Method, system, medium and apparatus for continuous monitoring of compressor near stall acoustic signatures

Abstract

A compressor near stall state voiceprint continuous monitoring method, system, medium and equipment, in the method, a predetermined number of microphone measuring points are randomly selected at the inlet to form a microphone array, and multi-channel sound pressure time domain signals are synchronously collected; a rectangular window is added to the multi-channel sound pressure time domain signal to construct an acoustic time domain signal segment, and a fast Fourier transform is performed on the multi-channel acoustic time domain signal segment in the current time domain window to form multiple sound pressure spectra; it is judged whether there are multiple non-synchronous single-tone characteristic frequencies which are non-integral multiples of the current rotation frequency in the sound pressure spectrum; a multi-channel microphone continuous frequency domain observation matrix of the non-synchronous single-tone characteristic frequency is constructed; an orthogonal Fourier transfer matrix is constructed based on the installation azimuth angle of the microphone sensor and the highest order of the mode; a compressor near stall characteristic mode reconstruction model under non-uniform few measuring points is established; the amplitude is solved based on the block orthogonal matching pursuit algorithm, and the compressor near stall working condition monitoring based on the voiceprint characteristics is realized.

Description

Technical Field

[0001] This invention relates to the field of compressor condition monitoring technology, and in particular to a method, system, medium, and equipment for continuous acoustic monitoring of compressor near stall conditions. Background Technology

[0002] As aircraft demand higher altitudes, speeds, and maneuverability, the operating environment of compressors becomes increasingly harsh. When an engine enters an aerodynamically unstable operating state, it can lead to reduced engine performance, increased vibration stress on the rotor blades of the compression components, increased thermal load and stress on the turbine, a reduced stable operating range of the combustion chamber, and may even damage the structural integrity of the engine, seriously threatening flight safety.

[0003] Monitoring unsteady pressure pulsations in compressors under near-stall conditions, and detecting and diagnosing precursory anomalies exhibited during this period, is crucial for designing available stability margins and controlling aerodynamic stability in aero-engine compression systems. Currently, monitoring compressor status typically involves installing strain gauges and pressure probes on rotating blades, which is costly and can interfere with the compressor flow field due to its invasive nature. Acoustic sensors, on the other hand, offer advantages such as high sensitivity, short transmission paths, and non-invasive measurement. Compressors often generate strong single-tone noise under near-stall conditions, and acoustic signals are a significant form of fault response. However, research on the acoustic signature characteristics of compressors under near-stall conditions is limited, and a near-stall monitoring method based on acoustic signature characteristics has not yet been established.

[0004] The information disclosed in the background section is only for enhancing the understanding of the background of this invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, system, medium, and device for continuous acoustic signature monitoring of compressors near stall conditions. By arranging a small number of non-uniform ring microphone arrays at the compressor inlet, and using a spectrum analysis module of the acoustic array test signals to determine the asynchronous characteristic single-tone frequencies of the compressor near stall conditions, a multi-channel microphone continuous frequency domain observation matrix of the asynchronous single-tone characteristic frequencies is constructed. A reconstructed model of the compressor near stall characteristic modes under non-uniformity and few measurement points is established. The amplitude of the compressor near stall characteristic acoustic modes is solved based on a block orthogonal matching pursuit algorithm, achieving compressor near stall condition monitoring based on acoustic signature features. This method achieves, for the first time, real-time monitoring of near stall characteristic mode amplitudes based on a small number of acoustic measurement points, providing an effective tool for engine control under near stall conditions. Simultaneously, this method uses a more sensitive, shorter transmission path, and non-invasive acoustic monitoring approach, overcoming the difficulty of achieving dynamic and sensitive feedback in traditional static pressure sensors monitoring near stall conditions.

[0006] A method for continuous acoustic signature monitoring of a compressor near stall includes:

[0007] In the first step, based on the number of rotor blades of the compressor... With the number of stator blades Calculate the highest circumferential modal order of the compressor With the number of microphones At the entrance, a predetermined number of microphone measurement points are randomly selected to form a microphone array, and multi-channel sound pressure time-domain signals are synchronously acquired.

