CN116127294B - Empirical mode decomposition instability judging method based on window superposition algorithm - Google Patents
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Abstract
The invention belongs to the field of aeroengine control systems, and particularly relates to an empirical mode decomposition instability judging method based on a window superposition algorithm. According to the invention, through carrying out empirical mode decomposition calculation on the pulsating total pressure/static pressure signal after the compression component in a windowing manner, a plurality of layers of eigenmode functions are obtained, window superposition is carried out on each layer of eigenmode functions, a plurality of layers of signals are obtained, signals which accord with instability characteristics are judged, the amplitude of the signals within a period of time is calculated, and the amplitude is divided by the average value of the bottommost signals, so that a normalized surge judgment value can be obtained, and the instability of the compression system can be judged after the normalized surge judgment value exceeds a preset threshold value. The method can identify weak instability signal characteristics to judge asthma, can avoid false instability characteristics formed by pulsation total pressure/static pressure fluctuation caused by movement of the throttle lever, and ensures the use safety of the aeroengine.
Description
Technical Field
The invention belongs to the field of aeroengine control systems, and particularly relates to an empirical mode decomposition instability judging method based on a window superposition algorithm.
Background
As the overall performance index of modern aero-engines increases, the overall pressure ratio, i.e. the load, of the compression components such as fans and compressors increases, so that the instability phenomena such as stall and surge, etc. of the compression system, which are caused by the serious separation of the air flow due to the reverse pressure gradient flow, are also more frequent. The instability judging module in the aero-engine control system has the function of monitoring whether the aero-engine compression system is unstable or not in real time. The current method for judging the instability in China is to filter the original signals and then judge whether the relative change value of the pressure difference behind the compressor (journal: design and verification of certain aeroengine surge control system: aerodynamics report, 2022,37 (05): 1100-1112) or the falling slope of the static pressure/total pressure behind the compressor (journal: surge detection method based on the static pressure change rate of the outlet of the compressor: aerodynamics report, 2020,35 (06): 1131-1139) exceeds a preset threshold value. The sensitivity of the asthma judging method is uncontrollable, meanwhile, the main frequency of instability cannot be directly selected for judgment, weak high-altitude instability signals cannot be judged when the threshold value is set too large, and pressure fluctuation caused by movement of the throttle lever cannot be avoided when the threshold value is set too small.
Disclosure of Invention
In view of the above, the present invention provides an empirical mode decomposition instability determination method based on a window superposition algorithm, which obtains a plurality of layers of eigenmode functions by performing empirical mode decomposition calculation on a pulsating total pressure/static pressure signal sub-window after compressing a component, and performs window superposition on each layer of eigenmode functions to obtain a plurality of layers of signals, determines a signal which accords with an instability characteristic therein, calculates an amplitude value within a period of time and divides the amplitude value by an average value of a bottommost signal, thereby obtaining a normalized surge determination value, and determining that the compression system is unstable when the normalized surge determination value exceeds a preset threshold value. The method can identify weak instability signal characteristics to judge asthma, can avoid false instability characteristics formed by pulsation total pressure/static pressure fluctuation caused by movement of the throttle lever, and ensures the use safety of the aeroengine.
In order to achieve the technical purpose, the invention adopts the following specific technical scheme:
an empirical mode decomposition instability judgment method based on a window superposition algorithm is used for instability judgment of an aeroengine compression system and comprises the following steps:
step one: acquiring a pulsation total pressure signal or a pulsation static pressure signal at the outlet of each compression component of the compression system to form an original pressure signal;
step two: intercepting the original pressure signal according to the set calculation window length and the overlap data amount to obtain a segmented pressure signal;
step three: performing empirical mode decomposition on all the segmented pressure signals to obtain a multi-layer eigenmode function;
step four: multiplying the eigen mode function of each layer by a set window function and carrying out window superposition according to the overlapped data quantity to obtain a continuous multi-layer decomposition pressure signal;
step five: performing Fourier transformation on each layer of decomposition pressure signals, calculating a main frequency, judging whether the main frequency is in an unstable frequency range, finding out the maximum amplitude of the main frequency in the unstable frequency range, dividing the main frequency of the maximum amplitude by the average value of the last layer of signals of each layer of decomposition pressure signals, and obtaining a normalized asthma judging value;
step six: judging whether the normalized asthma judging value exceeds a set threshold value, and if so, generating a destabilizing signal; the destabilization signal is used for enabling a control system of the compression system to output an asthma-relieving instruction.
Further, in the fourth step, the set window function is a window function which is obtained according to different window function definitions, window lengths of the window functions, and window overlapping data amounts of the window functions, is 1 in the middle, and gradually changes to 0 toward two ends.
