satisfactory accuracy and reliability. It is one of the most common PWR inspection processes, with currently estimated accuracy and reliability ranging from 80 to 90% [5], [14]. Nevertheless, the presence of thick oxide on the rod surface can attenuate the ultrasonic signal and, consequently, compromise its quality and convey to misinterpretation [5], [14]. Unlike EC, UT eliminates the need to disassemble all the rods from a FA to identify the failures and, thus, reduces the probability of damaging rods through handling. It is important to emphasize that these alternative methods are commercially competitive but, in the strict technological aspect, they can be viewed as complementary as they investigate different properties of the same object (rod failures). This paper presents a prototype of an Ultrasonic Pulse-Echo System, which is designed and developed to identify damaged leaking fuel rods in PWR cores. This system performs an automatic ultrasonic data acquisition/classification for each rod of a FA. Such task involves the development of specific hardware as well as several levels of software (from control to final signal processing and classification). Ultrasonic echo signals (from the rod walls) are acquired and processed to extract quantitative parameters used to classify the rod. This designed system is composed of several independent parts involving an electronically-controlled mechanical system; a special purpose transducer; a data acquisition system; and a signal processing package including signal conditioning and four signal classification methods. Some of these parts have been already presented in conferences of diverse natures [15], [16]. This article is divided in nine items. In the first one, an introduction of the problem and its environment are depicted. Item II describes the Physical basis of the ultrasonic inspection of fuel rods. In Item III, it is shown a general view of the proposed ultrasonic inspection system. Item IV is dedicated to the description of the designed ultrasonic probe. Item V presents the experimental setup and describes the data acquisition protocol. The developed signal processing/classification methods are presented in Item VI while the rod classification results with a comparison between methods is made in Item VII. A discussion is provided in Item VIII, where an integrated view of the system is shown. The conclusion remarks are in Item IX.

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Fig. 1. Schematic diagram (top) and details (below) containing the zircaloy tube wall (thickness of 0.62 mm) and the gap between the tube inner wall and the fuel element. The incident pulse is p while p is the pulse reflected in the tube outer wall. The set of pulses p to p represents the first four multiple reflections occurring inside of the tube wall.

II. ULTRASONIC INSPECTION OF RODS—PHYSICAL DESCRIPTION AND BASIC TECHNICAL PRINCIPLES The plant core comprises 121 FA’s, each one composed of a 16 16 matrix of zircaloy rods, immersed in water. During normal operation, the gap between the cladding and the fuel pellets contains pressurized helium and fission gases. When a failure occurs, then water penetrates into the gap. The UT is based on the detection of water inside the gap, as a signature for rod failure. The gap filled with gas and the high frequency ultrasonic wave impose a high attenuation of the wave transmitted into the gap medium. Although the gap thickness is a fraction of millimeter, the previous conditions suffice to consider it with an infinite thickness as far as the propagation of ultrasonic wave is considered. The fuel rod cross section is schematically represented in Fig. 1. The UT pulse (emitted by the ultrasonic transducer) propagates radially through the tube wall. Considering a plane wave approach, the echo pulses due to the multi-reflections over the tube wall (Fig. 1) are expressed as follows:

(1) (2) (3) (4) with is the ultrasound (US) wave transmission coefficient from zircaloy tube (gap) to gap (zircaloy tube), the US wave reflection coefficient at the interface between the inner is the reflection coefficient at the intube wall and the gap. terface between the outer tube wall and water.

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III. THE ULTRASOUND SYSTEM’S GENERAL VIEW

Fig. 2. Typical echo envelope of the fuel rod obtained from our experiments. The four echo peaks (R to R ) come from the pulse reverberation inside the wall of the rod. The data from the cases (a) and (b) refer to the setup with air or water inside the gap, respectively.

