WO2022152336A1 - A method for monitoring turbine blade vibration - Google Patents

A method for monitoring turbine blade vibration Download PDF

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Publication number
WO2022152336A1
WO2022152336A1 PCT/CZ2021/050118 CZ2021050118W WO2022152336A1 WO 2022152336 A1 WO2022152336 A1 WO 2022152336A1 CZ 2021050118 W CZ2021050118 W CZ 2021050118W WO 2022152336 A1 WO2022152336 A1 WO 2022152336A1
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WO
WIPO (PCT)
Prior art keywords
signal
components
blade vibration
suppressed
vibration
Prior art date
Application number
PCT/CZ2021/050118
Other languages
French (fr)
Inventor
Jindřich LIŠKA
Jan JAKL
Vojtěch VAŠÍČEK
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Západočeská Univerzita V Plzni
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Application filed by Západočeská Univerzita V Plzni filed Critical Západočeská Univerzita V Plzni
Priority to CZ2023-364A priority Critical patent/CZ2023364A3/en
Priority to PCT/CZ2021/050118 priority patent/WO2022152336A1/en
Publication of WO2022152336A1 publication Critical patent/WO2022152336A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
    • G01H1/006—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines of the rotor of turbo machines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties

Definitions

  • the proposed invention falls within the field of measuring and assessing mechanical vibration and resonances of turbine blades.
  • the contact method is based on strain gauging. Strain gauging provides information about the mechanical stress on the blade surface. It concerns a contact measurement, where the sensor is firmly connected to the blade, which allows very accurate mechanical stress values to be obtained. This approach allows measurements with a high sampling rate, which can be used in later signal analysis and processing.
  • the most commonly used sensors are resistance strain gauges, which work on the principle of a conductor changing its electrical resistance as it becomes deformed.
  • monitoring the mechanical stress of the rotating blades means it is necessary to take the measured quantity data away from the rotating system in the form of an electrical signal.
  • the extreme properties of the working medium interacting with the installed sensors and channels in the flow section make long-term strain gauging untenable.
  • strain gauges The limiting factor of strain gauging in the case of monitoring the vibration of circulating blades is the necessity of installing the sensors in the flow section of the turbine, and therefore there must be resistance to the prevailing conditions and there must be some manner of outputting the measured signals from the rotating system.
  • strain gauges is unsuitable for long-term monitoring, and it can be used mainly for short-term measurements and experiments.
  • a method called Blade Tip-Timing can be used for the long-term monitoring of blade vibration.
  • the method uses a set of sensors built into the stator section above the blades being monitored (above the blade wheel). The measured signal is used to identify the time the blade passes under the sensor. From the knowledge of the geometric layout of the blades around the blade wheel’s circumference, i.e., the number of blades and the blade wheel circumference, the expected times the blade passes under the sensor can be determined. The deviations of the expected passage times and the actual measured passage times from the BTT sensors are further processed to determine the frequency and amplitude characteristics of the oscillations of each blade of the wheel (e.g., using frequency analysis by applications of Fourier transform).
  • the purpose of the invention is to eliminate the need to install new sensors in the turbine body or in the flow section and to use existing sensors (installed as standard). This will make it significantly cheaper to install a blade monitoring system and increase the availability of long-term blade monitoring not only for newly installed turbines, but also for machines already in operation (there is no need to shut down the machine when installing the monitoring equipment).
  • the essence of the invention is a method for monitoring turbine blade vibration from rotor vibration, for example, of a steam or gas turbine.
  • the method comprises measuring the rotor vibration of the turbine using at least one rotor vibration sensor located on at least one turbine bearing.
  • a sensor of relative rotor vibration is used. This means that the sensor is located on a fixed part of the device and senses the relative distance between the fixed part of the device and the possibly vibrating shaft of the turbine. This is most often a sensor using the principle of eddy current measurement.
  • the signal obtained from the rotor vibration sensor is preprocessed by converting it from the time domain to the frequency domain.
  • the result is the signal spectrum, which can be seen in Fig. 1.
  • the purpose of converting to the frequency domain is to identify the frequency components contained in the measured signal.
  • the discrete Fourier transform method is preferably used for conversion from the time domain to the frequency domain.
  • the wide band spectrum noise level of signal is suppressed and the wide band spectrum signal noise variance is suppressed. This results in an increase in the separation between the signal components that are excited by the blade oscillation (useful signal) and the noise.
  • the wide band spectrum noise level of signal is suppressed by liftering the signal cepstrum.
  • This is done by calculating the cepstrum of the rotor vibration signal and liftering this cepstrum - the unwanted components are suppressed by weighting the individual cepstrum coefficients c by a weighting function referred to as the lifter w.