Further, in the fourth step, the eigenmode function of each layer is multiplied by the set window function to effectively suppress sideband noise of the eigenmode function.
Further, in the fourth step, the window stacking is implemented based on a stacking of the bottom-most eigenmode functions between the decomposed pressure signals.
Further, if the number of layers of the eigen mode functions of each decomposition pressure signal is inconsistent, the method for ensuring that each decomposition pressure signal can be directly added when the windows are overlapped is as follows:
and filling the eigenvalue function of the decomposition pressure signal with the non-maximum layer number into the signal layer with the total value of 0 until the layer number of the eigenvalue function of each decomposition pressure signal is consistent.
Further, the compression component is a fan or a compressor.
Further, the set window function is a rectangular window, a triangular window or a hanning window.
By adopting the technical scheme, the invention has the following beneficial effects:
1) The invention can judge the instability of the components of the compression system such as the fan and the compressor.
2) The window superposition method provided by the invention can correct the eigenmode function obtained by empirical mode decomposition aiming at different window lengths, overlapping data amounts and window functions, eliminate sideband noise of the eigenmode function, and effectively distinguish weak instability characteristics of a compression system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of steps of an empirical mode decomposition instability determination method based on a window superposition algorithm in a specific embodiment of the present invention;
FIG. 2 is a flow chart of the construction of window functions in an embodiment of the present invention;
fig. 3 is a schematic diagram of a manner of overlapping corrected eigenmode functions according to time in an embodiment of the present invention.
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
In one embodiment of the present invention, an empirical mode decomposition instability determination method based on a window superposition algorithm is provided, which is used for instability determination of an aeroengine compression system, as shown in fig. 1, and includes the following steps:
step one: acquiring a pulsation total pressure signal or a pulsation static pressure signal at the outlet of each compression component of the compression system to form an original pressure signal;
step two: intercepting an original pressure signal according to the set calculation window length and the overlapping data quantity to obtain a segmented pressure signal;
step three: performing empirical mode decomposition on all the segmented pressure signals to obtain a multi-layer eigenmode function;
step four: multiplying each layer of eigen mode function by a set window function, and carrying out window superposition according to the overlapped data quantity to obtain a continuous multi-layer decomposition pressure signal;
step five: performing Fourier transformation on each layer of decomposition pressure signals, calculating a main frequency, judging whether the main frequency is in an unstable frequency range, finding out the maximum amplitude of the main frequency in the unstable frequency range, dividing the main frequency of the maximum amplitude by the average value of the last layer of signals of each layer of decomposition pressure signals, and obtaining a normalized asthma judging value;
step six: judging whether the normalized asthma judging value exceeds a set threshold value, and if so, generating a destabilizing signal; the destabilization signal is used to cause a control system of the compression system to output an anti-surge instruction.
In the present embodiment, in the fourth step, the window function is set to be a window function having a middle of 1 and gradually changing to 0 toward both ends, which is obtained from different window function definitions, window lengths of the window functions, and window overlap data amounts of the window functions.
In the fourth embodiment, in the step, the eigenmode function of each layer is multiplied by a set window function to effectively suppress sideband noise of the eigenmode function.
In the present embodiment, in step four, window stacking is implemented based on stacking of the bottom-most eigenmode functions between the decomposed pressure signals.
In this embodiment, if the number of layers of the eigenmode functions of each decomposition pressure signal is inconsistent, the method for ensuring that each decomposition pressure signal can be directly added when the windows are overlapped is as follows:
and filling the eigenvalue function of the decomposition pressure signal with the non-maximum layer number into the signal layer with the total value of 0 until the layer number of the eigenvalue function of each decomposition pressure signal is consistent.
In this embodiment, the compression member is a fan or a compressor.
In this embodiment, the window function is set as a rectangular window, a triangular window, or a hanning window.
In step one of this embodiment, in order to accurately determine whether the compression element is unstable, it should be ensured that the acquisition frequency of the compression element outlet pulsating pressure signal or the pulsating static pressure signal is not less than 4 times of the rotor frequency of the compression element, and the original pressure signals formed should be arranged according to serial numbers or time sequences to form original pressure signals (i.e. vectors
)。
In step two of the present embodiment, the window length N is calculated according to the setting window Overlap data amount N overlap And intercepting the original data. I.e.
