CN114659791A - Steam turbine fault detection method, system, device and storage medium - Google Patents
Steam turbine fault detection method, system, device and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system and a device for detecting faults of a steam turbine and a storage medium, which can be applied to the technical field of fault detection. The method comprises the steps of adding a Nuttall window function to an original vibration displacement signal to improve subsequent processing precision, performing Fourier transform on a second displacement signal obtained after the Nuttall window function is added to generate an amplitude spectrum, calculating a power frequency noise signal according to the amplitude spectrum, removing the power frequency noise signal from the original vibration displacement signal to obtain a third displacement signal, performing singular value decomposition on the third displacement signal, and inhibiting a random noise signal in the third displacement signal through a priority difference spectrum peak algorithm, so that the power frequency noise signal and the random noise signal in the original vibration displacement signal are effectively eliminated, the loss of a useful signal is reduced, and the result of performing steam turbine fault analysis on a target displacement signal obtained after the power frequency noise signal and the random noise signal are eliminated is more accurate.
Description
Technical Field
The invention relates to the technical field of fault detection, in particular to a method, a system, a device and a storage medium for detecting faults of a steam turbine.
Background
In the related technology, the vibration displacement signals of the steam turbine contain power frequency noise and random noise, and if the signals are not processed and directly utilized, the actual operation state of the steam turbine can be misjudged. Currently, for this type of signal, separate methods are usually used to suppress power frequency noise and random noise, respectively. One of the most common power frequency noise suppression methods at present is a wave trap method, and the premise of applying the wave trap method is that a power frequency noise frequency spectrum is assumed to be fixed, but actual power frequency noise has fluctuation in a certain range, and in order to effectively filter the actual power frequency noise, the wave trap frequency spectrum needs to have a certain width, so that even if power frequency interference is filtered, useful signals are damaged at the same time, and the accuracy is poor.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a method, a system, a device and a storage medium for detecting the fault of the steam turbine, which can effectively inhibit power frequency noise signals and random noise signals and simultaneously reduce the loss of useful signals.
On one hand, the embodiment of the invention provides a steam turbine fault detection method, which comprises the following steps:
collecting an original vibration displacement signal of a steam turbine as a first displacement signal;
adding a Nuttall window function to the first displacement signal to obtain a second displacement signal;
performing Fourier transform on the second displacement signal to generate an amplitude spectrum;
calculating a power frequency noise signal according to the amplitude spectrum;
removing the power frequency noise signal from the first displacement signal to obtain a third displacement signal;
after singular value decomposition is carried out on the third displacement signal, a random noise signal in the third displacement signal is suppressed through a priority difference spectrum peak algorithm, and a target displacement signal is obtained;
and analyzing the fault of the steam turbine according to the target displacement signal.
In some embodiments, said performing a singular value decomposition of said third displacement signal comprises:
constructing a Hankel matrix with preset dimensionality according to the third displacement signal;
and carrying out singular value decomposition on the Hankel matrix to obtain a singular value sequence of the Hankel matrix.
In some embodiments, the suppressing the random noise signal in the third displacement signal by the preferential difference spectral peak algorithm to obtain a target displacement signal includes:
generating a singular value difference spectrum according to the singular value sequence;
determining a reconstruction order according to the singular value difference spectrum;
and reconstructing a time domain signal according to the reconstruction order to obtain a target displacement signal.
In some embodiments, said calculating a power frequency noise signal from said amplitude spectrum comprises:
determining corresponding amplitudes of preset three spectral lines in the amplitude spectrum;
calculating an interpolation coefficient according to the amplitude;
calculating power frequency noise signal parameters according to the interpolation coefficients and the amplitudes corresponding to the preset three spectral lines;
and determining a power frequency noise signal according to the power frequency noise signal parameter.
In some embodiments, the determining the corresponding amplitude of the preset three spectral lines in the amplitude spectrum includes:
extracting a maximum spectral line with the amplitude value in a preset range of the peak frequency point of the amplitude spectrum as a target spectral line;
extracting a left spectral line and a right spectral line of the target spectral line from the amplitude spectrum respectively;
determining the magnitudes of the target spectral line, the left spectral line, and the right spectral line on the magnitude spectrum.
In some embodiments, the calculating a power frequency noise signal parameter according to the interpolation coefficient and the amplitude corresponding to the preset three spectral lines includes:
and calculating power frequency noise signal parameters according to the interpolation coefficient and the amplitude of the target spectral line on the amplitude spectrum.