[0008] In the second step, a rectangular window is added to the multi-channel acoustic time-domain signal to construct an acoustic time-domain signal segment. A fast Fourier transform is then performed on the multi-channel acoustic time-domain signal segment within the current time-domain window to generate multiple acoustic pressure spectra. It is then determined whether there are multiple asynchronous single-tone characteristic frequencies in the acoustic pressure spectrum that are not integer multiples of the current rotation frequency. If not, it indicates that the compressor is in normal working condition, and the second step is repeated. If so, further monitoring of near-stall conditions is carried out.

[0009] In the third step, a multi-channel microphone continuous frequency domain observation matrix for asynchronous single-tone characteristic frequencies is constructed. Based on the microphone sensor installation azimuth angle and the highest order of the mode, an orthogonal Fourier transfer matrix is ​​constructed to establish a compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points.

[0010] In the fourth step, the amplitude of the compressor near-stall characteristic mode reconstruction model is solved based on the block orthogonal matching pursuit algorithm, so as to realize the monitoring of compressor near-stall conditions based on acoustic features.

[0011] In the aforementioned method for continuous acoustic signature monitoring of a compressor near-stall state, the first step includes,

[0012] Step S101: Calculate the highest circumferential modal order, where the modal order of the circumferential acoustic mode is... , This indicates the order of pressure pulsations caused by unsteady aerodynamic forces resulting from the interference of the fan's rotation and stationary motion. Represent a non-negative integer to determine the highest circumferential modal order. The measurable modal range is determined to be: ;

[0013] Step S102: Set the number of microphones in the virtual ring. The installation location, based on the Nyquist-Shannon sampling theory, and the number of microphones... With the highest modal order The relationship is The virtual microphone array is evenly arranged in the circumferential direction, and the spacing between each virtual grid point is [missing information]. ;

[0014] Step S103: Randomly install K microphones on the casing wall in the circumferential direction, where K is approximately the number of microphones sampled by Nyquist sampling. 50%, installation angle .

[0015] In the aforementioned method for continuous acoustic signature monitoring of a compressor near-stall state, the second step (S2) includes the following steps:

[0016] Step S201: Determine the sampling frequency to establish the window length for the acoustic time-domain signal segment. Add a rectangular window to the multi-channel sound pressure time-domain signal, with a window length of... and , For any integer, This is the sampling frequency of the microphone sensor; and every [time] during continuous sampling... Windowing is applied to the sound pressure time-domain signal of a multi-channel signal, and the overlap length of each window is... ;

[0017] Step S202: Process the current acoustic time-domain signal segment Perform a Fast Fourier Transform on the K sound pressure time-domain signals: ,in, This represents the current acoustic time-domain signal segment matrix of the sound pressure level time-domain signal obtained from a non-uniform microphone array measurement. The dimension of the current time-domain signal segment matrix is... , Represents the Discrete Fourier Transform. For multi-channel arrays in the first The frequency domain matrix of an acoustic time-domain signal segment, with dimensions of , , means as follows:

[0018]

[0019] in ,

[0020] Step S203: Based on the obtained frequency domain matrix Plot the sound pressure spectrum obtained from sensors at different locations and observe whether there are multiple frequency shifts that are divided by integer multiples. The non-synchronous single-tone characteristic frequency outside, that is, exists If not, repeat step 2 (S2); if it exists, further monitor the near-stall condition.

[0021] In the aforementioned method for continuous acoustic signature monitoring of a compressor near-stall state, the third step includes:

[0022] Step S301: Establish asynchronous single-tone characteristic frequencies Multi-channel microphone continuous frequency domain observation matrix :

[0023]

[0024] in, Represents a segment of acoustic time-domain signal The asynchronous single-tone characteristic frequency at the k-th measurement point of the non-uniform array Amplitude,

[0025] Step S302: Construct the orthogonal Fourier transfer matrix ,

[0026]

[0027] in ,

[0028] Step S303: Define the acoustic mode matrix A near-stall characteristic mode reconstruction model for the compressor was established.