1 st to nth data in (a) window The first is the 1 st component section pressure data, which is marked as +.>
The method comprises the steps of carrying out a first treatment on the surface of the Then
Of 1+ (N) window -N overlap ) Data to 2N window -N overlap The number is 2 nd component section pressure data, which is marked as +.>
The method comprises the steps of carrying out a first treatment on the surface of the Analogize in turn, let us block>
In 1+i (N) window -N overlap ) Data to Nth window +i*(N window -N overlap ) The i-th component is the pressure data of the section, which is marked as +.>
。
In step three of this embodiment, the pressure data is segmented for the ith component
Empirical mode decomposition is performed to obtain j layers of eigenmode functions, which are marked as IMF i,j 。
In step four of the present embodiment, a window function is first constructed, as shown in fig. 2, and a window length N is defined and calculated from the window function window Overlap data amount N overlap Constructing a window function, wherein the data length of the window function is equal to N window The initial value of all data of the window function is 1.
Then calculate
The quotient mod and remainder res are obtained. The first N data and the last N data of the window function are attenuation bands, the middle N window The data of-2 n remain unchanged at 1. If the remainder res is 0, the decay band length N of the window function is N window -N overlap The global correction coefficient is 1/(mod-1), and if the remainder res is not 0, the attenuation band length n of the window function is res, and the global correction coefficient is 1/mod.
The attenuation bands constructed by the first n data are generated according to window function definition, and if a rectangular window is selected, the n data are all 0.5; if a triangular window is selected, wherein the ith data is (i-1)/(n-1); if a Hanning window is selected, then the ith data is
. The attenuation band of n data after the window function can be obtained from the attenuation band image of the first n data.
Multiplying all data of the window function by a global correction function to obtain a final window function and an intrinsic mode function IMF i,j And multiplying the obtained product by a window function to carry out correction to obtain a corrected eigenmode function.
The corrected eigenmode functions are overlapped according to time as shown in fig. 3.
According to the embodiment, the intrinsic mode function obtained by empirical mode decomposition can be corrected according to different window lengths, overlapping data amounts and window functions, sideband noise of the intrinsic mode function is eliminated, and weak instability characteristics of a compression system can be effectively resolved.
The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the disclosure are intended to be covered by the protection scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (7)
1. The empirical mode decomposition instability judging method based on the window superposition algorithm is used for judging the instability of an aeroengine compression system and is characterized by comprising the following steps of:
step one: acquiring a pulsation total pressure signal or a pulsation static pressure signal at the outlet of each compression component of the compression system to form an original pressure signal;
step two: intercepting the original pressure signal according to the set calculation window length and the overlap data amount to obtain a segmented pressure signal;
step three: performing empirical mode decomposition on all the segmented pressure signals to obtain a multi-layer eigenmode function;
step four: multiplying the eigen mode function of each layer by a set window function and carrying out window superposition according to the overlapped data quantity to obtain a continuous multi-layer decomposition pressure signal;
step five: performing Fourier transformation on each layer of decomposition pressure signals, calculating a main frequency, judging whether the main frequency is in an unstable frequency range, finding out the maximum amplitude of the main frequency in the unstable frequency range, dividing the main frequency of the maximum amplitude by the average value of the last layer of signals of each layer of decomposition pressure signals, and obtaining a normalized asthma judging value;
step six: judging whether the normalized asthma judging value exceeds a set threshold value, and if so, generating a destabilizing signal; the destabilization signal is used for enabling a control system of the compression system to output an asthma-relieving instruction.
2. The empirical mode decomposition destabilizing determination method based on a window superimposition algorithm according to claim 1, wherein in the fourth step, the set window function is a window function having a middle of 1 and a gradual change to both ends of 0, which is obtained from different window function definitions, window lengths of the window functions, and window superimposition data amounts of the window functions.
3. The method for determining the empirical mode decomposition instability based on the window stacking algorithm according to claim 2, wherein in the fourth step, each layer of eigenmode functions is multiplied by the set window function to effectively suppress sideband noise of the eigenmode functions.
4. The empirical mode decomposition instability determination method based on a window stacking algorithm according to claim 3, wherein in the fourth step, the window stacking is implemented based on stacking of the bottom-most eigenmode functions between the decomposed pressure signals.
5. The empirical mode decomposition instability determination method based on a window stacking algorithm according to claim 4, wherein if the number of layers of the eigenmode functions of each decomposition pressure signal is inconsistent, the method for ensuring that each decomposition pressure signal can be directly added during window stacking is as follows:
and filling the eigenvalue function of the decomposition pressure signal with the non-maximum layer number into the signal layer with the total value of 0 until the layer number of the eigenvalue function of each decomposition pressure signal is consistent.
6. The method for determining empirical mode decomposition instability based on a window stacking algorithm according to claim 5, wherein the compression element is a fan or a compressor.
7. The empirical mode decomposition destabilizing determination method based on a window superimposition algorithm according to claim 6, characterized in that the set window function is a rectangular window, a triangular window, or a hanning window.
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