monitoring of hot pipes at the power plant neurath using … · 2014-05-28 · monitoring of hot...

293

Chapter 14-x

Authors:Bianca WeihnachtThomas KlesseRobert NeubeckLars Schubert

Weihnacht, B., Klesse, T., Neubeck, R. and Schubert, L., 2013. Monitoring of Hot Pipes at the Power Plant Neurath Using Guided Waves. Conference Proceedings SPIE Smart Struc-tures and Materials & Nondestructive Evaluation and Health Monitoring, San Diego, March 11–14, 2013.

14Monitoring of Hot Pipes at the Power Plant Neurath Using Guided Waves

MotivationThe increase of operating temperatures of power plants up to 720 °C requires novel

high performance steels in the piping systems. This goes along with higher risks of dam-age and instability. A monitoring system to measure the underlying effects is desired.

Main ResultsAll necessary components of a monitoring system to be operated at hot pipes have

been developed and the application has been tested in the field at a bypass pipe under real operating conditions.

294

14 Monitoring of Hot Pipes at the Power Plant Neurath Using Guided Waves

14-1 Introduction In order to reduce the CO2-emissions and to increase the energy efficiency in power

plants, the steam parameters are increased. The commonly used live steam temperatures of about 600 °C are to be increased beyond 700 °C in order to increase the efficiency of the entire plant over 50 %. For such high operating temperatures novel high-performance steels like P 91 and P 92 must be applied for the piping systems. The material Life Cycle and especially the creep rupture properties of these novel high-performance steels, however, are not yet sufficiently known so that periodical inspections of such plants are not suffi-cient, due to reasons of safety. Particularly piping systems made of these materials which are exposed to high internal pressure must be subject to integral monitoring.

Higher temperatures lead to a higher risk of damaging and have a direct impact on the structure stability and the deposition structure which have a large influence on the long-term stability under load and the damage behaviour. Adequately trusted results for the prediction of the residual service life of those high strength steels are hard to achieve. This causes security problems that avoid the application of those steels or demand for ear-ly exchange of the pipe components. To overcome these problems the implementation of an online monitoring system is needed in addition to periodic inspection. The system should enable the characterization and evaluation of the actual state of the material. Only with an accurate prediction of the residual life time of the components, the safety and the availability and resulting from that the efficiency of power plants can be guaranteed.

The monitoring of modern combined heat and power plants is currently focused on the age consumption which is determined by the probabilistic calculation from real load data and in the advanced stage by additional material investigation during shut-off times. The live-time reserves of the materials can be used more efficiently by applying monitor-ing systems. This leads to a more efficient and furthermore safer operation of the power plant.

High resolution NDT-methods based on ultrasound methods or X-ray are state of the art for periodic inspections in order to detect cracks and wall thickness reductions by per-pendicularly introduced ultra-sound. The clear disadvantage lies in the fact that only local condition information of the specimen (local wall thickness, local cracks) can be obtained. While using only NDT-methods – the testing of hundreds of metres of pipes would be very expensive. Additionally, the power plant has to be shut down during the measurement.

It has to be stated that the existing monitoring methods are not applicable neither for the existing martensitic steels and nickel alloys nor for the materials under development. They do not allow for a detailed description of the damage processes. Contrary to the low alloyed steels applied in a lower temperature range, the structure stability which refers to the stability of the time- and load-dependent precipitation is of great importance.

295

Materials and Methods 14-2

Crack Sensors

Guided wave propagation

Actuator

Propagation of guided waves in pipes [Köhler et al., 2004] (left) and measurement principle of Acousto Ultrasonic (AU) measurements (right)

F.14-1

14-2 Materials and Methods

14-2-1 Measuring Technique Acousto Ultrasonics (AU)

An active SHM system uses a pulser for excitation of elastic waves and receiver trans-ducers with resonant frequencies in the ultrasonic range and wave propagation dynamics predictions for damage detection (see F.14-1 left).

Acousto Ultrasonics (AU) uses ultrasonic methods in a frequency range between 10 and 500 kHz. Ultrasonic waves are reflected by surfaces and interfaces, attenuated by dis-persion and absorption, and undergo mode changes during reflection and transmission.

These effects depend strongly on the frequency of the wave, its direction of propa-gation, its initial mode, and the location and orientation of surfaces and damage. When damage has occurred to a structure, changes in the signal and therefore the transfer func-tion indicate the type of damage like cracks and wall thickness reductions due to corro-sion. By pre-calculating the expected changes in the signal from given types and degrees of damage, the damage can be evaluated from AU measurements [Schubert, 2004; Schu-bert et al., 2008; 2009]. This kind of measurements is repeated according to the expected damage velocity. Using e. g. hourly measurement intervals, the growing of the damaged can be described with high time resolution. A high spatial resolution is achieved by using high frequencies with the disadvantage of shorter possible travel paths. An initial situa-tion (baseline) must be measured to describe the undamaged situation at different load levels since the damage might be load dependent.