[0029]

[0030] in, For the first matrix row vectors The number of dominant modes of interest. It is a structurally sparse norm and has:

[0031] ,

[0032] This is an indicator function that returns 1 when the condition is true.

[0033] In the aforementioned method for continuous acoustic signature monitoring of a compressor near-stall state, the fourth step includes:

[0034] Step S401: Set the residual matrix Index sequence number set Structural sparsity The number of dominant modes of interest, the number of iterations. ;

[0035] Step S402, Iteration Count Update ;

[0036] Step S403: Calculate the support set index ;

[0037] Step S404: Update the index sequence set ;

[0038] Step S405: Calculate the acoustic mode amplitude vector of the corresponding support set.

[0039] ;

[0040] Step S406: Update the residual matrix ;

[0041] Step S407: Check the number of iterations Has the structural sparsity been achieved? If the target value is reached, the amplitude monitoring result of the compressor near-stall characteristic mode reconstruction model will be output; otherwise, return to step S402.

[0042] In the aforementioned method for continuous acoustic signature monitoring of a compressor near stall state, the compressor is the compressor of an aero-engine.

[0043] In the aforementioned method for continuous acoustic signature monitoring of a compressor near stall state, the compressor includes a fan structure.

[0044] A fan blade synchronous vibration identification system implementing the method includes:

[0045] The sound field measurement module is used to measure the number of rotor blades of the compressor. With the number of stator blades Calculate the highest circumferential modal order of the compressor With the number of microphones At the entrance, a predetermined number of microphone measurement points are randomly selected to form a microphone array, and multi-channel sound pressure time-domain signals are synchronously acquired.

[0046] The spectrum analysis module is used to add rectangular windows to the multi-channel sound pressure time-domain signal, construct acoustic time-domain signal segments, and perform fast Fourier transform on the multi-channel acoustic time-domain signal segments within the current time-domain window to generate multiple sound pressure spectra. It then determines whether there are multiple asynchronous single-tone characteristic frequencies in the sound pressure spectrum that are not integer multiples of the current frequency.

[0047] The module is used to construct a multi-channel microphone continuous frequency domain observation matrix for asynchronous single-tone characteristic frequencies, construct an orthogonal Fourier transfer matrix based on the microphone sensor installation azimuth angle and the highest order of the mode, and establish a compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points.

[0048] The monitoring module uses a block orthogonal matching pursuit algorithm to solve the amplitude of the compressor near-stall characteristic mode reconstruction model, thereby realizing compressor near-stall condition monitoring based on acoustic signature features.

[0049] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.

[0050] An electronic device, the electronic device comprising:

[0051] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0052] The processor implements the method when executing the program.

[0053] Compared with existing technologies, this invention has the following advantages: Compared with traditional interstage static pressure monitoring, this invention utilizes acoustic information to monitor the near-stall point of a compression system, exhibiting higher sensitivity and stronger adaptability; it reveals the group sparsity characteristics in the acoustic modal monitoring problem, and solves the acoustic modal reconstruction problem more accurately and robustly through a block orthogonal matching pursuit algorithm; it achieves continuous-time monitoring of the amplitude of near-stall characteristic modes with fewer measurement points, greatly simplifying the testing system. The spatial modal characteristics of the acoustic signal are defined as the acoustic signature characteristics of the compressor near-stall point. Attached Figure Description

[0054] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0055] In the attached diagram:

[0056] Figure 1 This is the flowchart of this disclosure;

[0057] Figure 2 This is a schematic diagram of a method and apparatus for continuous acoustic signature monitoring of a compressor near stall state based on structural sparse features, provided in one embodiment of this disclosure.

[0058] Figure 3 This is a spectrum diagram of the sound pressure signal at the near-stall condition provided in one embodiment of this disclosure;

[0059] Figure 4(a) shows an embodiment of this disclosure. Figure 3 The results of acoustic mode decomposition at the characteristic frequency 14.7EO; Figure 4(b) is an embodiment of this disclosure. Figure 3 A schematic diagram of the acoustic mode decomposition results at the characteristic frequency of 15.7EO;

[0060] Figures 5(a) to 5(b) Figure 4(a) is a schematic diagram of the two-mode acoustic modal amplitude monitoring results provided in one embodiment of this disclosure;

[0061] Figures 6(a) to 6(b) Figure 4(b) is a schematic diagram of the two-mode acoustic modal amplitude monitoring results provided in one embodiment of this disclosure.

