the advanced loose parts monitoring system (alps) and wavelet analysis
TRANSCRIPT
Int. J. Nuclear Energy Science and Technology, Vol. 2, No. 3, 2006 241
Copyright © 2006 Inderscience Enterprises Ltd.
The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis
Gergő Pokol* and Gabor Por Department of Nuclear Technology Budapest University of Technology and Economics H-1111 Műegyetem rkp. 9., Budapest, Hungary APANDSD Ltd, H-1511 P.O. Box 25, Budapest, Hungary E-mail: [email protected] E-mail: [email protected] *Corresponding author
Abstract: The Advanced Loose Parts monitoring System (ALPS), is installed in each Unit of Paks NPP. Its characteristics and some interesting results are presented. Wavelet analysis is being introduced to data evaluation techniques. The short-time Fourier transform and the continuous wavelet transform techniques have been used to present the time signal in a time-frequency and time-scale plane. Characteristic frequencies of the physical acoustic system and the growing frequencies of spectrum components during the start-up of the main coolant pumps are clearly seen on those pictures. The newly applied wavelet coherence promises to find new oscillation in the pair of signals, which remain hidden in time-dependent autospectra.
Keywords: loose parts detection; continuous wavelet transform; scalogram; wavelet coherence.
Reference to this paper should be made as follows: Pokol, G. and Por, G. (2006) ‘The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis’, Int. J. Nuclear Energy Science and Technology, Vol. 2, No. 3, pp.241–252.
Biographical notes: Gergő Pokol (MSc in Engineering Physics, Budapest University of Technology and Economics, Hungary, 2004) is a PhD student at the Department of Nuclear Technology, Budapest University of Technology and Economics, Hungary. His research interests include noise diagnostics, signal processing and data analysis, methods based on continuous transforms, fusion plasma physics, anomalous transport and transient MHD modes.
Gabor Por graduated (MSc) in Solid State Physics and in Computation Mathematics (BSc) from St. Petersburg, State University, in 1972. He obtained his PhD in Nuclear Physics at R. Eotvos University Budapest. He was a Candidate for Doctor of Sciences of the Hungarian Academy of Sciences in 1986. He obtained an MSc in Industrial Politics in 1989. He obtained a PhD from R. Eotvos University Budapest for diagnostic studies in Nuclear Power Plants. He was a scientific co-worker of KFKI- AEKI Budapest (1972–1992); a Project Leader of the diagnostic system for nuclear power plants (1988–1992); and Head of the Department of the Training Reactor of Budapest University of Technology and Economics (1992–1995). He has been Executive Director of APandSD Ltd since 1993; Associate Professor of Institute of Nuclear Techniques of BUTE since 1994; and Professor of the College of Dunaujvaros since 2001.
242 G. Pokol and G. Por
1 Introduction
Loose Part Monitoring Systems (LPMS) are in use in almost every Pressurised Water Reactor (PWR) type Nuclear Power Plant (NPP), since it is requested by both US (US Nuclear Regulatory Commission (NRC), American Society of Mechanical Engineers (ASME)) and European (Deutsches Institut für Normung e.V. (DIN), International Engineering Consortium (IEC)) regulations and standards. The first version of a new generation of LPMS was tested in Paks NPP in 1991–1992 under the acronym HELPS (Hungarian Expert Loose Parts System) and its basic principles and experiences have been published in several papers (Szappanos et al., 1997; 1999, Szappanos and Por, 1999). Based on the results of HELPS, a new version of LPMS has been elaborated and named as Advanced Loose Parts Monitoring System (ALPS). Its principles and some results with this system have also been published, partly during the last Specialist Meeting On Reactor Noise (SMORN-8) (Por et al., 2003). This new version is a fully matured commercial LPMS, of which four have been sold for Paks NPP (one for each unit), and they have been in operation for more than three years now.