  • the principle can be seen in Fig. 2. This suppresses the unwanted spectral components by using a suitably chosen lifter, see Fig. 3.
  • the lifter of the cepstrum is used here to calculate the “spectral envelope”, which characterizes the level of the background noise.
  • a lifter in the form of a Gaussian function is used. Using the lifter and the calculation of the spectral envelope subsequently allows this spectral envelope to be subtracted from the amplitude spectrum and thus suppress wide band spectrum noise.
  • the results of this principle can be seen in Fig. 4 and Fig. 5.
  • the wide band spectrum signal noise variance is suppressed. This step is based on the finding that each spectral component of the signal, or amplitude, is additively loaded with noise.
  • the mean value of the wide band spectrum noise was suppressed in the previous step.
  • the wide band spectrum signal noise variance can be advantageously suppressed by averaging the liftered spectra of the signal. The averaging is done by summing several consecutive signal spectra calculated over time and dividing them by the number of summed spectra.
  • the principle of suppressing of the wide band spectrum signal noise variance by averaging can be seen in Figure 6. Spectral estimation methods can also be used to suppress the wide band spectrum signal noise variance.
  • Identification includes the process of thresholding the spectrum of the preprocessed signal and clustering the components from the thresholding. Identification may further include a process of filtering the clustered components. Filtering can be used to remove some insignificant clusters, leading to more accurate results.
  • Thresholding the preprocessed signal means it is possible to identify those amplitudes of the spectrum of the preprocessed signal with suppressed variance of wide band spectrum noise that exceed the value of a pre-selected threshold (amplitude level).
  • the principle of thresholding can be seen in Fig. 7.
  • the median of the values of the analysed liftered spectrum can be used to determine the threshold level.
  • the value of the original unliftered spectrum of the signal at the identified blade frequency is used as the amplitude of the identified frequency component.
  • Clustering the components from thresholding means grouping the components according to a defined degree of similarity. The degree of similarity between two components in this function is based on their frequency distance. The frequency distance in this case is compared with a predefined clustering threshold, once this is crossed, it determines the formation of a new cluster.
  • the frequency distance can be conveniently defined e.g., by making use of the Euclidean distance. Thanks to clustering, it is possible to decide about the number of identified blade frequencies and specific properties (frequency and amplitude values).
  • An example of the clustering output is shown in Fig. 8. From the figure it is clear that the algorithm closely follows both blade components present in the rotor vibration signal.
  • the identified blade vibration components are then monitored for the magnitude of deviation in frequency and/or amplitude of at least one identified component from its nominal state.
  • the values obtained can be used, for example, to detect increased blade vibration if the amplitude of the identified component increases from its nominal state.
  • Another possible use may be to identify a change in frequency of a blade component, which indicates a frequency realignment of one or more blades. This may indicate a possible mechanical change (damage - a crack, part of the material breaking, etc.).
  • This example describes a method for monitoring turbine blade vibration from rotor vibration. During this method, the turbine rotor vibration is measured using at least one rotor vibration sensor located on at least one turbine bearing.
  • the signal obtained is preprocessed by converting it from the time domain to the frequency domain.
  • the conversion is performed using a discrete Fourier transform.
  • the wide band spectrum noise level of signal is suppressed and the wide band spectrum signal noise variance is suppressed.
  • the wide band spectrum noise level of signal is suppressed by lifting the cepstrum.
  • the wide band spectrum signal noise variance is suppressed by averaging the liftered signal spectra.
  • the components of the blade vibration in the preprocessed signal are identified. This components identification includes the process of thresholding the spectrum of the preprocessed signal and clustering the components from the thresholding.
  • the identified blade vibration components are subsequently monitored for the magnitude of deviation in frequency and/or amplitude of at least one identified component from its nominal state.
  • Fig. 2 the cepstrum of the rotor vibration c and the lifter in the form of a Gaussian function w;
  • Fig. 3 the liftered cepstrum of the rotor vibration
  • Fig. 4 suppressing the signal noise level using the liftered cepstrum, where the amplitude spectrum X is given by the solid line and the liftered spectrum Xw by the dashed line;
  • Fig. 5 the amplitude spectrum from Fig. 4 after subtracting the spectral envelope and zero mean spectral noise
  • Fig. 7 thresholding of the preprocessed signal processed in the steps of Figs. 4 to 6, where those amplitudes of the averaged spectrum that exceed the value of the pre-selected threshold T (amplitude level) are identified - see amplitudes indicated by square C;
  • Fig. 8 the output of the component clustering, where the curve composed of the values of p representing the centre of each cluster is the solid line in the figure, while the black points X are the individual components (identified components of blade vibration).