In some embodiments, said acquiring a raw vibration displacement signal of the steam turbine comprises:
and acquiring original vibration displacement signals of a plurality of data points according to a preset sampling frequency.
In another aspect, an embodiment of the present invention provides a steam turbine fault detection system, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring an original vibration displacement signal of the steam turbine as a first displacement signal;
the first processing module is used for adding a Nuttall window function to the first displacement signal to obtain a second displacement signal;
the second processing module is used for performing Fourier transform on the second displacement signal to generate an amplitude spectrum;
the calculation module is used for calculating a power frequency noise signal according to the amplitude spectrum;
the third processing module is used for removing the power frequency noise signal from the first displacement signal to obtain a third displacement signal;
the fourth processing module is used for suppressing a random noise signal in the third displacement signal through a priority difference spectrum peak algorithm after singular value decomposition is carried out on the third displacement signal, so that a target displacement signal is obtained;
and the analysis module is used for carrying out steam turbine fault analysis according to the target displacement signal.
On the other hand, an embodiment of the present invention provides a steam turbine fault detection apparatus, including:
at least one memory for storing a program;
at least one processor configured to load the program to perform the method for detecting a turbine fault.
In another aspect, an embodiment of the present invention provides a storage medium, in which a computer-executable program is stored, and the computer-executable program is executed by a processor to implement the method for detecting a fault of a steam turbine.
The steam turbine fault detection method provided by the embodiment of the invention has the following beneficial effects:
according to the method, a Nuttall window function is added to an original vibration displacement signal acquired in real time to improve subsequent processing precision, then a second displacement signal obtained after the Nuttall window function is added is used as Fourier transform to generate an amplitude spectrum, a power frequency noise signal is calculated according to the amplitude spectrum, a third displacement signal is obtained after a power frequency noise signal is removed from the original vibration displacement signal, then after singular value decomposition is carried out on the third displacement signal, a random noise signal in the third displacement signal is suppressed through a priority difference spectrum peak algorithm, so that the power frequency noise signal and the random noise signal in the original vibration displacement signal are effectively eliminated, loss of useful signals is reduced, and the result of performing steam turbine fault analysis on a target displacement signal obtained after the power frequency noise signal and the random noise signal are eliminated is more accurate.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further described with reference to the following figures and examples, in which:
FIG. 1 is a flow chart of a method of detecting a fault in a steam turbine according to an embodiment of the present invention;
FIG. 2 is a time domain waveform diagram corresponding to an original vibration displacement signal according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an amplitude spectrum corresponding to the time domain waveform shown in FIG. 3 according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an amplitude spectrum after Fourier transform of a windowed vibration displacement signal according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an amplitude spectrum of a vibration displacement signal from which a power frequency noise signal is removed after Fourier transform according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a singular value spectrum of a vibration displacement signal after singular value decomposition according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a singular value difference spectrum corresponding to the singular value spectrum shown in FIG. 6 according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a time domain signal reconstructed from the singular value difference spectrum of FIG. 7 according to an embodiment of the present invention;
fig. 9 is a schematic diagram of an amplitude spectrum corresponding to fig. 8 according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present numbers, and the above, below, within, etc. are understood as including the present numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
In the description of the present invention, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The steam turbine main unit equipment structure is complicated, and numerous and the mutual influence of parameter is to the effective monitoring of steam turbine state the focus that people paid attention to always. Because of the sensitivity of the vibration to the state of the steam turbine set, when the steam turbine set is abnormal or has a fault, the vibration of the set usually reacts immediately, so the analysis of the vibration signal is an important means for monitoring the running state of the steam turbine, which is helpful for carrying out early analysis and diagnosis on the fault and taking corresponding measures in time. The non-contact eddy current displacement sensor is widely used for monitoring the state of low-frequency vibration objects such as a steam turbine and the like, an axis locus reflecting the running condition of a rotor can be generated by using two mutually perpendicular eddy current displacement sensors, and whether the rotor has faults such as unbalance, misalignment, shaft crack, oil film whirling, oil film instability and the like can be visually judged through the axis locus. However, because the working environment of the steam turbine is complex and severe, the vibration displacement signals of the main engine of the steam turbine unit are extremely susceptible to the interference of power frequency noise in the acquisition process, the acquired vibration displacement signals also contain a large amount of random noise, and if the signals are directly utilized without processing, the actual running state of the steam turbine can be misjudged. Therefore, an effective signal purification method is required to be adopted to suppress power frequency noise and random noise of the power supply in the vibration displacement signal as much as possible and extract a signal capable of reflecting the real working state of the steam turbine.