14-2-2 Site Description and Waveguide Positions

RWE operates the lignite power plant Neurath. In Block E of this power plant the by-pass is installed and operated. All test and research activities have to be checked regard-ing their safety and to be coordinated with the business operation of the plant. In order to investigate the environmental noise level and the influences of the steam flow in the pipe,

296


Bypass Insulation

190

mm25.6 mm

Bypass location within the power plant (right side, lowest part)

Pipe dimensions (left) and bypass at the power plant with insulation (right)

F.14-2

F.14-3

an extra bypass for research purposes was established. This made the investigations inde-pendent from the power plant operating. The bypass is made of high temperature steel with 9 % chrome. It has a diameter of 190.0 mm and the wall thickness equals 25.6 mm (see F.14-2 and F.14-3 left).

The external surface temperature of the bypass reaches up to 520 °C. Therefore an insulation is applied to minimize energy losses (see F.14-3 right).

In order to protect the actuators and sensors from the heat radiated from the pipe wave guides were welded to the bypass on burlings used for the stabilization of the in-sulation (see F.14-4 and F.14-5). The wave guides have a length of 1 m and a diameter of 1 mm. Four measurement locations were chosen for installation at the bypass in the power plant Neurath. The wave guides are located in semicircles at two locations at a distance of 1.20 m and 90° shifted.

For these measurements, artificial drillings were performed between the wave guides with a depth of 7 mm (1/3 wall thickness).

297

Materials and Methods 14-2

Wave guide 3

Temperature measurement

Removeableinsulation

Sensors

Drilling

Actuators

1.20 m

12 14

90°

Wave guide 1

Wave guide 1/2

Wave guide 3/4

Wave guide 4

Wave guide 2

Location of the measurements in the power plant

Wave guide distribution at the bypass

F.14-5

F.14-4

14-2-3 Measuring Setup

For the excitation of the acoustic waves with the piezoelectric actuators and for re-ceiving the acoustic waves, the multi-channel acoustic measurement system (MAS) is developed for long-term monitoring of technical structures and plants (F.14-6 left and data sheet T.14-1). The system is based on a universal digital and customer adapted ana-logue device. The individual system adaptability is guaranteed by a digital signal proces-sor (DSP), by standardized data transfer ports as well as by the reconfigurable logic and software. The networking possibility of the varying MAS systems ensures the design of complex sensor networks for the monitoring of different system parameters.

The internal setup of the sensor/actuator and the pre-amplifier is shown in F.14-6 (right). It can be used as well for low frequency (e. g. modal analysis) and high frequency (acoustic emission, Acousto Ultrasonics) applications. They support the sending and re-ceiving of acoustic waves in the frequency range of 10…500 kHz.

298


Sensor & actuator module

Triaxialaccelerometer

Low-pass amplifier

Power amplifier

Wave guide

Ceramic insulation

Ceramic insulationPiezoelectric sensor

Piezoelectric sensor

Met

al c

ase

(shi

eld)

Band-pass amplifier

MAS measurementsystem (input stage)

Output Input

Number of differential channels 4 4

Resolution max. 160 Vpp @10 nF 12 Bit

Sample rate Arbitrary 12.5 MHz

Input level 14 Bit 10 Vpp, 100 Ohm

Frequency domain 18.75 MHz 10…500 kHz

Post amplifier for each channel separate −22…+20 dB

Analogue input and output for piezoelectric sensors of the measuring device MAS

MAS-measurement system (left) and simplified schematic of the measurement system with sensor/actuator and pre-amplifier (right)

T.14-1

F.14-6

14-2-4 Processing of the Data

In the following sections lower-cased bold letters mark vectors, lower-cased non-bold letters scalar values, capitalized bold letters matrices and the upper index the step of the processing, whereby zero stands for the initial measurement.

Mathematically a single measurement could be described as:

0( , , ) ( , , )T Z f T Z= +u t t e E.14-1

Whereas 0 m∈u is the measured signal voltage in [V], equidistantly sampled using m samples over the time m∈t in [s], by a given temperature T ∈ in [°C] and the dam-age indicator Z∈ , which indicates the artificial hole diameter in [mm]. The abstract function : m mf × × → × × characterizes the undisturbed system, material and geometry behaviour, which is superimposed by a statistically distributed noise m∈e .

299

Results 14-3

The damage parameter and the temperature are assumed as scalar values not varying significantly over a measurement interval of a few microseconds.