In the first part of this paper we give a very short overview of the system components and principles, since details can be found in the abovementioned publications. The second section deals with the presentation of a few new events recorded in the last two years. However, the main emphasis in this paper is given to the introduction of the results of wavelet analysis. The ALPS has originally been equipped with several manual packages to assist the user: it can estimate all four moments, FFT and AR spectra, Sequential Probability Ratio Test (SPRT), etc.; it is also possible to listen to signals and to print it (visualisation with zoom and shifts). However, wavelet analysis was not part of its original software. Here we present, from the first results of wavelet analysis, some interesting events from the latest records.
2 Basic parts of ALPS
ALPS in Paks NPP consists of four measuring PCs, which are connected to a common data evaluation PC. The last also hosts the WEB-server of ALPS, which is capable of disseminating the results of analysis via the intranet of the NPP. In fact, the intranet was used to connect the measuring PCs as well. Data sampling is going on continuously, collecting data into a ring buffer in measuring PCs from maximum 32 measuring channels: from accelerometers mounted on each hot leg closing valve (denoted as M1…M6), on steam generators (G1…G6), on main coolant pumps (F1…F6), on safety rod moving mechanisms (S1…S6), on the flange of reactor vessel (R1…R4) and from some pressure fluctuation sensors (reactor inlet and outlet and pressuriser). This set of signals has proven its ability to notice almost all acoustic events in and around the primary circuit.
A parallel programme tests the content of the ring buffer, with high sensitivity for the first four moments and by a fast SPRT algorithm. If any deviation from the normal behaviour is observed, data (of selected length, typically three seconds) are saved on a hard disk.
The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis 243
The main data-evaluating PC checks the four measuring PCs regularly (typically every three minutes), and if it finds any record in the given sectors of its hard disk, it downloads it for further analysis. Further analysis includes a more detailed and more tuned event analysis based mainly on SPRT, but also on spectrum estimation, Auto Regression (AR) modelling and construction of a heat map, which enables the user to find the time block, where the loose part event takes place.
The expert part of ALPS compares the estimated parameters of the new event with the parameter sets of the achieved events. It finds the nearest neighbours using the well-known nearest neighbourhood’s method. The user may accept the suggestion or discard it.
An interesting and very important new feature of ALPS is the automatic recording mode during the initial start-up of the main coolant pumps. Typically we take a 60 second-long record, triggered by the first rotation of any of the main coolant pumps. In this paper we mainly demonstrate such records, since the most interesting occurrences can typically be found during the start-up period.
3 Demonstration of a few new events recorded by the system
The playback of records of the starting period of the main coolant pumps are very informative, since the listener can directly hear the speeding up of the main coolant pump, as if he/she were present there. In Figure 1, we demonstrate an absolutely clean start of a main pump, recorded by the accelerometer mounted on it.
Figure 1 Nice, clean start-up of the main coolant pump loop 5
However, in some cases small metallic parts are resting in the main coolant pipelines, which are picked up by the stream and carried away until they are either disintegrated or stuck in some traps. Figure 2 shows a very noisy start-up, which we shall analyse in detail by wavelet methods.
244 G. Pokol and G. Por
Figure 2 A very noisy start-up of pump loop 6
We note that the start-up of the pumps can be ‘heard’ by far away G and M detectors as well. (Figure 3 shows the same start-up as Figure 2). Naturally, the sound of small parts, if there were any at all, would arrive later to those sensors.
Figure 3 The start-up of loop six detected on steam generator and main closing valve
Finally, it is very important to underline that ALPS not only serves as a loose-parts monitoring tool, but it also catches other acoustic events more and more: the vibration of the blades of closing valves, motion noises of regulating rods, waterfall from the upper block of the reactor during water discharge, abnormal vibration of surgeon water supply pipelines, etc. As an example, in Figure 4, we present a typical noise of moving regulating rods, confirmed by tests.
The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis 245
Figure 4 Normal motion sound of regulating rod
4 Wavelet analysis
4.1 Few basic formulas applied in the given data analysis
When introducing wavelet analysis into the tools of the ALPS system, we decided to use continuous linear time-frequency transforms. There are many arguments for this selection, the two most important ones being the time-shift invariance and the absence of interference patterns between signal components. Perhaps the most important compromise that had to be made was that the time-frequency resolution of the transforms was limited by Heisenberg’s uncertainty relation, and it had to be predetermined by setting the transform parameters.