The vibration displacement signal of the steam turbine contains power frequency noise of a power supply and random noise, and for the type of signal, a separate method is usually adopted to respectively inhibit the power frequency noise and the random noise. One of the most common power frequency noise suppression methods at present is a wave trap method, and the premise of applying the wave trap method is that a power frequency noise frequency spectrum is assumed to be fixed, but because actual power frequency noise has fluctuation in a certain range, in order to effectively filter the power frequency noise, the wave trap frequency spectrum needs to have a certain width, so that even if power frequency interference is filtered, useful signals are damaged at the same time, and the accuracy is poor.
Based on this, the embodiment of the invention provides a steam turbine fault detection method, which adds a Nuttall window function to an original vibration displacement signal acquired in real time to improve the subsequent processing precision, then the second displacement signal obtained after the Nuttall window function is added is used as Fourier transform to generate an amplitude spectrum, then a power frequency noise signal is calculated according to the amplitude spectrum, and removing power frequency noise signals from the original vibration displacement signals to obtain third displacement signals, then, after the third displacement signal is subjected to singular value decomposition, a random noise signal in the third displacement signal is suppressed through a priority difference spectrum peak algorithm, thereby effectively eliminating power frequency noise signals and random noise signals in the original vibration displacement signals, reducing the loss of useful signals, the result of the steam turbine fault analysis based on the target displacement signal obtained after the power frequency noise signal and the random noise signal are eliminated is more accurate.
Specifically, referring to fig. 1, an embodiment of the present invention provides a method for detecting a turbine fault, including steps 110 to 170:
and 110, acquiring an original vibration displacement signal of the steam turbine as a first displacement signal.
In the embodiment of the application, the original vibration displacement signals of a plurality of data points can be collected according to the preset sampling frequency. Specifically, when the preset sampling frequency is fsThen can be based on the sampling frequency fsCollecting data of N data points to form vibration displacement signal x0(N), (N ═ 0,1, …, N-1). The power frequency noise signal ζ (n) in the vibration displacement signal can be expressed as shown in equation (1):
wherein, A in the formula (1)1、f1、
The amplitude, the frequency and the initial phase in the power frequency noise parameters are respectively.
For example, with a sampling frequency of 1024Hz as the preset sampling frequency, acquiring data of 1024 points to obtain an original vibration displacement signal x0(t), the original vibration displacement signal x0The time domain waveform of (t) is shown in FIG. 2, and the corresponding amplitude spectrum is shown in FIG. 3.
And step 120, adding a Nuttall window function to the first displacement signal to obtain a second displacement signal.
In the embodiment of the present application, the time domain expression of the nuttalll window function may be as shown in formula (2):
in the time domain expression of equation (2), amDenotes a preset number, N is 0,1, …, N-1, and N denotes the points. For example, a0＝10/32，a1＝15/32，a2＝6/32，a3＝1/32。
Then the first displacement signal x is subjected to a Nuttall window function w (n) shown in formula (2)0(n) performing a plurality of point truncations to obtain a discrete signal sequence as a second displacement signal x1(n) wherein x1(n)＝x0(n)·w(n)。
And step 130, performing Fourier transform on the second displacement signal to generate an amplitude spectrum.
In the embodiment of the application, the windowed vibration displacement signal x1(n) performing fast Fourier transform to generate an amplitude spectrum as shown in FIG. 4, wherein the spectral line k with the largest amplitude is showna50, corresponding magnitude | X (k)a) 0.002465 left line ka-1Corresponding to an amplitude | X (k) of 49a-1) 0.002001 right spectral line ka+151, corresponding amplitude | X (k)a+1) 0.001651. Phase arg [ X (k) corresponding to maximum spectral linea)]＝-0.213052rad。
And 140, calculating a power frequency noise signal according to the amplitude spectrum.
