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An improved method for reactor coolant pump abnormality monitoring using power line signal analysis
Nuclear Engineering and Design 236 (2006) 57–62
An improved method for reactor coolant pump abnormality monitoring using power line signal analysis Jae Cheon Jung ∗ , Poong Hyun Seong Korea Power Engineering Company, Korea Advanced Institute of Science and Technology, 150 deokjin-dong, Yuseong-ku, Daejeon, Republic of Korea Received 25 June 2004; received in revised form 19 June 2005; accepted 19 June 2005
Abstract An improved method to detect the reactor coolant pump (RCP) abnormality is suggested in this work. The monitoring parameters that are acquired from power line signal analysis are motor torque, motor speed and characteristic harmonic frequencies. The combination of Wigner–Ville Distribution (WVD) and feature area matrix comparison method is used for abnormality diagnosis. For validation of the proposed method, the test was performed during cool-down phase and heat-up phase in nuclear power plant (NPP) by cross-comparison with RCP vibration monitoring system (VMS). Using pump internal inspection results, the diagnosis prediction is verified. © 2005 Elsevier B.V. All rights reserved.
1. Introduction Recently, a non-intrusive, sensorless monitoring technique has been introduced in NPP for abnormality monitoring of motor driven rotating machinery. The motor current signature analysis (MCSA) has been developed and commercialized as an alternative or supplementary method for motor condition monitoring. MCSA utilizes the frequency signature of the supply current of an induction motor in place of the sensors. However, it has limitation since very weak signals from the load must be detected in the presence of a large ∗
Corresponding author. Tel.: +82 42 868 4340; fax: +82 42 861 1488. E-mail address: [email protected] (J.C. Jung). 0029-5493/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.nucengdes.2005.06.015
energy conversion component (Boashash and Black, 1987). In order to compensate this weakness, the following methods are also introduced; utilizing the spectral analysis of an instantaneous power (Dorrell and Thomson, 1997), observation of the motor air-gap torque (Filippetti et al., 1995), and monitoring the combination of air-gap flux, current, and vibration signals (Hsu, 1995; Jones and Baraniuk, 1994). To increase the performance of MCSA through adoption of diagnostic functions, statistical inference rule-based (Jung and Seong, 1997) or neural network (Jung et al., 2002; Kliman and Stein, 1992) models have also been presented. Fast Fourier Transform (FFT) that is used as a deterministic tool of MCSA is not sufficient either to express
58
J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
the non-stationary signals or to detect the effect of time-varying load. Consequently, the following methods are adopted; torque oscillation removal from current signal (Legowski et al., 1996), and time–frequency analysis of stator current (P/PM, 1996; Schoen et al., 1995). Most time–frequency analyses employ some kind of smoothing kernel, a window or a filter to reduce the noise and cross-components. The choice of the kernel drastically affects the appearance and quality of the resulting analyses (Schoen and Habetler, 1997). We propose the power line signal analysis method that estimates the machinery abnormality as a supplementary or alternative method for maintaining the safety goal and for satisfying the NPP cost concern. We select the reactor coolant pump (RCP) for the representative NPP machinery. Because the RCP is continuously being monitored for its performance and condition, cross-comparison of the results can easily be executed using the combination of Wigner–Ville Distribution (WVD) (Upadhyaya and McClanahan, 1998) and feature area matrix. The WVD represents the time–frequency profile of current fed to the machinery. The abnormality of RCP is diagnosed by checking if the characteristic harmonic frequencies extracted by WVD belong to the predefined featuring area. The test is performed during cool-down phase and heat-up phase in NPP. For cross-comparison of the monitoring results, the RCP vibration monitoring system (VMS) is used. In addition, using the pump internal visual inspection results, the abnormality detection result is verified.
2. RCP abnormality monitoring using WVD and feature area matrix The overall scheme of RCP abnormality monitoring method is shown in Fig. 1. The motor torque is estimated by three-phase voltage and current. In case of MCSA, the harmonic components of line frequency are modulated in the frequency spectrum of single phase current. On the other hand, torque is a function of current and voltage, the modulation effect due to harmonic components of line frequency can be eliminated. The WVD of motor torque gives some benefit to discriminate the weak characteristic harmonic components from line and its harmonic frequencies.