Abstract

When monitoring turbine blade vibration, the turbine rotor vibration is measured using at least one rotor vibration sensor located on at least one turbine bearing. The signal obtained is preprocessed by converting it from the time domain to the frequency domain (preferably by a discrete Fourier transform), the wide band spectrum noise level of signal is suppressed (preferably by liftering the signal cep strum) and the variance of the wide band spectrum signal noise is suppressed (preferably by averaging the liftered spectra of the signal). Subsequently, the components of the blade vibration in the preprocessed signal are identified. The identification includes the process of thresholding the preprocessed signal and clustering the components from the thresholding. The identified blade vibration components are then monitored for the magnitude of deviation in frequency and/or amplitude of at least one identified component from its nominal state.

Description

A Method for Monitoring Turbine Blade Vibration
Field of technology
The proposed invention falls within the field of measuring and assessing mechanical vibration and resonances of turbine blades.
Background art
Monitoring the vibration of blades is an important task in the diagnosis of turbomachinery, especially steam and gas turbines. The steam turbine operator is under pressure to minimize the cost of operating the turbine whilst also ensuring that the turbine is trouble free. One of the ways to achieve this is by long-term monitoring and the early detection of potential faults. Nevertheless, it is common practice that there is no long-term monitoring of turbine blade vibration. This is due to the diagnostic system’s relatively high purchase and operating costs. Existing approaches to blade vibration monitoring are mainly based on two measurement methods, contact and non-contact.
The contact method is based on strain gauging. Strain gauging provides information about the mechanical stress on the blade surface. It concerns a contact measurement, where the sensor is firmly connected to the blade, which allows very accurate mechanical stress values to be obtained. This approach allows measurements with a high sampling rate, which can be used in later signal analysis and processing. The most commonly used sensors are resistance strain gauges, which work on the principle of a conductor changing its electrical resistance as it becomes deformed. However, monitoring the mechanical stress of the rotating blades means it is necessary to take the measured quantity data away from the rotating system in the form of an electrical signal. At the same time, the extreme properties of the working medium interacting with the installed sensors and channels in the flow section make long-term strain gauging untenable. The limiting factor of strain gauging in the case of monitoring the vibration of circulating blades is the necessity of installing the sensors in the flow section of the turbine, and therefore there must be resistance to the prevailing conditions and there must be some manner of outputting the measured signals from the rotating system. Thus, the use of strain gauges is unsuitable for long-term monitoring, and it can be used mainly for short-term measurements and experiments.
A method called Blade Tip-Timing (BTT) can be used for the long-term monitoring of blade vibration. The method uses a set of sensors built into the stator section above the blades being monitored (above the blade wheel). The measured signal is used to identify the time the blade passes under the sensor. From the knowledge of the geometric layout of the blades around the blade wheel’s circumference, i.e., the number of blades and the blade wheel circumference, the expected times the blade passes under the sensor can be determined. The deviations of the expected passage times and the actual measured passage times from the BTT sensors are further processed to determine the frequency and amplitude characteristics of the oscillations of each blade of the wheel (e.g., using frequency analysis by applications of Fourier transform).
The purpose of the invention, the essence of which is described in the following section, is to eliminate the need to install new sensors in the turbine body or in the flow section and to use existing sensors (installed as standard). This will make it significantly cheaper to install a blade monitoring system and increase the availability of long-term blade monitoring not only for newly installed turbines, but also for machines already in operation (there is no need to shut down the machine when installing the monitoring equipment).
Disclosure of the invention
The essence of the invention is a method for monitoring turbine blade vibration from rotor vibration, for example, of a steam or gas turbine. The method comprises measuring the rotor vibration of the turbine using at least one rotor vibration sensor located on at least one turbine bearing. Preferably a sensor of relative rotor vibration is used. This means that the sensor is located on a fixed part of the device and senses the relative distance between the fixed part of the device and the possibly vibrating shaft of the turbine. This is most often a sensor using the principle of eddy current measurement.
The signal obtained from the rotor vibration sensor is preprocessed by converting it from the time domain to the frequency domain. The result is the signal spectrum, which can be seen in Fig. 1. The purpose of converting to the frequency domain is to identify the frequency components contained in the measured signal. The discrete Fourier transform method is preferably used for conversion from the time domain to the frequency domain. However, there are several other methods that can be used. These include, for example, the least squares method or methods performing spectral estimation based on autoregressive models, such as the Burg method or the Yule-Walker method.
Next, the wide band spectrum noise level of signal is suppressed and the wide band spectrum signal noise variance is suppressed. This results in an increase in the separation between the signal components that are excited by the blade oscillation (useful signal) and the noise.