Linear time-frequency transforms can be calculated by taking the inner product of the signal and families of time-frequency atoms. A time-frequency atom is a function, the energy of which is well localised in both time and frequency. The families have to be generated in such a way that they cover the whole time-frequency plane. Heisenberg’s uncertainty relation gives a lower bound to the extent of the atoms. It also states that the resolution of the transform is optimal for the Gabor-chirp atom:
2
( ) ict btf t ae e−=
The classical tool for continuous linear time-frequency analysis is the short-time Fourier transform (STFT), which was introduced into signal analysis by Gabor (1946). Here the time-frequency atom family is produced by continuously shifting the atoms in time (by u) and in frequency (by ζ ):
, ( )i tug e g t uξξ = − ,
where: || || 1g = .
Hence the transform is calculated as the following inner product:
246 G. Pokol and G. Por
,( , ) , ( ) ( ) i tuSf u f g f t g t u e dtξξξ
+∞−
−∞
=< >= −∫ .
Being a linear transform, STFT conserves the total signal energy, thus a time-frequency energy-distribution called spectrogram can be defined the following way:
2( , ) | ( , ) |SP f u Sf uξ ξ= .
The other basic tool used in our analysis is the continuous wavelet transform (CWT) introduced by Morlet in 1983. Here the time-frequency atom family is generated by shifting a mother wavelet (ψ) in time (by u) and dilating (by s),
,
1u s
t u
ss
−⎛ ⎞Ψ = Ψ⎜ ⎟⎝ ⎠
where || || 1Ψ = . The transform is calculated just like the STFT:
,
1( , ) , ( )u s
t uWf u s f f t dt
ss
+∞∗
−∞
−⎛ ⎞=< Ψ >= Ψ ⎜ ⎟⎝ ⎠∫
The time-frequency energy-distribution defined by CWT is called scalogram:
2( , ) | ( , ) |WP f u s Wf u s= .
In both transforms we have used Gabor-chirps as time-frequency atoms, which means Gauss-window for the g(t) window function of STFT and Morlet-wavelet for CWT. The most important difference between the STFT and the CWT is their time-frequency resolution. While the STFT has a uniform resolution over the whole time-frequency plane, the resolution of the CWT varies with frequency. In the case of CWT, on higher frequencies we have better time resolution, and in return, worse frequency resolution. This property of the CWT resembles our usual definitions of frequency: there has to be a given minimum number of oscillations to state that the signal has a given frequency. In the CWT, that number of oscillations in the mother wavelet can be set as a transform parameter. An overview of these transforms can be found in Mertins (1999) and Mallat (2001).
4.2 Actual results of wavelet analysis of recorded signals
Typically it is difficult to calculate spectrograms with continuous transforms owing to large computation time and memory request. We have developed an easily adjustable effective algorithm for calculating the spectrogram by reducing its resolution in both time and frequency, yet still preserving a reasonable time-shift and frequency-shift invariance. This way we can calculate the spectrogram for the whole time span of a 60 second-long start-up signal.
We can use this spectrogram to visualise the frequency changes, and get a general view of the signal. In Figure 5, we show a time signal and the spectrogram of a clean start-up of the main coolant pump. In the first few seconds, one can see the rise in characteristic frequencies during the start-up. Later the sound level decreases significantly, and the background noise is distributed into some wider (e.g., around 1.8 kHz) and narrower (e.g., at 1.3 kHz) preferred frequency bands. It is interesting that one can observe similar frequency bands in case of a metal impact or hammering as well. This band structure is probably connected to the acoustic transfer function of the physical system.
The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis 247
Figure 5 Clean start-up of the main coolant pump: a) spectrogram and b) raw signal
Using a spectrogram, we can easily see from the stripes of the frequency bands that we have good contact with the acoustic system, and from the light dots, if there are impacts of loose parts. In case of any electrical failure, audible white noise is produced. We have had a case when an overdriven ADC produced a white noise signal. In that case the spectrogram did not show the frequency band structure; the energy of the signal was evenly distributed in both time and frequency.