From (1) to (4) it follows:

The ultrasonic system prototype was designed to operate inside a pool, submerged 10 m, and subjected to temperatures around 60 as well as to gamma and beta radiations. The probe is attached to a flexible metallic arm that is guided through the fuel rod matrix by a specially designed XY automatic electromechanical positioning system. The speed of the probe scan through the aisle between the rod columns is chosen to optimize the balance between signal quality versus scanning time. High scanning speeds generate misalignments between the surface of the rod and the probe face, leading to a general decrease in the whole echo signal quality. On the other hand, low speeds increase the scanning time and turn the system useless for practical operational purposes. In order to minimize rescan possibility, 36 independent echo signals were acquired as the probe moved in front of the rod. This quantity of signals avoids statistical analysis with few sample distributions. It is important to point out that, in the real situations, signal acquisition can be complicated by the fact that the rods of the FA’s may be occasionally out of their original position or deformed due to operational causes. Due to the uncertainty on the exact position of the probe within the matrix, the ultrasonic probe continuously transmits pulses with a pulse repetition frequency of 1 kHz, while moving along the aisles. Obviously, this procedure generates a set of echo signals for each rod. Some of these signals, with good Signal-to-Noise Ratio (SNR), are obtained when the probe US beam axis is normal to the rod wall. Other signals, however, can have their amplitudes critically lowered and can lead to a misinterpretation from the signal processing methods. IV. ULTRASONIC PROBE

(5) (6) (7) The US pulse-echo technique is based on the different acoustic impedance mismatch between the Zircaloy tube wall and the content of the gap: gas or water. According to (5) to to follows a geometric (7), the amplitude of the echoes . When water fills the gap (due progression with to a failure), becomes smaller and then, the amplitude of the successive echoes, , decays faster, when compared to an intact rod. Typical plots for the echo envelopes (obtained experimentally) from rods with air or water are shown in Fig. 2(a) and 2(b), respectively, as a function of reverberation time (depth) of the tube wall thickness. The transducer face is quite close to the rod and so a second reflection from the outer wall of the rod is to ), due to reverberations of also shown. Four echoes ( the transmitted pulse inside the rod wall, are shown between the first and second echoes from the outer wall of the rod. The envelope amplitude of the 4 echoes has a decay profile dependent on and consequently, on the medium inside the gap. The profile decay bears fluctuations due to realistic setup situation.

In a standard PWR, the space between neighbor rods is about 3 mm apart. This assembly geometry imposes some constraints on the ultrasonic probe, concerning the choice of the piezoelectric element, probe size, focusing, dead zone and depth resolution between the echo signals reflected from the outer and inner walls of the fuel rod. To be positioned between adjacent fuel rods, the whole probe thickness can not exceed 2.5 mm. For adequate depth resolution, the transmitted ultrasonic pulse length should be shorter than 0.5 . Because the probe face is placed very close to the fuel rod, the dead zone should not extend beyond 1 mm. In order to comply with these requirements, the piezoelectric element must be very thin, operate at a high frequency (above 20 MHz) with a broad spectrum (implying in a low mechanical and electrical quality factors to emit a short pulse). Due to the lack of space to insert an acoustic matching layer at the probe front face, the piezoelectric element must also have an acoustic impedance close to that of the medium (water) in contact with the probe face. To satisfy these demands, the probe was made with a PVDF (polyvinylidene fluoride) piezoelectric polymer film [17]–[19] thickness and gold electrodes [20]. The probe center with 25 frequency was 25 MHz, with a broad spectrum (pulse duration shorter than 0.5 ). The dead zone was strategically avoided by placing the PVDF element 1 mm apart from the probe face,


Fig. 3. Draw of the probe (PVDF film, backing and housing) in contact with the rod in two planes: one perpendicular and the other along to the fuel rod axis. The leaf spring promotes the coupling between probe and rod.