Fig. 1. Scheme of abnormality monitoring method using power line signal.
The torque can be expressed in terms of three-phase voltages and currents as Eq. (1) (Filippetti et al., 1995). P TAir-gap = √ (ia − ib ) · [vca − Rs (ic − ia )] dt 2 3 − (ic − ia ) · [vab − Rs (ia − ib )] dt (1) where ia,b,c , three-phase current; va,b,c , three-phase voltage; P, number of poles of the motor; Rs , stator resistance. The bilinear representation of signals in the joint time–frequency domain exhibits good property for processing non-stationary signal analysis. WVD shows the three-dimensional representation of energy density of time and frequency domain, it can be used to identify the abnormality of RCP. The WVD distribution is defined by Eq. (2) (Upadhyaya and McClanahan, 1998). ∞ τ −j2πfτ τ ∗ Wx (t, f ) = x t+ ·x t− ·e dτ 2 2 −∞ (2) where, x(t), current signal; x* (t), conjugate of x (t); f, frequency; τ, time constant. To discriminate the abnormalities, the harmonic frequency components (fh ) within 40 dB range of line frequency (fL ) are extracted as defined by Eq. (3). The discrimination level is determined by the corrective
J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
action chart that are recommended by the P/PM technology magazine (Yazici and Kliman, 1999). Among harmonic frequency components, the line frequency (60 Hz) and its integer harmonics (120 Hz, 180 Hz, . . .) are eliminated simultaneously. fL If A dB ≥ 40 dB and fh = N · fL , fh then fh = fP ,
otherwise fh = 0
(3)
where N, integer (1, 2, 3, . . .); A, amplitude at frequency (line and harmonics). The feature area (∇f1/2 ) is extracted from the frequency spectrum per sampling block using Eq. (4). ∇f1/2 =
d
(fP )j
(4)
j=c
where c, d, frequency band from peak to half attenuation. The matrix in Eq. (5) shows the feature areas that are analyzed. The column is the extracted feature area of each sampling block while the row represents the total sampling blocks ∇(f1/2 ) 11 ∇(f1/2 )12 ∇(f1/2 )13 · · · ∇(f1/2 )1k ∇(f1/2 )21 ∇(f1/2 )22 ∇(f1/2 )23 · · · ∇(f1/2 )2k Y = ∇(f1/2 )31 ∇(f1/2 )32 ∇(f1/2 )33 · · · ∇(f1/2 )3k . .. ∇(f1/2 ) ∇(f1/2 )i2 ∇(f1/2 )i3 · · · ∇(f1/2 )ik i1 The abnormality of RCP is determined by the ratio (R) of the average feature areas in measurement (Ymeasure ) to healthy state (Yhealthy ) as defined in Eq. (6). For a healthy RCP, the ratio (R) would be close to one (1). When the ratio goes beyond one, RCP may be in an abnormal state. 1 te N Ymeasure i=t k=1 (∇f1/2 )ik N R = 1 te 0 N = (6) Yhealthy m=1 (∇f1/2 )lm l=t N 0
3. Validation of the proposed method For validation of the proposed method, the crosscomparison of test results between RCP vibration monitoring system (RCPVMS) and power line signal analysis method were performed during the cool-down phase
59
and the heat-up phase. The polar plot of RCPVMS was measured in the cool-down phase. This plot is useful to confirm the weight balance of the shaft, resonance of the RCP structure, looseness or crack of the internal components. It indicates the phase and amplitude of the shaft vibration with the pump speed increase. Fig. 2 shows the impeller looseness phenomena. The increase of vibration amplitude along with shaft 1× phase shifted to the opposite direction (180◦ ) as time passed. As a result, the RCP internal components were replaced; pump seal, shaft, and impeller during overhaul outage. The power line signal was acquired from the switchgear room inside the turbine building. The threephase voltage was acquired from a secondary tap of potential transformer (PT) and the three-phase current was acquired from current transformer (CT) output to a protection relay using a hole sensor type secondary CT. This hook-up allows non-intrusive measurement and assures the non-interruption of plant operation. Fig. 3 shows the three-dimensional plot of RCP condition before and after the RCP components replace-
(5)
ment. The amplitude of the harmonic frequencies after components replacement is reduced more than 20%. This means that the pump condition is improved after maintenance. The feature vector by Eq. (5) is 512 × 512 (frequency × time) so the data generated in one measurement period is 262,144. By conditional branching logic (if-then rule) described in Eq. (3), (7450) samples (iread ) are extracted. Table 1 presents WVD analysis and feature area extraction result before and after the RCP internal components replacement. After RCP maintenance, the average feature area is decreased. The difference of average feature area between before and after RCP maintenance is greater than 27%. It is obvious that the RCP is in the healthy state due to the components
J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
60
Fig. 2. The polar plot of VMS that shows impeller looseness phenomena (before RCP component replacement).