Besides giving an overall view of our signal, the spectrogram can be used to localise possible major events for further analysis. We can zoom in on these individual events using the scalogram calculated by CWT.
In Figure 6a we see the scalogram of an impact event on the time-frequency plane, which is a more conventional visualisation compared to the time-scale parameter plane. The changing of resolution with frequency can easily be observed. However, the natural way of plotting a scalogram is on the time-scale parameter plane, where the scale parameter is plotted on a logarithmic scale. Since it fits the natural resolution of CWT better, such a scalogram is able to present the changes of the different signal components over several orders of magnitude of frequency with optimal time resolution on each frequency, which is the most important advantage of the CWT over STFT (Figure 6b).
248 G. Pokol and G. Por
Figure 6 Typical metallic impact: a) scalogram on time-frequency plane b) scalogram on time-scale plane and c) raw signal
The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis 249
One can reveal the abovementioned overall frequency band structure before the impact event in Figure 6. When the event takes place the general RMS of the signal rises, and thus, all existing frequency components are also intensified (see frequency bands around 7.4 kHz, 5.3 kHz and 1.8 kHz, marked in Figures 6a and b). However, a few new frequencies are also generated by the impact (see at 4.5 kHz, 830 Hz and 180 Hz). If we use the advantage of the time-scale plane (Figure 6b), the low frequency components might be more visible. Here one can see clearly the appearance of the 180 Hz component, as well as some other new, weak components: one between 180 –830 Hz and another between 830 –1800 Hz. We believe those are important since they belong to the given impact; therefore we store them among the classification parameters.
4.3 Wavelet-based coherence
An interesting new tool based on CWT is the wavelet coherence technique. We calculate the wavelet coherence in the usual way (van Milligen et al. (1995) analogously to the traditional coherence based on FFT in the following four steps:
1 Calculating CWT with analytical wavelets for two signals:
Wf(u, s), and Wg(u, s)
2 WAPSD (scalogram) and WCPSD calculation (analogous to APSD and CPSD): *
,
*
*
( , ) ( , ) ( , )
( , ) ( , ) ( , )
( , ) ( , ) ( , )
f g
f
g
WCPSD u s Wf u s Wg u s
WAPSD u s Wf u s Wf u s
WAPSD u s Wg u s Wg u s
=
=
=
3 WAPSD and WCPSD averaging done by smoothing:
2
2
2
2
2
2
, ,
1( , ) ( , )
1( , ) ( , )
1( , ) ( , )
T
T
T
T
T
T
t
f g f g
t
t
f f
t
t
g g
t
WCPSD t s WCPSD u s duT
WAPSD t s WAPSD u s duT
WAPSD t s WAPSD u s duT
+
−
+
−
+
−
=
=
=
∫
∫
∫
4 Coherence calculation:
,
,
( , )( , )
( , ) ( , )
f g
f g
f g
WCPSD t sWCOH t s
WAPSD t s WAPSD t s=
This way we get a time- and frequency-dependent coherence value, which holds the time-shift invariance property of the CWT.
In Figure 7, we show the wavelet coherence of the first three seconds of the sound during the start-up of a main coolant pump as detected by the sensors on the hot leg isolating valve (M3) and on the steam generator (G3) of the same loop N3. The scalogram of the M3 signal on Figure 7a and the scalogram of the G3 signal on Figure 7c
250 G. Pokol and G. Por
are quite similar: we have fixed frequencies, like the mains frequency 50 Hz and several rising frequencies originating from the accelerating pump (15–35 Hz, 830–1260 Hz and 1300–2250 Hz).
Figure 7 Start-up of main coolant pump: a) scalogram of M3 signal b) M3 raw signal c) scalogram of G3 signal d) G3 raw signal and e) wavelet coherence of M3 and G3 signals
Since the averaging was done with T = 0.25 sec, all (rather high) coherences below 50 Hz cannot be considered realistic, since here the time resolution of CWT is larger than or comparable to the integration time for averaging, which leads to high values and high uncertainties in coherence.