which also prevented from abrasive damage during the scanning. The total probe thickness was less than 3 mm and its active . area was about 20 Fig. 3 depicts a diagram of the probe layout, viewed in two planes, one perpendicular and the other along to the fuel rod axis, including the main components of the probe: the active PVDF piezoelectric film, the backing layer and the housing. The probe was also designed to be attached to a flexible mechanical arm by means of a pair of leaf springs, which allows it to be always in contact with the fuel rod wall, when moving in front of it. The probe geometry and dimensions, the springs and the arm, altogether, provide the conditions to accommodate the probe while it navigates between the rods of a fuel assembly, using the already mentioned computer controlled XY electromechanical positioning system. Due to the cylindrical shape of the target to be tested, the probe was designed to be cylindrically focused, with the focus at the rod axis. With this focusing geometry, the probe emits a wave front coincident with the rod external shape. This indeed optimizes the ultrasonic coupling between the wave and the target. The housing front face was machined to have a cylindrical groove oriented with the fuel rod axis of symmetry. The gap between the PVDF film and the outer wall of the cladding is filled with water. Details about the probe manufacturing and assembling are given in [20]. The probe was tested at 60 degrees Celsius, 2 atm and radiation dose of 80 kGy, which represent the actual conditions of the FA’s pool where the tests are to be carried out. These tests demonstrated that the original characteristics and performance of the probe were kept approximately unchanged after being exposed to these adverse conditions. V. EXPERIMENTAL DATA SETUP AND ACQUISITION PROTOCOL For the laboratory experiment, a prototype of the fuel assembly was built employing genuine rods with air inside (containing no fuel). Some rods were filled with water to simulate cracked rods. The fuel rods were positioned to form an assembly of 16 16 positions. Some of these positions are occupied by guiding tubes and instrumentation tubes, and the other positions have fuel rods. In total, data were acquired for 205 rods, in which 14 of them filled with water. This experimental setup simulates the inspection of a large number of assemblies, as cracked rods are found to be quite rare in the actual operational conditions.

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In this work the assembly was scanned through one single assembly face. The continuous emission of ultrasonic pulses allowed the shortening of the inspection cycle of the assembly and the gathering of a significant number of echo signals for each rod (around 36 signals in average), which increased the reliability of the results. The optimal condition of the ultrasonic beam normal to the rod surfaces was not met for many of the acquired signals, resulting in random fluctuations in the echo signal pattern for both classes (rods with water or gas), and potentially increasing the difficulty of the detection task. Similar fluctuations can be also expected during real case inspection procedures. So our experimental conditions were able to simulate such scenario. The rod deformation, due to reactor operational conditions, also contributes to these fluctuations. It is important to point out that a lower scanning speed will allow a better accommodation of the probe to the rod surface. The transducer, connected to a pulser-receiver equipment, emitted pulses (spikes of 150 V amplitude) with a duration of 91 ns. The pulse repetition rate was 1 kHz and the echo envelope was digitized with a sampling rate of 110 MHz, resulting in 220 samples per event. VI. SIGNAL PROCESSING CRITERIA The US signal analysis was performed on the envelope of the echo signals, considering the inner four reflections (R1 to R4, in Fig. 2). Three different methods were developed: 1) Exponential decay estimation, 2) Fisher linear discriminant, and 3) Artificial neural network. The exponential method is based on a physical model of the US wave propagation characteristics inside the rod shell. The amount of reflected energy at the internal interface of the rod wall depends on the contents of the gap. It is expected that a train of reflections from a failed rod decays exponentially and faster than the one from an intact rod. The exponential coefficient is taken as representative of each rod. Then a clustering method is used to separate the group of coefficients into two different rod families: GAS or WATER. The condition of the beam axis normal to the external surface wall of the rods is important for this approach to be used efficiently, since a well-behaved exponential decay tends to be produced in such orthogonal condition. With the continuous emission of ultrasonic pulse while the probe is moving, not all signals are of good quality. Therefore, a previous signal validation is required, which was performed by imposing the first peak from the echo signals to be above a certain threshold (defined heuristically among other possibilities). The second method is the Fisher discriminant which is a classical statistical method that develops a mapping aiming at reducing the dimension of the pattern input vectors, while preserving optimal separation between classes [21]. The third method performs a non-linear signal processing: a three-layer neural network was fed from the echo-signal samples and was designed using a fully connected, feedforward topology, which was trained with backpropagation method [22]. For this, the set of echo signals was equally split into two, forming the training and testing sets. The signal testing set did not participate in the training phase and, therefore, was