[0062] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0063] Specific embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0064] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.

[0065] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0066] like Figures 1 to 6(b) As shown, the method for continuous acoustic signature monitoring of compressors near stall includes the following steps:

[0067] In the first step S1, based on the number of rotor blades of the compressor... With the number of stator blades Calculate the highest circumferential modal order of the compressor With the number of microphones At the entrance, a predetermined number of microphone measurement points are randomly selected to form a microphone array, and multi-channel sound pressure time-domain signals are synchronously acquired.

[0068] In the second step S2, a rectangular window is added to the multi-channel acoustic pressure time-domain signal to construct an acoustic time-domain signal segment. A fast Fourier transform is then performed on the multi-channel acoustic time-domain signal segment within the current time-domain window to generate multiple acoustic pressure spectra. It is then determined whether there are multiple asynchronous single-tone characteristic frequencies in the acoustic pressure spectrum that are not integer multiples of the current rotation frequency. If not, it indicates that the compressor is in normal working condition, and the second step S2 is repeated. If so, further monitoring of near-stall conditions is carried out.

[0069] In the third step S3, a multi-channel microphone continuous frequency domain observation matrix for asynchronous single-tone characteristic frequencies is constructed. Based on the microphone sensor installation azimuth angle and the highest order of the mode, an orthogonal Fourier transfer matrix is ​​constructed to establish a compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points.

[0070] In the fourth step S4, the amplitude of the compressor near-stall characteristic mode reconstruction model is solved based on the block orthogonal matching pursuit algorithm, so as to realize the monitoring of compressor near-stall operating conditions based on acoustic features.

[0071] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near-stall state, the first step S1 includes:

[0072] Step S101: Calculate the highest circumferential modal order, where the modal order of the circumferential acoustic mode is... , This indicates the order of pressure pulsations caused by unsteady aerodynamic forces resulting from the interference of the fan's rotation and stationary motion. Represent a non-negative integer to determine the highest circumferential modal order. The range of measurable states is determined as follows: ;

[0073] Step S102: Set the number of microphones in the virtual ring. The installation location and number of microphones were sampled by Nyquist-Shannon. With the highest modal order The relationship is The virtual microphone array is evenly arranged in the circumferential direction, and the spacing between each virtual grid point is [missing information]. ;

[0074] Step S103: Randomly install K microphones on the casing wall in the circumferential direction, where K is approximately the number of microphones sampled by Nyquist sampling. 50%, installation angle .

[0075] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near-stall state, the second step S2 includes the following steps:

[0076] Step S201: Determine the sampling frequency to establish the window length for the acoustic time-domain signal segment. Add a rectangular window to the multi-channel sound pressure time-domain signal, with a window length of... and , For any integer, This is the sampling frequency of the microphone sensor; and every [time] during continuous sampling... Windowing is applied to the sound pressure time-domain signal of a multi-channel signal, and the overlap length of each window is... ;

[0077] Step S202: Process the current acoustic time-domain signal segment Perform a Fast Fourier Transform on the K sound pressure time-domain signals: ,in, This represents the current acoustic time-domain signal segment matrix of the sound pressure level time-domain signal obtained from a non-uniform microphone array measurement. The dimension of the current time-domain signal segment matrix is... , Represents the Discrete Fourier Transform. For multi-channel arrays in the first The frequency domain matrix of an acoustic time-domain signal segment, with dimensions of , , means as follows:

[0078]

[0079] in ,

[0080] Step S203: Based on the obtained frequency domain matrix Plot the sound pressure spectrum obtained from sensors at different locations and observe whether there are multiple frequency shifts that are divided by integer multiples. The non-synchronous single-tone characteristic frequency outside, that is, exists If it does not exist, repeat step S2; if it exists, further monitor the near-stall condition.