Fig. 3. Three-dimensional plot of WVD analysis result: (a) before maintenance and (b) after maintenance.
Table 1 WVD analysis and feature area extraction result
te N
Pump condition
Numbers of feature (N)
Feature summation
Before pump maintenance After pump maintenance
Ifeature = 7450 Ifeature = 7450
sumfeature = 1926.234 sumfeature = 1227.786
i=t0
k=1
(∇f1/2 )ik
Average feature area (Ymeasure ) 1.277 1
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61
Fig. 4. Frequency spectrum of RCP motor current.
replacement and corrective action during overhauloutage. For verification of this result, FFT spectrum from 0 Hz to 100 Hz are investigated. The frequency spectrum in Fig. 4 shows the characteristic harmonic components that also indicate that the abnormality is eliminated after the RCP maintenance. In addition, the feature frequency that represents the shaft speed (1×) is also reduced. Weak characteristic harmonic frequencies appear in the frequency spectrum before RCP components replacement. These harmonic frequencies are due to the RCP impeller movement.
The polar plot that was captured after pump internal replacement does not show any symptom of abnormality (1× shift) as shown in Fig. 5. During 14th overhaul outage, the RCP internals that show the vibration characteristics of loop binding, impeller loosing and cracks on the thermal sleeve have been replaced. After the pump internals replacement, a visual inspection was performed to clarify the cause of vibration. The result shows that there are many cracks on the pump shaft keyway and thermal sleeve.
Fig. 5. Polar plot of VMS measurement after maintenance.
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J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
4. Conclusions and further study An improved method to monitor the RCP abnormality in a NPP is proposed. This method uses the power line signal analysis method in conjunction with Wigner–Ville Distribution and feature vector technique. The characteristic harmonic frequencies that are symptoms of a specific RCP abnormality were extracted using WVD technique and condition branching logic (if-then rule). By comparison of average feature area, the RCP abnormality was detected. The proposed method can be extended to distinguish the type and size of defect if diagnostic functions are added to the condition branching logic. The validation of the proposed method was done by the cross-comparison of test results between RCP vibration monitoring system (RCPVMS) and power line signal analysis method during the cool-down phase and the heat-up phase. Moreover, the visual inspection result clarifies the cause of vibration. In the case of the power line signal analysis method, the average feature area is decreased after RCP maintenance. The difference of average feature area between before and after RCP maintenance is greater than 27%. It is obvious that the RCP is in a healthy state due to the components replacement and corrective action during overhaul-outage. The RCPVMS detected the vibration characteristics of loop binding, impeller loosing and cracks on the thermal sleeve during the cool-down phase. After the pump internals replacement a symptom of abnormality (1× shift) was shown. Through the pump internal inspection, the diagnosis results using the power line signal analysis method was verified. The result shows that there are many cracks on the pump shaft keyway and thermal sleeve. This method allows monitoring the performance of a pump without any intrusive sensors. Moreover, it gives alternative or supplemental measures for trending or monitoring RCP performance during normal, abnormal and accident states. Although the test was limited to RCP, this method will be applied to test the per-