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used to evaluate the signal classification performance generalization. The activation function was the hyperbolic tangent. A single hidden layer (10 neurons) neural network processed all acquired signals from a given rod and a network output (single neuron with training target values of 1 for water and 1 for gas) above zero classifies a signal as from a failed rod or as an intact rod otherwise. Voting over all labeled signals results in final rod classification. The neural processing proved to be robust to US signal fluctuations, so that signal validation was not required and consequently full signal echoes statistics could be used. This is a quite attractive feature for using the neural processing in this application, as signal fluctuation is being considered a major drawback in ultrasound-based monitoring systems. A forth method, the ratio of the echo amplitudes R4/R1, a well known technique in the area, was also implemented to compare with the other three implemented methods. VII. CLASSIFICATION RESULTS The UT system was tested using a FA prototype. Approximately 5,000 US echo signals (4,368 from air-filled rods and 284 from failed rods, approximately 36 signals per rod) were processed. Firstly, the signals were time-aligned based on the relative delay obtained from the correlation of each incoming signal with a reference one, selected as typical from the database. Only 135 samples from the resulting time-aligned echo signals were retained for feeding the classifiers. Such samples came just after the first echo signal envelope (reflection from the rod front wall) reached the baseline and they fully characterize the following four peak structure of the signal envelope (Fig. 2), which is related to inner reflections within the rod. These 135 samples were used as input to the Fisher and NN classifiers. To obtain an accurate signal, the sensor and the rod must be well aligned. However, this is not always the case, as misalignment may occur during signal acquisition. Fig. 4 presents the highest echo amplitude peak profile as the arm containing the US transducer moved along one of the aisles of the fuel assembly. These amplitude peaks form a plateau which establishes a range of probe positions relative to each rod. The echoes obtained from these positions have good signal-to-noise rate. However, outside this central region, part of the incident pulse energy is reflected in other directions, and so the echo amplitude decreases substantially, occasionally compromising the signal quality. The final rod classification decision was established by each implemented method (see Table I). Both, Fisher and neural network techniques, independently detected 93% of the failed rods (which corresponds to identifying 13 from 14 failed rods in the studied FA), but the neural network obtained a considerable lower false alarm level (2% error in non-failed rods). The exponential method had, however, a poorer performance (detected only 10 failed rods from the 14 ones). As the R4/R1 ratio is intended to serve as a comparison to the other methods, its performance was obtained by finding the optimal threshold found with “privileged” information of data, so as to get the best possible separation. This threshold is consistent with the theoretical values obtained for R4/R1, considering

Fig. 4. Profile of the highest peak amplitudes for different relative positions (distances) transducer-rod. R and R refer to the first and fourth reflected echo amplitude peaks, respectively. The rod contains water in the gap.

TABLE I PERFORMANCE OF DIFFERENT DETECTION METHODS

3 Optimal separation threshold

water or air rods. This technique detected 10 from the 14 failed rods. VIII. DISCUSSION This paper proposes a system to identify failed fuel rods in PWR nuclear reactors, based on the association of a specially designed US transducer and signal processing methods. There are other ultrasonic methods being developed to detect failed fuel rods [23]. The focus of the present paper was to try to improve methods already used in operational field. The present work gives an integrated view of the system, including new approach original processing and classification methods in order to enhance its global performance. A summary of the different method performances is depicted in Table I. The results were obtained with an experimental setup using an original (intact) non-irradiated FA. It is well known that a successful signal processing classification basically depends on the quality of data acquisition and on the choice of classification methods as well. In the system here developed, the US probe continuously emits and receives signals while it is passing between the rods. Due to this acquisition procedure, the signal presents a random fluctuation that can be assumed to be similar to the one when working in the real case (FAs already used in the nuclear reactor core). We were able to develop a classification method, based on an artificial neural network (ANN), which could handle all signals, regardless of their signal-to-noise levels. The other two implemented classification methods had to discard low quality signals (those outside the amplitude plateau, as in Fig. 4). Nevertheless, they were able