[0081] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near-stall state, the third step S3 includes:

[0082] Step S301: Establish asynchronous single-tone characteristic frequencies Multi-channel microphone continuous frequency domain observation matrix :

[0083]

[0084] in, Represents a segment of acoustic time-domain signal The asynchronous single-tone characteristic frequency at the k-th measurement point of the non-uniform array Amplitude,

[0085] Step S302: Construct the orthogonal Fourier transfer matrix ,

[0086]

[0087] in ,

[0088] Step S303: Define the acoustic mode matrix A near-stall characteristic mode reconstruction model for the compressor was established.

[0089]

[0090] in, For the first matrix row vectors The number of dominant modes of interest. It is a structurally sparse norm and has:

[0091] ,

[0092] This is an indicator function that returns 1 when the condition is true.

[0093] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near-stall state, the fourth step S4 includes:

[0094] Step S401: Set the residual matrix Index sequence number set Structural sparsity The number of dominant modes of interest, the number of iterations. ;

[0095] Step S402, Iteration Count Update ;

[0096] Step S403: Calculate the support set index ;

[0097] Step S404: Update the index sequence set ;

[0098] Step S405: Calculate the acoustic mode amplitude vector of the corresponding support set.

[0099] ;

[0100] Step S406: Update the residual matrix ;

[0101] Step S407: Check the number of iterations Has the structural sparsity been achieved? If the target value is reached, the amplitude monitoring result of the compressor near-stall characteristic mode reconstruction model will be output; otherwise, return to step S402.

[0102] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near stall state, the compressor is an aero-engine compressor.

[0103] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near stall state, the compressor includes a fan structure.

[0104] In a preferred embodiment of the method for continuous acoustic signature monitoring of a compressor near-stall state, it includes:

[0105] The sound field measurement module is used to measure the number of rotor blades of the compressor. With the number of stator blades Calculate the highest circumferential modal order of the compressor With the number of microphones At the entrance, a predetermined number of microphone measurement points are randomly selected to form a microphone array, and multi-channel sound pressure time-domain signals are synchronously acquired.

[0106] The spectrum analysis module is used to add rectangular windows to the multi-channel sound pressure time-domain signal, construct acoustic time-domain signal segments, and perform fast Fourier transform on the multi-channel acoustic time-domain signal segments within the current time-domain window to generate multiple sound pressure spectra. It then determines whether there are multiple asynchronous single-tone characteristic frequencies in the sound pressure spectrum that are not integer multiples of the current frequency.

[0107] The module is used to construct a multi-channel microphone continuous frequency domain observation matrix for asynchronous single-tone characteristic frequencies, construct an orthogonal Fourier transfer matrix based on the microphone sensor installation azimuth angle and the highest order of the mode, and establish a compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points.

[0108] The monitoring module uses a block orthogonal matching pursuit algorithm to solve the amplitude of the compressor near-stall characteristic mode reconstruction model, thereby realizing compressor near-stall condition monitoring based on acoustic signature features.

[0109] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.

[0110] An electronic device, the electronic device comprising:

[0111] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0112] The processor implements the method when executing the program.

[0113] In one embodiment, Figure 1 This is a flowchart of the continuous acoustic signature monitoring method for compressor near-stall state based on structural sparse features, which is the result of this invention. By arranging a small number of non-uniform ring microphone measurement points at the compressor, multi-channel acoustic signals of the compressor are synchronously acquired, and acoustic time-domain signal segments are constructed. The compressor operating state is determined based on the fast Fourier transform results within the current time-domain window. A compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points is established, and the amplitude of the compressor near-stall characteristic acoustic mode is solved based on the block orthogonal matching pursuit algorithm, so as to realize the monitoring of compressor near-stall operating conditions based on acoustic signature features.