formance and to detect for the abnormality of safety related machinery in NPP. References Boashash, B., Black, P.J., November 1987. An efficient real time implementation of the Wigner–Ville Distribution. IEEE Trans. Acoust. Speech Signal Process. ASSP-35 (11). Dorrell, D.G., Thomson, W.T., Roach, S., 1997. Analysis of airgap flux, current, and vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3-phase induction motors. IEEE Trans. Ind. Appl. 33 (1). Filippetti, F., Franceschini, G., Tassoni, C., 1995. Neural networks aided on-line diagnostics of induction motor rotor faults. IEEE Trans. Ind. Appl. 31 (4). Hsu, J.S., 1995. Monitoring of defects in induction motors through air-gap torque observation. IEEE Trans. Ind. Appl. 31 (5). Jones, D.L., Baraniuk, R.G., December 1994. A simple scheme for adapting time–frequency representations. IEEE Trans. Signal Process. 42 (12). Jung, J.C., Seong, P.H., 1997. Development of an induction motor abnormality monitoring system (IMAMS) using power line signal analysis. In: Maintenance and Reliability Conference Proceedings, Knoxville, Tennessee, May. Jung, J.C., Chang, Y.W., Seong, P.H., 2002. Application of power line signal analysis method to the monitoring of safety related machinery in nuclear power plant. In: American Nuclear Society Winter Meeting, November. Kliman, G.B., Stein, J., 1992. Methods of motor current signature analysis. Electric Mach. Power Syst. 20, 463–474. Legowski, S.F., Sadrul Ula, A.H.M., Trzynadlowski, A.M., 1996. Instantaneous power as a medium for the signature analysis of induction motors. IEEE Trans. Ind. Appl. 32 (4). P/PM, Technology Magazine, Machine Condition Monitoring, June 1996. Schoen, Randy R., Lin, B.K., Habetler, Thomas G., Schlag, J.H., Farag, S., 1995. Unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Trans. Ind. Appl. 31 (6). Schoen, R.R., Habetler, T.G., 1997. Evaluation and implementation of a system to elimination arbitrary load effects in current-based monitoring of induction machines. IEEE Trans. Ind. Appl. 33 (6). Upadhyaya, B.R., Patrick McClanahan, J., Monitoring electric aging using vibration and current signatures, UTNE/DUKE/97-04. The University of Tennessee, September 1998. Yazici, B., Kliman, G.B., 1999. An adaptive statistical time–frequency method for detection of broken bars and bearing faults in motors using stator current. IEEE Trans. Ind. Appl. 35 (2).
An improved method for reactor coolant pump abnormality monitoring using power line signal analysis Jae Cheon Jung ∗ , Poong Hyun Seong Korea Power Engineering Company, Korea Advanced Institute of Science and Technology, 150 deokjin-dong, Yuseong-ku, Daejeon, Republic of Korea Received 25 June 2004; received in revised form 19 June 2005; accepted 19 June 2005
Abstract An improved method to detect the reactor coolant pump (RCP) abnormality is suggested in this work. The monitoring parameters that are acquired from power line signal analysis are motor torque, motor speed and characteristic harmonic frequencies. The combination of Wigner–Ville Distribution (WVD) and feature area matrix comparison method is used for abnormality diagnosis. For validation of the proposed method, the test was performed during cool-down phase and heat-up phase in nuclear power plant (NPP) by cross-comparison with RCP vibration monitoring system (VMS). Using pump internal inspection results, the diagnosis prediction is verified. © 2005 Elsevier B.V. All rights reserved.