In the well-determined part of coherence, not only the fixed 50 Hz line frequency shows high coherence, but the fixed 7500 Hz as well. It is worth comparing that with the small value in WAPSD. This later one can probably be identified as the sound of large rotating machinery housed in the containment.
The Advanced Loose Parts Monitoring System (ALPS) and wavelet analysis 251
More interesting is the coherence in the frequency range of 140 Hz and 7400 Hz with rising frequencies. Note that the oscillation of the coherence is quite different from that of WAPSD of the two signals.
5 Conclusion
In this paper, the most important features of the ALPS monitoring system were presented. That system has now worked for more than three years on all units of Paks NPP. We are introducing wavelet methods as new tools for data analysis.
It has been demonstrated that the STFT-based spectrogram is capable of localising an event, even in long records. It follows the changes in frequency of spectrum components due to the accelerating pump. In the background, well-defined permanent frequency bands were found, which we attribute to acoustic transfer properties of the physical system itself. Thus the spectrogram both characterises the wellness of the system and visualises the events.
It has also been shown that CWT-based scalograms contain well-defined frequency bands both in the background and after the metal impact. We found that in the case of scalograms the time-scale plane was sometimes more adequate to present the results than the time-frequency plane. In order to understand the structure presented, a simulation similar to that presented by (Park et al., 2004) could be suitable.
Even without a detailed analysis involving simulations, we can use scalograms to classify the different types of bursts seen in our signals. After identifying the fingerprints of different kinds of events, we will probably be able to find a more effective way of acquiring the information from the signal using a series of narrow-band bandpass filters, together with coincidence techniques. This way the results of the analysis originally done using scalograms can be implemented into real-time signal monitoring.
We have successfully used a very interesting and very promising visualisation and analysing tool, called wavelet coherence, for a pair of signals. It has been demonstrated that wavelet coherence can reveal other time structures within the signals than the scalograms. This gives us hope that, with better averaging and with the introduction of the more suitable bicoherence method, we can identify the nature of the recognised undulation in our coherence picture.
References
Gabor, D. (1946) ‘Theory of communications’, Journal of the Institute of Electric Engineers, Vol. 93, pp.429–457.
Mallat, S. (2001) A Wavelet Tour of Signal Processing, Academic Press, 2nd edition.
Mertins, A. (1999) Signal Analysis, John Wiley and Sons Ltd.
van Milligen, B.Ph., Sánchez, E., Estrada, T., Hidalgo, C., Branas, B., Carreras, B. and García, L. (1995) ‘Wavelet bicoherence: a new turbulence analysis tool’, Physics of Plasmas, August, Vol. 2. No. 8, p.3017.
Morlet, J. (1983) Issues in Acoustic Signal – Image Processing and Recognition, Berlin: Springler – Verlag, pp.2003–2262.
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Park, J-H., Lee, J-H., Park, G-Y. and Jung, C-G. (2004) ‘Application of continuous wavelet transform method to the location estimation in the Loose Parts Monitoring System – (LPMS)’, Proc. IMORN-29, Budapest 17–22 May.
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Szappanos, G., Feher, A, Lorincz, J., Nemes, L., Por, G., Csikos, T., Glodi, O., Lipcsei, S. and Tri, T.D. (1997) ‘A new digital expert loose part detection system’, Annals in Nuclear Energy, Vol. 24, No. 14, pp.1097–1103.
Szappanos, G., Kiss, J.J., Por, G., Kiss, J.M. (1999) ‘Analysis of measurements made by HELPS loose part detection system during installation and operation periods’, Progress in Nuclear Energy, Vol. 34, pp.185–193.
Szappanos, G., Por, G. (1999) ‘Basic ideas and realization of completely digitised loose part detection system HELPS’, Progress in Nuclear Energy, Vol. 34, No. 3, pp.195–201.