In the embodiment of the application, the amplitude corresponding to the preset three spectral lines in the amplitude spectrum is determined, then the interpolation coefficient is calculated according to the amplitude, then the power frequency noise signal parameter is calculated according to the interpolation coefficient and the amplitude corresponding to the preset three spectral lines, and then the power frequency noise signal is determined according to the power frequency noise signal parameter. Specifically, for determining the corresponding amplitude of the preset three spectral lines in the amplitude spectrum, the maximum amplitude spectral line within the preset range of the peak frequency point of the amplitude spectrum is extracted as a target spectral line, then the left spectral line and the right spectral line of the target spectral line are respectively extracted from the amplitude spectrum, and then the amplitudes of the target spectral line, the left spectral line and the right spectral line on the amplitude spectrum are determined. And for the determination of the power frequency noise parameters, calculating power frequency noise signal parameters according to the interpolation coefficient and the amplitude of the target spectral line on the amplitude spectrum.
In this embodiment, when the vibration displacement signal is sampled asynchronously, the frequency point f of the power frequency noise signal is due to the fence effect1Hardly exactly at the sampling frequency, i.e. f1And are generally not integers. Suppose that the interpolation coefficient δ is k-kaThen there is-0.5<δ<0.5. In the amplitude spectrum, the maximum spectral line of the amplitude obtained by sampling near the peak frequency point is set as kaTaking the maximum-amplitude spectral line as a target spectral line whose left spectral line is ka-1Right spectral line is ka+1，ka-1、kaAnd ka+1The corresponding amplitudes of the three preset spectral lines are respectively | X (k)a-1)|、|X(ka) I and I X (k)a+1) L. The three spectral line interpolation is to accurately solve the delta value by using the information of the three preset spectral lines. In particular, assume that
The interpolation coefficient δ is shown in equation (3):
δ ═ 4(α -1)/(α +1) formula (3)
And calculating the power frequency noise signal parameter by using the interpolation coefficient delta. In particular, the amplitude A1Is shown in formula (4) and frequency f1Is shown in formula (5) and the initial phase
Is shown in equation (6):
f1＝(kα+ delta. delta.f equation (5)
The power frequency noise parameters in the fault characteristic signals can be accurately calculated by using the formula, and then an expression of the power frequency noise signals zeta (n) shown in a formula (7) is obtained:
where n in the formula (7) is 0,1, …, and 1023.
For example, the power frequency noise signal calculated according to the formula (4), the formula (5) and the formula (6) has an amplitude of 0.007950, a frequency of 49.836749Hz and a phase of 0.299313 rad. Then, the mathematical expression for obtaining the power frequency noise signal according to the power frequency noise signal parameters is shown in formula (8):
ζ (n) ═ 0.007950 · sin (2 π · 49.836749 · n/1024+0.299313) formula (8)
Where n in the formula (8) is 0,1, …, and 1023.
And 150, removing the power frequency noise signal from the first displacement signal to obtain a third displacement signal.
In this embodiment of the present application, a third displacement signal may be obtained by subtracting the first displacement signal and the power frequency noise signal. For example, from a first displacement signal x0And (n) subtracting the power frequency component zeta (n) to obtain a turbine vibration displacement signal with suppressed power frequency noise as a third displacement signal.
For example, the third displacement signal is x2(n) indicates that the signal x is shifted from the first displacement0The subtraction of the power frequency noise signal ζ (n) from (n) can be expressed as shown in equation (9):
x2(n)＝x0(n) - ζ (n) formula (9)
In particular, for x2(n) fast Fourier transform to generate the amplitude spectrum shown in FIG. 5. As can be seen from fig. 5, the power frequency noise in the first displacement signal is effectively eliminated, and the adjacent components of the power frequency noise are less affected. Therefore, the power frequency noise reduction method of the embodiment has obvious advantages compared with a wave trap filtering method.
And 160, after singular value decomposition is carried out on the third displacement signal, suppressing a random noise signal in the third displacement signal through a priority difference spectrum peak algorithm to obtain a target displacement signal.
In the embodiment of the application, singular value decomposition is performed on the third displacement signal, and after a Hankel matrix with preset dimensionality is constructed according to the third displacement signal, singular value decomposition is performed on the Hankel matrix to obtain a singular value sequence of the Hankel matrix. In particular, by the third displacement signal x2(n) (n is 0,1, …,1023) to construct a Hankel matrix with a preset dimension of 512 x 513 as shown in formula (10), wherein the Hankel matrix means that elements on each minor diagonal are all arrangedEqual square matrix:
then, carrying out singular value decomposition on the matrix A to generate an orthogonal matrix U epsilon R512×512And V ∈ R513×513So that equation (11) holds:
A＝UΛVTformula (11)
In equation (11), Λ is a diagonal matrix, R is a rank of Λ, and the non-zero diagonal element σi(n-0, 1, …,511) is called the singular value of a, i.e., the singular value spectrum shown in fig. 6.