Preferably, the wide band spectrum noise level of signal is suppressed by liftering the signal cepstrum. This is done by calculating the cepstrum of the rotor vibration signal and liftering this cepstrum - the unwanted components are suppressed by weighting the individual cepstrum coefficients c by a weighting function referred to as the lifter w. The principle can be seen in Fig. 2. This suppresses the unwanted spectral components by using a suitably chosen lifter, see Fig. 3. The lifter of the cepstrum is used here to calculate the “spectral envelope”, which characterizes the level of the background noise. Preferably, a lifter in the form of a Gaussian function is used. Using the lifter and the calculation of the spectral envelope subsequently allows this spectral envelope to be subtracted from the amplitude spectrum and thus suppress wide band spectrum noise. The results of this principle can be seen in Fig. 4 and Fig. 5.
In the next step the wide band spectrum signal noise variance is suppressed. This step is based on the finding that each spectral component of the signal, or amplitude, is additively loaded with noise. The mean value of the wide band spectrum noise was suppressed in the previous step. The wide band spectrum signal noise variance can be advantageously suppressed by averaging the liftered spectra of the signal. The averaging is done by summing several consecutive signal spectra calculated over time and dividing them by the number of summed spectra. The principle of suppressing of the wide band spectrum signal noise variance by averaging can be seen in Figure 6. Spectral estimation methods can also be used to suppress the wide band spectrum signal noise variance.
After preprocessing the signal, the components of the blade vibration in the preprocessed signal are identified. Identification includes the process of thresholding the spectrum of the preprocessed signal and clustering the components from the thresholding. Identification may further include a process of filtering the clustered components. Filtering can be used to remove some insignificant clusters, leading to more accurate results.
Thresholding the preprocessed signal means it is possible to identify those amplitudes of the spectrum of the preprocessed signal with suppressed variance of wide band spectrum noise that exceed the value of a pre-selected threshold (amplitude level). The principle of thresholding can be seen in Fig. 7. During automatic detection, the median of the values of the analysed liftered spectrum can be used to determine the threshold level. The value of the original unliftered spectrum of the signal at the identified blade frequency is used as the amplitude of the identified frequency component.
It is desirable to represent each of the blade spectral components by one frequency and one amplitude value, instead of all the identified components. This is why the components are clustered. Clustering the components from thresholding means grouping the components according to a defined degree of similarity. The degree of similarity between two components in this function is based on their frequency distance. The frequency distance in this case is compared with a predefined clustering threshold, once this is crossed, it determines the formation of a new cluster.
The frequency distance can be conveniently defined e.g., by making use of the Euclidean distance. Thanks to clustering, it is possible to decide about the number of identified blade frequencies and specific properties (frequency and amplitude values). An example of the clustering output is shown in Fig. 8. From the figure it is clear that the algorithm closely follows both blade components present in the rotor vibration signal.
The identified blade vibration components are then monitored for the magnitude of deviation in frequency and/or amplitude of at least one identified component from its nominal state. The values obtained can be used, for example, to detect increased blade vibration if the amplitude of the identified component increases from its nominal state. Another possible use may be to identify a change in frequency of a blade component, which indicates a frequency realignment of one or more blades. This may indicate a possible mechanical change (damage - a crack, part of the material breaking, etc.).
Exemplary embodiment of the invention
This example describes a method for monitoring turbine blade vibration from rotor vibration. During this method, the turbine rotor vibration is measured using at least one rotor vibration sensor located on at least one turbine bearing.
The signal obtained is preprocessed by converting it from the time domain to the frequency domain. In this example, the conversion is performed using a discrete Fourier transform. Next, the wide band spectrum noise level of signal is suppressed and the wide band spectrum signal noise variance is suppressed. In this example, the wide band spectrum noise level of signal is suppressed by lifting the cepstrum. In this example, the wide band spectrum signal noise variance is suppressed by averaging the liftered signal spectra. After preprocessing the signal, the components of the blade vibration in the preprocessed signal are identified. This components identification includes the process of thresholding the spectrum of the preprocessed signal and clustering the components from the thresholding.
The identified blade vibration components are subsequently monitored for the magnitude of deviation in frequency and/or amplitude of at least one identified component from its nominal state.
Brief description of drawings
The example of the proposed solution is described with reference to drawings showing
Fig. 1 - rotor vibration spectrum X;
Fig. 2 - the cepstrum of the rotor vibration c and the lifter in the form of a Gaussian function w;
Fig. 3 - the liftered cepstrum of the rotor vibration;
Fig. 4 - suppressing the signal noise level using the liftered cepstrum, where the amplitude spectrum X is given by the solid line and the liftered spectrum Xw by the dashed line;
Fig. 5 - the amplitude spectrum from Fig. 4 after subtracting the spectral envelope and zero mean spectral noise;
Fig. 6 - averaged spectrum processed in the steps of Fig. 4 and Fig. 5;
Fig. 7 - thresholding of the preprocessed signal processed in the steps of Figs. 4 to 6, where those amplitudes of the averaged spectrum that exceed the value of the pre-selected threshold T (amplitude level) are identified - see amplitudes indicated by square C;
Fig. 8 - the output of the component clustering, where the curve composed of the values of p representing the centre of each cluster is the solid line in the figure, while the black points X are the individual components (identified components of blade vibration).