1. Introduction Recently, a non-intrusive, sensorless monitoring technique has been introduced in NPP for abnormality monitoring of motor driven rotating machinery. The motor current signature analysis (MCSA) has been developed and commercialized as an alternative or supplementary method for motor condition monitoring. MCSA utilizes the frequency signature of the supply current of an induction motor in place of the sensors. However, it has limitation since very weak signals from the load must be detected in the presence of a large ∗
Corresponding author. Tel.: +82 42 868 4340; fax: +82 42 861 1488. E-mail address: [email protected] (J.C. Jung). 0029-5493/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.nucengdes.2005.06.015
energy conversion component (Boashash and Black, 1987). In order to compensate this weakness, the following methods are also introduced; utilizing the spectral analysis of an instantaneous power (Dorrell and Thomson, 1997), observation of the motor air-gap torque (Filippetti et al., 1995), and monitoring the combination of air-gap flux, current, and vibration signals (Hsu, 1995; Jones and Baraniuk, 1994). To increase the performance of MCSA through adoption of diagnostic functions, statistical inference rule-based (Jung and Seong, 1997) or neural network (Jung et al., 2002; Kliman and Stein, 1992) models have also been presented. Fast Fourier Transform (FFT) that is used as a deterministic tool of MCSA is not sufficient either to express
58
J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
the non-stationary signals or to detect the effect of time-varying load. Consequently, the following methods are adopted; torque oscillation removal from current signal (Legowski et al., 1996), and time–frequency analysis of stator current (P/PM, 1996; Schoen et al., 1995). Most time–frequency analyses employ some kind of smoothing kernel, a window or a filter to reduce the noise and cross-components. The choice of the kernel drastically affects the appearance and quality of the resulting analyses (Schoen and Habetler, 1997). We propose the power line signal analysis method that estimates the machinery abnormality as a supplementary or alternative method for maintaining the safety goal and for satisfying the NPP cost concern. We select the reactor coolant pump (RCP) for the representative NPP machinery. Because the RCP is continuously being monitored for its performance and condition, cross-comparison of the results can easily be executed using the combination of Wigner–Ville Distribution (WVD) (Upadhyaya and McClanahan, 1998) and feature area matrix. The WVD represents the time–frequency profile of current fed to the machinery. The abnormality of RCP is diagnosed by checking if the characteristic harmonic frequencies extracted by WVD belong to the predefined featuring area. The test is performed during cool-down phase and heat-up phase in NPP. For cross-comparison of the monitoring results, the RCP vibration monitoring system (VMS) is used. In addition, using the pump internal visual inspection results, the abnormality detection result is verified.
2. RCP abnormality monitoring using WVD and feature area matrix The overall scheme of RCP abnormality monitoring method is shown in Fig. 1. The motor torque is estimated by three-phase voltage and current. In case of MCSA, the harmonic components of line frequency are modulated in the frequency spectrum of single phase current. On the other hand, torque is a function of current and voltage, the modulation effect due to harmonic components of line frequency can be eliminated. The WVD of motor torque gives some benefit to discriminate the weak characteristic harmonic components from line and its harmonic frequencies.
Fig. 1. Scheme of abnormality monitoring method using power line signal.
The torque can be expressed in terms of three-phase voltages and currents as Eq. (1) (Filippetti et al., 1995). P TAir-gap = √ (ia − ib ) · [vca − Rs (ic − ia )] dt 2 3 − (ic − ia ) · [vab − Rs (ia − ib )] dt (1) where ia,b,c , three-phase current; va,b,c , three-phase voltage; P, number of poles of the motor; Rs , stator resistance. The bilinear representation of signals in the joint time–frequency domain exhibits good property for processing non-stationary signal analysis. WVD shows the three-dimensional representation of energy density of time and frequency domain, it can be used to identify the abnormality of RCP. The WVD distribution is defined by Eq. (2) (Upadhyaya and McClanahan, 1998). ∞ τ −j2πfτ τ ∗ Wx (t, f ) = x t+ ·x t− ·e dτ 2 2 −∞ (2) where, x(t), current signal; x* (t), conjugate of x (t); f, frequency; τ, time constant. To discriminate the abnormalities, the harmonic frequency components (fh ) within 40 dB range of line frequency (fL ) are extracted as defined by Eq. (3). The discrimination level is determined by the corrective
J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
action chart that are recommended by the P/PM technology magazine (Yazici and Kliman, 1999). Among harmonic frequency components, the line frequency (60 Hz) and its integer harmonics (120 Hz, 180 Hz, . . .) are eliminated simultaneously. fL If A dB ≥ 40 dB and fh = N · fL , fh then fh = fP ,