The goal is to find a set of k operators :i m mg × × → × × with 1, ,i k= :

1( , , ) ( ( , , ))i i iT Z g T Z−=u t u t , E.14-2

which could transform every signal initially measured at the temperature T to a signal at a reference temperature RT with the same damage parameter:

0( , , ) ( , , )kRT Z T Z≈u t u t . E.14-3

The signal at the reference temperature was approximated stepwise, whereas iu should converge with increasing index i to the signal 0( , , )RT Zu t measured under these specific conditions:

0 0 0 1 0( , , ) ( , , ) ( , , ) ( , , ) ( , , ) ( , , ) 0kR R RT Z T Z T Z T Z T Z T Z− > − > > − ≈u t u t u t u t u t u t E.14-4

The challenge lies in the construction of an operator set which performs the transfor-mation over a wide range of temperatures sufficiently and also conserves the influence of the damage parameter on the signal and the associated damage class.

14-3 Results

14-3-1 Temperature Correction

In order to correct the temperature influence on the signals correctly, a data process-ing procedure was developed for the measured temperature range from 20 °C to 500 °C and is described in the following sections.

For the processing, only the first two wave packets of each measured signal were con-sidered for simplicity because of their stability over a wide temperature range. The chal-lenge of the proposed procedure lies in the definition of boundaries to be specified for the wave packet extraction which are unstable and strongly depending on the temperature.

Therefore the boundaries were redefined after each step of constructing the phase correction operator in time domain. For this specific case, the boundary is a function of the location of the right or left minima over the temperature. Those two univariate poly-nomial functions of the chosen degree 1l − are observed by using two Lp-regressions of the form:

minp

xp− →Ax b , E.14-5

where n l×∈A is the Vandermonde-matrix of our n measurements, n∈b a vector of the minimum locations on the time axis and l∈x are the unknown polynomial coefficients. Because of the strong temperature dependency we used the L1-norm, which is more ro-

300


bust against outliners than the more commonly used L2-norm. To guarantee uniqueness, we chose the minimum p-norm solution. The signal was tapered afterwards.

14-3-2 Phase Correction in Time Domain

The process of constructing a phase correction operator is based on the work of Schu-bert [Schubert, 2012] and divided into two parts. At first the signal based on the envelope

0 m∈h of the initially measured wave packet was corrected and then, after redefining the boundaries, on a shifted form of the wave packet itself. The envelope ( , , )i T Zh t is cal-culated by the absolute of the Hilbert-transformation H of the signal:

( , , ) ( ( , , ))i iT Z T Z=h t u tH . E.14-6

Subsequently the time shift t∆ in [s] between the envelope and the envelope of the reference temperature is calculated for each of the n measurements by searching for the argument of the maximum of the cross-correlation between those envelopes (note that the results of E.14-7 have to be reduced by 1m− to get the real time shift) :

( )arg max ( , , ) ( , , ) ( )i it Rt T Z T Z∆ = ⋅h t h t t . E.14-7

Afterwards an L1-regression is performed according to E.14-5 with b: vector of the n time shifts. This procedure is also successfully applicable if the solution of E.14-7 is non-unique, because multiple values just become extra columns in b and A is also extended with the corresponding temperature entries. Based on the time shift function over the temperature obtained by the L1-regression, a circular shift is performed on each signal.

The phase correction of the wave packet itself is the same procedure apart from an additional redefinition of the shifted time signal boundaries. After shifting the signal the boundaries have to be finally redefined. The results of the whole procedure are a pair of final boundary-location functions and two shift functions, which could be superimposed to one function and converted to a shift function regarding any arbitrary reference tem-perature. The last point has the advantage that we do not need a reference signal at the exact reference temperature while calculating the time shifts, for example we could use a signal measured near the reference temperature instead.

14-3-3 Phase and Amplitude Correction in Frequency Domain

After the phase correction in time domain the wave packets have the same length and nearly no time shift but in general differ in amplitude and slightly in phase due to dispersion effects. To adjust such effects, the Fourier-transformation F was performed to convert the signals into frequency domain:

( , , ) ( ( , , ))i iT Z T Z=z f u tF . E.14-8

The vector ( , , )i mT Z ∈z f is the Fourier-transformed signal, whereby every column carries the information about amplitude and phase of one certain frequency, which are

301

Results 14-3

0.01

0.005

–0.005

–0.01

0

1.19 1.20

u0(t

,T,Z=

8) [V

]

t [s]

1.21 1.22 1.23 ·10–3

0.01

0.005

–0.005

–0.01

0

1.19 1.20

u1(t

,T,Z=

8) [V

]

1.21 1.22 1.23 ·10–3

0.01

0.005

–0.005

–0.01

0

1.19

50 100 150 200 250 300 350 400

1.20

u2(t

,T,Z=

8) [V

]