Claims

- 6 - Claims
1. A method for monitoring turbine blade vibration, wherein the turbine rotor vibration is measured using at least one rotor vibration sensor located on at least one turbine bearing and the signal thus obtained is preprocessed by converting it from the time domain to the frequency domain, characterized in that the wide band spectrum noise level of signal is suppressed and the wide band spectrum signal noise variance is suppressed, after preprocessing the signal, the components of blade vibration in the preprocessed signal are identified, wherein the identification includes the process of thresholding the spectrum of the preprocessed signal and clustering the components from the thresholding and subsequently the identified blade vibration components are monitored for the magnitude of deviation in frequency and/or amplitude of at least one identified component from its nominal state.
2. The method for monitoring turbine blade vibration according to claim 1 characterized in that the wide band spectrum noise level of signal is suppressed by liftering the signal cepstrum.
3. The method for monitoring turbine blade vibration according to claim 1 or 2 characterized in that the wide band spectrum signal noise variance is suppressed by averaging the liftered signal spectra.
4. The method for monitoring turbine blade vibration according to any one of claims 1 to 3 characterized in that the signal obtained is converted from the time domain to the frequency domain using a discrete Fourier transform.
PCT/CZ2021/050118 2021-10-26 2021-10-26 A method for monitoring turbine blade vibration WO2022152336A1 (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116340730A (en) * 2023-05-26 2023-06-27 西安现代控制技术研究所 Rocket elastic vibration frequency online identification and inhibition method
CN116340730B (en) * 2023-05-26 2023-08-04 西安现代控制技术研究所 Rocket elastic vibration frequency online identification and inhibition method

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