otherwise fh = 0
(3)
where N, integer (1, 2, 3, . . .); A, amplitude at frequency (line and harmonics). The feature area (∇f1/2 ) is extracted from the frequency spectrum per sampling block using Eq. (4). ∇f1/2 =
d
(fP )j
(4)
j=c
where c, d, frequency band from peak to half attenuation. The matrix in Eq. (5) shows the feature areas that are analyzed. The column is the extracted feature area of each sampling block while the row represents the total sampling blocks ∇(f1/2 ) 11 ∇(f1/2 )12 ∇(f1/2 )13 · · · ∇(f1/2 )1k ∇(f1/2 )21 ∇(f1/2 )22 ∇(f1/2 )23 · · · ∇(f1/2 )2k Y = ∇(f1/2 )31 ∇(f1/2 )32 ∇(f1/2 )33 · · · ∇(f1/2 )3k . .. ∇(f1/2 ) ∇(f1/2 )i2 ∇(f1/2 )i3 · · · ∇(f1/2 )ik i1 The abnormality of RCP is determined by the ratio (R) of the average feature areas in measurement (Ymeasure ) to healthy state (Yhealthy ) as defined in Eq. (6). For a healthy RCP, the ratio (R) would be close to one (1). When the ratio goes beyond one, RCP may be in an abnormal state. 1 te N Ymeasure i=t k=1 (∇f1/2 )ik N R = 1 te 0 N = (6) Yhealthy m=1 (∇f1/2 )lm l=t N 0
3. Validation of the proposed method For validation of the proposed method, the crosscomparison of test results between RCP vibration monitoring system (RCPVMS) and power line signal analysis method were performed during the cool-down phase
59
and the heat-up phase. The polar plot of RCPVMS was measured in the cool-down phase. This plot is useful to confirm the weight balance of the shaft, resonance of the RCP structure, looseness or crack of the internal components. It indicates the phase and amplitude of the shaft vibration with the pump speed increase. Fig. 2 shows the impeller looseness phenomena. The increase of vibration amplitude along with shaft 1× phase shifted to the opposite direction (180◦ ) as time passed. As a result, the RCP internal components were replaced; pump seal, shaft, and impeller during overhaul outage. The power line signal was acquired from the switchgear room inside the turbine building. The threephase voltage was acquired from a secondary tap of potential transformer (PT) and the three-phase current was acquired from current transformer (CT) output to a protection relay using a hole sensor type secondary CT. This hook-up allows non-intrusive measurement and assures the non-interruption of plant operation. Fig. 3 shows the three-dimensional plot of RCP condition before and after the RCP components replace-
(5)
ment. The amplitude of the harmonic frequencies after components replacement is reduced more than 20%. This means that the pump condition is improved after maintenance. The feature vector by Eq. (5) is 512 × 512 (frequency × time) so the data generated in one measurement period is 262,144. By conditional branching logic (if-then rule) described in Eq. (3), (7450) samples (iread ) are extracted. Table 1 presents WVD analysis and feature area extraction result before and after the RCP internal components replacement. After RCP maintenance, the average feature area is decreased. The difference of average feature area between before and after RCP maintenance is greater than 27%. It is obvious that the RCP is in the healthy state due to the components
J.C. Jung, P.H. Seong / Nuclear Engineering and Design 236 (2006) 57–62
60
Fig. 2. The polar plot of VMS that shows impeller looseness phenomena (before RCP component replacement).
Fig. 3. Three-dimensional plot of WVD analysis result: (a) before maintenance and (b) after maintenance.
Table 1 WVD analysis and feature area extraction result
te N
Pump condition
Numbers of feature (N)
Feature summation
Before pump maintenance After pump maintenance
Ifeature = 7450 Ifeature = 7450
sumfeature = 1926.234 sumfeature = 1227.786
i=t0
k=1
(∇f1/2 )ik
Average feature area (Ymeasure ) 1.277 1
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61
Fig. 4. Frequency spectrum of RCP motor current.
replacement and corrective action during overhauloutage. For verification of this result, FFT spectrum from 0 Hz to 100 Hz are investigated. The frequency spectrum in Fig. 4 shows the characteristic harmonic components that also indicate that the abnormality is eliminated after the RCP maintenance. In addition, the feature frequency that represents the shaft speed (1×) is also reduced. Weak characteristic harmonic frequencies appear in the frequency spectrum before RCP components replacement. These harmonic frequencies are due to the RCP impeller movement.
The polar plot that was captured after pump internal replacement does not show any symptom of abnormality (1× shift) as shown in Fig. 5. During 14th overhaul outage, the RCP internals that show the vibration characteristics of loop binding, impeller loosing and cracks on the thermal sleeve have been replaced. After the pump internals replacement, a visual inspection was performed to clarify the cause of vibration. The result shows that there are many cracks on the pump shaft keyway and thermal sleeve.