In this embodiment, after the third displacement signal is subjected to singular value decomposition, all singular values are divided into a smaller singular value reflecting noise and a larger singular value reflecting useful signal characteristics, the smaller singular value part is set to zero, and the larger singular value part is retained, so that the purpose of suppressing random noise is achieved. The singular value decomposition noise reduction method is to select a proper order k value for reconstruction, the selection of the reconstruction order k is crucial, the signal distortion is serious when the order value is too small, and more noise is caused when the order value is too large. Therefore, the present embodiment determines the k value using the preferred differential spectral peak method.
Specifically, for suppressing the random noise signal in the third displacement signal by using a priority difference spectrum peak algorithm to obtain a target displacement signal, a singular value difference spectrum is generated according to the singular value sequence, a reconstruction order is determined according to the singular value difference spectrum, and then time domain signal reconstruction is performed according to the reconstruction order to obtain the target displacement signal. Wherein the singular value sequence sigmaiGenerating a singular value differential spectrum, the singular value differential spectrum betaiThe definition is shown in formula (12):
βi＝σi-σi+1formula (12)
In the formula (12), i is 0,1,2, …, 510.
The optimal difference spectrum peak value method is used, namely the most appropriate reconstruction order k value is selected from the difference spectrum peak values, and then the matrix A' is reconstructed by the formula (13):
then, the first row and the last column in the matrix A' are extracted to generate a target displacement signal x3(n)，x3And (n) is the vibration displacement signal with suppressed random noise. x is the number of3And (n) is also a finally purified turbine eddy current vibration displacement signal.
For example, a singular value difference spectrum as shown in fig. 7 is generated from the singular value spectrum shown in fig. 6. As can be seen from fig. 7, when the singular value index is greater than 20, the difference spectrum peak is small, and the small peak indicates that each subsequent singular value is not changed substantially, and k is 20, which is the optimal reconstruction order. Reconstructing with the reconstruction order k being 20, and obtaining the time domain signal x as shown in fig. 8 by reconstruction3(n), the amplitude spectrum of the time domain signal is shown in fig. 9. Comparing fig. 8 with fig. 2 and fig. 9 with fig. 3, it is found that the suppression effect of the preferred difference spectrum peak method on random noise is significant, that the number of spikes due to random noise is much reduced in the waveform in fig. 8, and that the high frequency spectrum corresponding to random noise is also very small in fig. 9. The time domain waveforms and frequency spectra shown in fig. 8 and 9 are also the purification results of the final vibration displacement signal.
And 170, analyzing the fault of the steam turbine according to the target displacement signal.
In the embodiment of the application, the original vibration displacement signal inhibits power frequency noise by using a windowing interpolation method, random noise is inhibited by using a preferred difference spectrum peak value method, and finally, the purified vibration displacement signal of the steam turbine is obtained and used as a target displacement signal, and the target displacement signal can relatively truly reflect the actual working condition of the steam turbine. The embodiment performs the fault analysis of the steam turbine state based on the purified target displacement signal, and can effectively improve the accuracy of the fault analysis result.
The embodiment of the invention provides a steam turbine fault detection system, which comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring an original vibration displacement signal of the steam turbine as a first displacement signal;
the first processing module is used for adding a Nuttall window function to the first displacement signal to obtain a second displacement signal;
the second processing module is used for performing Fourier transform on the second displacement signal to generate an amplitude spectrum;
the calculation module is used for calculating a power frequency noise signal according to the amplitude spectrum;
the third processing module is used for removing the power frequency noise signal from the first displacement signal to obtain a third displacement signal;
the fourth processing module is used for suppressing random noise signals in the third displacement signals through a priority difference spectrum peak algorithm after singular value decomposition is carried out on the third displacement signals, so that target displacement signals are obtained;
and the analysis module is used for carrying out steam turbine fault analysis according to the target displacement signal.
The content of the embodiment of the method of the invention is all applicable to the embodiment of the system, the function of the embodiment of the system is the same as the embodiment of the method, and the beneficial effect achieved by the embodiment of the system is the same as the beneficial effect achieved by the method.
The embodiment of the invention provides a steam turbine fault detection device, which comprises:
at least one memory for storing a program;
at least one processor configured to load the program to perform the method of turbine fault detection illustrated in FIG. 1.