[0114] The schematic diagram of the aircraft engine fan structure used in the test is shown below. Figure 2 As shown, the number of first-stage rotor blades of the aero-engine fan Number of guide vanes According to the formula for calculating the modal order of fan single-tone noise... , usually take The order of the pressure pulsation caused by the unsteady aerodynamic forces resulting from the fan's rotation and stationary interference is 1. In this case, we take... Focus on the highest modal order The measurable number is determined to be the modal range. Number of measurable modes ;

[0115] Microphones were randomly selected at a small number of circumferential locations at the pipe inlet. The number of microphones... The Nyquist-Shannon sampling theorem must be satisfied, i.e. In this embodiment, we take Number of measurement points in a non-uniform ring array The installation angle is ;

[0116] The acoustic pressure signal of an aero-engine compressor is measured using a circular acoustic array, and the sampling frequency of each sensor is... The acoustic time-domain signal segment window length is determined based on the sampling frequency of 20000Hz. Data points, and during continuous sampling, the multi-channel signal is windowed every 1024 sampling points, i.e., the overlap length of each window. ;

[0117] A fast Fourier transform is performed on the sound pressure signals measured by 32 evenly distributed microphones in the current time domain signal segment to obtain the frequency domain matrix. Frequency domain matrix The size is Plot the spectrum of the signals measured by each sensor, with the installation location as the reference point. Taking the microphone signal as an example, at this time the compressor speed is 9200 r / min and the frequency is 153.33 Hz. Its spectrum near the stall point is as follows: Figure 3 As shown, the horizontal axis EO represents the frequency multiple, the vertical axis represents the sound pressure level, the black markings represent the single-tone frequencies that are integer multiples of the frequency, and the red markings represent the single-tone frequencies that are not integer multiples of the frequency, i.e., the anomalous single-tone characteristic frequencies.

[0118] Constructing a compressed sensing matrix ,in The installation angle for the k-th sensor is... Let j be the modal order of the j-th circumferential modal wave, with magnitude . .by Figure 3 Taking the two asynchronous frequencies of 14.7EO and 15.7EO as examples, we focus on the first two dominant modes at each asynchronous frequency and define... Establish a sparse estimation model for acoustic modes with few measurement points at various frequencies:

[0119]

[0120] right Figure 3 Acoustic modal decomposition was performed at the abnormal peak frequency, and acoustic modal spectra at each characteristic frequency were plotted. The acoustic modal spectra at 14.7EO are shown in Figure 4(a), where the acoustic modal orders were observed to be +9 and -8; the acoustic modal spectra at 15.7EO are shown in Figure 4(b), where the acoustic modal orders were observed to be +10 and -7.

[0121] The sparse model was solved using the block orthogonal matching pursuit algorithm. For comparison, a more advanced L1 norm sparse constraint was introduced, and the amplitudes of two characteristic modes at 14.7EO and 15.7EO were monitored. The monitoring results for the +9 and -8 modes at 14.7EO are shown in Figures 5(a) and 5(b); the monitoring results for the +10 and -7 modes at 15.7EO are shown in Figures 6(a) and 6(b). In these figures, the black line represents the modal reference amplitude (FSA) obtained from a uniform array of numerous measurement points, the red line represents the modal amplitude (BOMP) measured using the block orthogonal matching pursuit model described in this patent, and the blue line represents the modal amplitude (SGL) measured using the advanced L1 norm sparse constraint method. It is evident that the modal amplitude monitoring results obtained from the block orthogonal matching pursuit model described in this patent are closer to the monitoring results of a uniform array composed of a large number of measuring points. Clearly, the proposed compressor near-stall modal monitoring method based on block orthogonal matching pursuit is significantly superior to the L1 norm sparse constraint method. Furthermore, the proposed continuous acoustic signature monitoring method for compressor near-stall states based on structural sparse features can accurately reconstruct the time history of characteristic modal amplitudes at each frequency using fewer measuring points, observing a significant increase in the amplitude of each characteristic mode at approximately 6 seconds. This provides important reference value for monitoring and early warning of aerodynamic stability in compression systems.