Fig. 5. Polar plot of VMS measurement after maintenance.
62
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4. Conclusions and further study An improved method to monitor the RCP abnormality in a NPP is proposed. This method uses the power line signal analysis method in conjunction with Wigner–Ville Distribution and feature vector technique. The characteristic harmonic frequencies that are symptoms of a specific RCP abnormality were extracted using WVD technique and condition branching logic (if-then rule). By comparison of average feature area, the RCP abnormality was detected. The proposed method can be extended to distinguish the type and size of defect if diagnostic functions are added to the condition branching logic. The validation of the proposed method was done by the cross-comparison of test results between RCP vibration monitoring system (RCPVMS) and power line signal analysis method during the cool-down phase and the heat-up phase. Moreover, the visual inspection result clarifies the cause of vibration. In the case of the power line signal analysis method, the average feature area is decreased after RCP maintenance. The difference of average feature area between before and after RCP maintenance is greater than 27%. It is obvious that the RCP is in a healthy state due to the components replacement and corrective action during overhaul-outage. The RCPVMS detected the vibration characteristics of loop binding, impeller loosing and cracks on the thermal sleeve during the cool-down phase. After the pump internals replacement a symptom of abnormality (1× shift) was shown. Through the pump internal inspection, the diagnosis results using the power line signal analysis method was verified. The result shows that there are many cracks on the pump shaft keyway and thermal sleeve. This method allows monitoring the performance of a pump without any intrusive sensors. Moreover, it gives alternative or supplemental measures for trending or monitoring RCP performance during normal, abnormal and accident states. Although the test was limited to RCP, this method will be applied to test the per-
formance and to detect for the abnormality of safety related machinery in NPP. References Boashash, B., Black, P.J., November 1987. An efficient real time implementation of the Wigner–Ville Distribution. IEEE Trans. Acoust. Speech Signal Process. ASSP-35 (11). Dorrell, D.G., Thomson, W.T., Roach, S., 1997. Analysis of airgap flux, current, and vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3-phase induction motors. IEEE Trans. Ind. Appl. 33 (1). Filippetti, F., Franceschini, G., Tassoni, C., 1995. Neural networks aided on-line diagnostics of induction motor rotor faults. IEEE Trans. Ind. Appl. 31 (4). Hsu, J.S., 1995. Monitoring of defects in induction motors through air-gap torque observation. IEEE Trans. Ind. Appl. 31 (5). Jones, D.L., Baraniuk, R.G., December 1994. A simple scheme for adapting time–frequency representations. IEEE Trans. Signal Process. 42 (12). Jung, J.C., Seong, P.H., 1997. Development of an induction motor abnormality monitoring system (IMAMS) using power line signal analysis. In: Maintenance and Reliability Conference Proceedings, Knoxville, Tennessee, May. Jung, J.C., Chang, Y.W., Seong, P.H., 2002. Application of power line signal analysis method to the monitoring of safety related machinery in nuclear power plant. In: American Nuclear Society Winter Meeting, November. Kliman, G.B., Stein, J., 1992. Methods of motor current signature analysis. Electric Mach. Power Syst. 20, 463–474. Legowski, S.F., Sadrul Ula, A.H.M., Trzynadlowski, A.M., 1996. Instantaneous power as a medium for the signature analysis of induction motors. IEEE Trans. Ind. Appl. 32 (4). P/PM, Technology Magazine, Machine Condition Monitoring, June 1996. Schoen, Randy R., Lin, B.K., Habetler, Thomas G., Schlag, J.H., Farag, S., 1995. Unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Trans. Ind. Appl. 31 (6). Schoen, R.R., Habetler, T.G., 1997. Evaluation and implementation of a system to elimination arbitrary load effects in current-based monitoring of induction machines. IEEE Trans. Ind. Appl. 33 (6). Upadhyaya, B.R., Patrick McClanahan, J., Monitoring electric aging using vibration and current signatures, UTNE/DUKE/97-04. The University of Tennessee, September 1998. Yazici, B., Kliman, G.B., 1999. An adaptive statistical time–frequency method for detection of broken bars and bearing faults in motors using stator current. IEEE Trans. Ind. Appl. 35 (2).