to correctly classify some signals (8–14%) that the ANN could not. Translating signal detection into rod identification performance, the false alarm probability was 2%, whereas the probability of correctly identifying failed rods amounted to 93% (Table I). In terms of the assembly prototype used for detection, the performance achieved corresponds to misclassifying only one single failed rod in the assembly, which means that more tests on a larger database would be required to state more precisely the ultimate limits of the neural processing technique. Among the three methods tested for classification, the best individual performance was obtained for the neural classifier. In spite of their high signal-to-noise ratio fluctuations, all signals in the database could be considered for the statistical representation of rod status, as the neural classifier proved to be less sensitive to such fluctuations. Comparing the three methods with the R4/R1 technique, it becomes evident that two of them have higher performance (Fisher and Neural Network) and the exponential method has similar detection performance for failed rods and lower ratio for rods with air; nevertheless it is important to remember that the R4/R1 threshold was chosen as the best one for these data. We have also calculated an average echo-signal for each rod and tested the following amplitude ratios R4/R1 and to verify their classification potentials. No statistical significant difference was found with respect to the ones presented in Table I. In the event of classification doubts, it is possible to enhance the classifier performance by, for instance, a hierarchical combination of the discriminating methods compared against the neural network. Another possibility for clarifying the classification doubt is a new signal acquisition for this specific rod in an orthogonal direction. It is important to notice that it is also possible to acquire signal during the retreat of the probe back from the aisle. Preliminary tests have demonstrated the feasibility of this procedure. These signals could be used for improving the performance of the classification methods, as each rod would have virtually the double of signals. Considering the overall performance of the proposed system one can observe that the ultrasound-based method was a powerful tool for identifying failed rods within the fuel assembly context. It is important to remark that the laboratory experimental setup was essentially the same as in real case concerning geometrical and material components, rod and assembly configuration. Thus, we expect the trained models to have an adequate performance, nevertheless additional tests may be required to evaluate factors that might impose additional signal training for developing a specific model. It is also worth to mention that, strictly from the technical point of view, the first step to identify a failed rod would be the identification of the assembly, through sipping techniques. The second step would be identifying failed rods with an US method. Then, the last step would be repairing the assembly by picking just the indicated rods, and substituting them. In general, the best quality signals come from around the center of the rod surface. In those positions there is more en-

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ergy due to reverberation (low energy loss), which means that more information is embedded on the signal. In this case, a good quality signal means that a reverberation pattern is present in the signal and not that the signal would have a high signal-to-noise ratio. IX. CONCLUSIONS A system prototype to identify failed fuel rods in PWR nuclear reactors based on ultrasound technique was developed. Although US inspection is a well known technique, we were able to improve its performance mainly by coupling new features in hardware and signal processing, represented by the following contributions: a) A new specifically designed thin robust transducer for ultrasound signal acquisition. This transducer was able to generate accurate signals, which are to be used in a rod classification scheme; b) Several classification methods have been tested. The chosen classification method was based on a neural network, and has shown a performance of 93% in identifying failed rods (higher than the range 80–90%, usually obtained by nuclear industries). The accuracy achieved indicates that ultrasound is a feasible technique, especially when associated to elaborated classification methods. This scenario points out for the system implementation, in the real case, for inspection of fuel assemblies in Nuclear Power Plants. ACKNOWLEDGMENT The authors would like to thank the Brazilian agency CNPq and the following institutions: ELETRONUCLEAR (operational and financial support), SENAI/RJ (mechanical parts preparation), IEN (laboratory support), IPEN (transducer beta irradiation test), IME (transducer test for gamma irradiation), and FEC (fuel rod prototype fabrication). They would also like to thank Marcelo de Albuquerque Thomé, Francisco Nippo, Walter Pinto de Carvalho, Walter Guerreiro da Silva, and Amauri de Jesus Xavier. REFERENCES [1] Electric Power Research Institute, Fuel Integrity Reliability Improvement Guidelines EPRT, Charlotte, NC, Rep. Tr-1000659, 1992. [2] Electric Power Research Institute, Fuel Integrity Monitoring and Failure Evaluation Handbook Tr-1003407, 2003, Rev. 1. [3] P. File, “Field experience with failed-fuel detection-PWRs,” Trans. Amer. Nucl. Soc., vol. 61, p. 48, 1990. [4] S. D. Kreider and A. Schneider, “The detection of failed fuel in LWRs—A historical review,” Trans. Amer. Nucl. Soc., vol. 61, no. 47, pp. 46–47, 1990. [5] International Atomic Energy Agency, “Review of fuel failures in water cooled reactors,” Nuclear Energy Series, NF-T-2.1, p. 53, 2010. [6] A. Dumont, “FRAGEMA fuel reliability: From detection of fuel failures to the feedback on design and fabrication,” in Proc. Tech. Comm. Meeting Fuel Failure in Normal Operation of Water Reactors: Experience, Mechanisms and Management, Dimitrovgrad, 1992, Vienna, Austria, 1993, vol. 46, IAEA-TECDOC-709, IAEA. [7] G. P. Stora and P. Chenebault, “Defect fuel rod evaluation in EDF PWR reactors,” in Proc. ANS-ENS Top. Meeting Reactor Safety Aspects of Fuel Behaviour, Sun Valley, ID, 1981, vol. 1, p. 119, CENG 1981. [8] J. B. Genin et al., “DIADEME: A computer code to assess in operation defective fuel characteristics and primary circuit contamination,” in Proc. Int. Conf. Water Chemistry of Nuclear Reactor Systems, Avignon, France, 2002, FNES.