1.21 1.22 1.23 ·10–3

0.01

0.012

0.008

0.006

0.004

0.002

01.19 1.20

h0(t

,T,Z=

8) [V

]

t [s]

T [°C]

1.21 1.22 1.23 ·10–3

0.01

0.012

0.008

0.006

0.004

0.002

01.19 1.20 1.21 1.22 1.23 ·10–3

0.01

0.012

0.008

0.006

0.004

0.002

01.19 1.20 1.21 1.22 1.23 ·10–3

h1(t

,T,Z=

8) [V

]h2

(t,T

,Z=

8) [V

]Results of the temperature correction for 8 mm hole. Top: original signal, middle: signal after correction in time domain (time shift), bottom: signal after correction in time and frequency domain (phase and amplitude correction)

F.14-7

all together superimposing to the wave packet. The E.14-5 was solved for each frequency component (m-times) separately with n∈b as the vector of the certain Fourier-compo-nent for all n corrected signals.

The Vandermonde-matrix remained unchanged. The L2-norm was applied for this step since it showed smoother results. Note that b is a complex vector and therefore also ∈ and the resulting m polynomial correction functions have complex coefficients.

As a next step the polynomial functions were shifted so that they equal zero at RT . The resulting functions describe the difference of each Fourier-component from the reference temperature and have therefore to be subtracted from each corrected signal in frequency domain. The results are shown in F.14-7. The left side shows the time domain signals and the right side the envelopes. On the top, the original signals at different temperatures are displayed. The middle column shows the results after the first time shift corrections in time domain. On the bottom, the signals are show after the last step, the correction in frequency domain.

302


10

5

0

–5

–10

–20 –15 –10 –5 0–25

Normalized arg max {h2(t)}

Nor

mal

ized

max

{h2 (t)

}

z=0mm

z=8mm

z=10mm

Normalized maximum amplitude and corresponding time of the second wave packet

F.14-8

14-3-4 Damage Detection

Holes with diameters of 8 and 10 mm and with depth of 1/3 of the wall thickness (7 mm depth) each were introduced. Data for all the different states were gathered. F.14-8 shows the correlation of the normalized amplitude and time of the second maximum in the wave packet for diameters of 0, 8 and 10 mm respectively as an example of a suc-cessful temperature compensation and damage detection. The ellipses mark 95 % of the variance.

14-4 ConclusionsAs demonstrated, the data recorded in the power plant could be successfully correct-

ed regarding the temperature influence that would make any differential measurements for damage detection impossible. It can be clearly seen that the first correction step does not take any dispersion effects into account. Only the correction in frequency domain gives satisfactory results and also reconstructs the maximum of the second wave packet correctly.

Furthermore, the damages of 8 mm and 10 mm holes with 1/3 of the wall thickness (7 mm depth) could be detected and separated from the initial undamaged condition.

For future applications, this experiment will be repeated to prove the approach. It is also planned to use cluster analysis for the detection of unknown damages.

303

References 14

Acknowledgement

The present work has been supported by the Commission of the European Communi-ties in the framework of the specific targeted research project IRIS (Integrated European Industrial Risk Reduction System) under the 7th Framework Programme (FP7-NMP-2007-LARGE-1). This support is gratefully acknowledged. We also thank all our partners in the IRIS consortium, especially RWE, for providing us with the access to the power plant and the operating and support of the bypass measurements.

ReferencesKöhler, B., Schubert, F. and Frankenstein, B., 2004. Numerical and Experimental Investi-

gation of Lamb Wave Excitation, Propagation and Detection for Structural Health Moni-toring. Proceedings of the 2nd European Workshop on Structural Health Monitoring, Munich, Germany, 7–9 July, Eds: C. Boller, W. J. Staszewski, 993–1000.

Schubert, F., 2004. Basic Principles of Acoustic Emission Tomography. Journal of Acoustic Emission 22:147–158.

Schubert, F., Frankenstein, B., Klesse, T., Kerkhof, K., Schuler, X., Friedmann, H., Hen-kel, F. O. and Wenzel, H., 2009. Structural Health Monitoring and Lifetime Management of Industrial Piping Systems. In: Encyclopedia of Structural Health Monitoring, Eds: C. Boller, F.-K. Chang, Y. Fujino, John Wiley & Sons, Chichester, UK.

Schubert, L., Barth, M., Klesse, T., Köhler, B. and Frankenstein, B., 2008. Guided Elastic Waves and their Impact Interaction in CFRP Structures Characterized by 3D Laser Scan-ning Vibrometry. SPIE 03/2008, San Diego.

Schubert, L., 2012. Condition Monitoring of Fiber Composite Materials by Guided Waves. Dissertation, TU Dresden, ISBN 978-3-942710-72-5, 2012 TUDpress.

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