The content of the method embodiment of the invention is all applicable to the device embodiment, the functions specifically realized by the device embodiment are the same as those of the method embodiment, and the beneficial effects achieved by the device embodiment are also the same as those achieved by the method.
An embodiment of the present invention provides a storage medium in which a computer-executable program is stored, and the computer-executable program is executed by a processor to implement the steam turbine fault detection method shown in fig. 1.
Embodiments of the present invention also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the method for turbine fault detection illustrated in fig. 1.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
Claims (10)
1. A method of detecting a fault in a steam turbine, comprising the steps of:
collecting an original vibration displacement signal of a steam turbine as a first displacement signal;
adding a Nuttall window function to the first displacement signal to obtain a second displacement signal;
performing Fourier transform on the second displacement signal to generate an amplitude spectrum;
calculating a power frequency noise signal according to the amplitude spectrum;
removing the power frequency noise signal from the first displacement signal to obtain a third displacement signal;
after singular value decomposition is carried out on the third displacement signal, a random noise signal in the third displacement signal is suppressed through a priority difference spectrum peak algorithm, and a target displacement signal is obtained;
and analyzing the fault of the steam turbine according to the target displacement signal.
2. The method of detecting a steam turbine fault of claim 1 wherein said performing a singular value decomposition of said third displacement signal comprises:
constructing a Hankel matrix with preset dimensionality according to the third displacement signal;
and carrying out singular value decomposition on the Hankel matrix to obtain a singular value sequence of the Hankel matrix.
3. The turbine fault detection method of claim 2, wherein said suppressing random noise signals in said third displacement signals by a preferential difference spectral peak algorithm to obtain target displacement signals comprises:
generating a singular value difference spectrum according to the singular value sequence;
determining a reconstruction order according to the singular value difference spectrum;
and reconstructing a time domain signal according to the reconstruction order to obtain a target displacement signal.
4. The method of detecting a turbine fault according to claim 1 wherein said calculating a power frequency noise signal from said amplitude spectrum comprises:
determining corresponding amplitudes of preset three spectral lines in the amplitude spectrum;
calculating an interpolation coefficient according to the amplitude;
calculating power frequency noise signal parameters according to the interpolation coefficients and the amplitudes corresponding to the preset three spectral lines;
and determining a power frequency noise signal according to the power frequency noise signal parameter.
5. The method of detecting turbine faults according to claim 4, wherein said determining the corresponding amplitudes of the predetermined three spectral lines in the amplitude spectrum comprises:
extracting a maximum spectral line with the amplitude value in a preset range of the peak frequency point of the amplitude spectrum as a target spectral line;
extracting a left spectral line and a right spectral line of the target spectral line from the amplitude spectrum respectively;
determining the magnitudes of the target spectral line, the left spectral line, and the right spectral line on the magnitude spectrum.
6. The method for detecting the fault of the steam turbine according to claim 5, wherein the step of calculating the power frequency noise signal parameter according to the interpolation coefficient and the amplitude corresponding to the preset three spectral lines comprises the following steps:
and calculating power frequency noise signal parameters according to the interpolation coefficient and the amplitude of the target spectral line on the amplitude spectrum.
7. The method of detecting a turbine fault according to claim 1 wherein said collecting a raw vibration displacement signal of the turbine comprises:
and acquiring original vibration displacement signals of a plurality of data points according to a preset sampling frequency.
8. A steam turbine fault detection system, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring an original vibration displacement signal of the steam turbine as a first displacement signal;
the first processing module is used for adding a Nuttall window function to the first displacement signal to obtain a second displacement signal;
the second processing module is used for performing Fourier transform on the second displacement signal to generate an amplitude spectrum;
the calculation module is used for calculating a power frequency noise signal according to the amplitude spectrum;
the third processing module is used for removing the power frequency noise signal from the first displacement signal to obtain a third displacement signal;
the fourth processing module is used for suppressing random noise signals in the third displacement signals through a priority difference spectrum peak algorithm after singular value decomposition is carried out on the third displacement signals, so that target displacement signals are obtained;
and the analysis module is used for carrying out steam turbine fault analysis according to the target displacement signal.
9. A steam turbine fault detection device, comprising:
at least one memory for storing a program;
at least one processor configured to load the program to perform the method for turbine fault detection according to any of claims 1-7.
10. A storage medium having stored therein a computer-executable program for implementing the steam turbine fault detection method according to any one of claims 1 to 7 when executed by a processor.
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