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[9] A. Tigeras et al., “MERLIN: Modelling fuel defects at EDF power plants,” in Proc. Int. Conf. Water Chemistry of Nuclear Reactor Systems, San Francisco, CA, 2004, EPRI, 2004. [10] A. Tigeras et al., “Improvement of fuel failure assessment based on radiochemical parameters (MERLIN code) taking in account the thermal mechanical fuel rod calculations (CYRANO3 code),” in Proc. Int. Conf. Water Chemistry of Nuclear Reactor Systems, San Francisco, CA, 2004a. [11] D. Parrat, J. B. Genin, Y. Musante, C. Petit, and A. Harrer, Failed Rod Diagnosis and Primary Circuit Contamination Level Determination, Thanks to the DIADEME Code IAEA-TECDOC-1345, IAEA, Vienna, Austria, 2003. [12] M. Attar, “State-of-the art ultrasonic detection of failed fuel,” Trans. Amer. Nucl. Soc., vol. 61, pp. 47–48, 1990. [13] A. Antolovic and B. Kurincic, “NPP Krsko experiences with spent fuel assembly inspection methods,” in Proc. Int. Conf., Nuclear Energy for New Europe, Portoroz, Slovenia, Sept. 8–11, 2008, pp. 1011.1–1011.11. [14] International Atomic Energy Agency, Review of Fuel Failures in Water Cooled Reactors, Tech. Rep. Ser. 388, 1998, p. 56. [15] J. M. Seixas, W. Soares-Filho, M. C. Bossan, Z. D. Thomé, and W. C. A. Pereira, “Neural identification of failed fuel rods in nuclear reactors,” in Proc. IEEE Int. Symp. Circuits and Systems , Geneva, Switzerland, 2000, pp. I-160–I-163. [16] Z. D. Thomé, W. C. A. Pereira, J. C. Machado, J. M. Seixas, W. SoaresFilho, and J. L. Chapot, “Ultrasonic system for fuel assembly inspection in pressurized water reactors,” in Proc. IEEE Ultrasonics Symp., San Juan, Puerto Rico, 2000, pp. 1–5.


[17] N. Murayama, K. Nakamura, H. Obara, and M. Segawa, “The strong piezoelectricity in polyvinylidene fluoride (PVDF),” Ultrasonics, pp. 15–23, 1976. [18] E. Fukada, “History and recent progress in piezoelectric polymers,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control, vol. UFFC-47, no. 6, pp. 1277–1290, 2000. [19] R. G. Swartz and J. D. Plummer, “On the generation of high-frequency acoustic energy with polyvinylidene fluoride,” IEEE Trans. Sonics Ultrason., vol. SU-27, no. 6, pp. 295–303, 1980. [20] J. C. Machado, Z. D. Thomé, A. J. Xavier, J. C. A. C. R. Soares, and W. G. Silva, “An ultrasonic probe for NDT inspection of fuel assembly used in nuclear power plant reactors,” presented at the 15th World Conference Non-Destructive Testing, Rome, Italy, Oct. 15–21, 2000. [21] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Hoboken, NJ: Wiley, 2004. [22] S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ: Prentice-Hall, 2008. [23] H. Kwun, E. V. Mader, and K. J. Krzywosz, “Guided wave inspection of nuclear fuel rods,” in Proc. 7th Int. Conf. NDE in Relation to Structural Integrity for Nuclear and Pressurized Components, Yokohama, Japan, May 12–15, 2009, pp. 1–7.