[0122] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A method for continuous acoustic signature monitoring of a compressor near stall state, characterized in that, Includes the following steps: In the first step (S1), based on the number of rotor blades of the compressor... With the number of stator blades Calculate the highest circumferential modal order of the compressor With the number of microphones At the entrance, a predetermined number of microphone measurement points are randomly selected to form a microphone array, and multi-channel sound pressure time-domain signals are synchronously acquired. In the second step (S2), a rectangular window is added to the multi-channel acoustic pressure time-domain signal to construct an acoustic time-domain signal segment. A fast Fourier transform is performed on the multi-channel acoustic time-domain signal segment within the current time-domain window to generate multiple sound pressure spectra. It is then determined whether there are multiple asynchronous single-tone characteristic frequencies in the sound pressure spectrum that are not integer multiples of the current rotation frequency. If not, it indicates that the compressor is in normal working condition, and the second step (S2) is repeated. If so, further monitoring of near-stall conditions is carried out. In the third step (S3), a multi-channel microphone continuous frequency domain observation matrix for asynchronous single-tone characteristic frequencies is constructed. Based on the microphone sensor installation azimuth angle and the highest order of the mode, an orthogonal Fourier transfer matrix is ​​constructed to establish a compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points. In the fourth step (S4), the amplitude of the compressor near stall characteristic mode reconstruction model is solved based on the block orthogonal matching pursuit algorithm, so as to realize the monitoring of compressor near stall condition based on acoustic features. in, The first step (S1) includes, Step S101: Calculate the highest circumferential modal order, where the modal order of the circumferential acoustic mode is... , This indicates the order of pressure pulsations caused by unsteady aerodynamic forces resulting from the interference of the fan's rotation and stationary motion. Represent a non-negative integer to determine the highest circumferential modal order. The measurable modal range is determined to be: ; Step S102: Set the number of microphones in the virtual ring. The installation location, based on the Nyquist-Shannon sampling theory, and the number of microphones... With the highest modal order The relationship is The virtual microphone array is evenly arranged in the circumferential direction, and the spacing between each virtual grid point is [missing information]. ; Step S103: Randomly install K sensors on the casing wall in the circumferential direction, where K is the number of microphones sampled by Nyquist sampling. 50%, installation angle ; The second step (S2) includes the following steps: Step S201: Determine the sampling frequency to establish the window length for the acoustic time-domain signal segment. Add a rectangular window to the multi-channel sound pressure time-domain signal, with a window length of... and , For any integer, This is the sampling frequency of the microphone sensor; and every [time] during continuous sampling... Windowing is applied to the sound pressure time-domain signal of a multi-channel signal, and the overlap length of each window is... ; Step S202: Process the current acoustic time-domain signal segment Perform a Fast Fourier Transform on the K sound pressure time-domain signals: ,in, This represents the current acoustic time-domain signal segment matrix of the sound pressure level time-domain signal obtained from a non-uniform microphone array measurement. The dimension of the current time-domain signal segment matrix is... , Represents the Discrete Fourier Transform. For multi-channel arrays in the first The frequency domain matrix of an acoustic time-domain signal segment, with dimensions of , , means as follows: ， in , Step S203: Based on the obtained frequency domain matrix Plot the sound pressure spectrum obtained from sensors at different locations and observe whether there are multiple frequency shifts that are divided by integer multiples. The non-synchronous single-tone characteristic frequency outside, that is, exists If not, repeat step 2 (S2); if it exists, further monitor the near-stall condition. The third step (S3) includes, Step S301: Establish asynchronous single-tone characteristic frequencies Multi-channel microphone continuous frequency domain observation matrix : ， in, Represents a segment of acoustic time-domain signal The asynchronous single-tone characteristic frequency at the k-th measurement point of the non-uniform array Amplitude, Step S302: Construct the orthogonal Fourier transfer matrix , ， in , Step S303: Define the acoustic mode matrix A near-stall characteristic mode reconstruction model for the compressor was established. ， in, For the first matrix row vectors The number of dominant modes of interest. It is a structurally sparse norm and has: ， This is an indicator function that returns 1 when the condition is true; The fourth step (S4) includes, Step S401: Set the residual matrix Index sequence number set Structural sparsity The number of dominant modes of interest, the number of iterations. ; Step S402, Iteration Count Update ; Step S403: Calculate the support set index ; Step S404: Update the index sequence set ; Step S405: Calculate the acoustic mode amplitude vector of the corresponding support set. ； Step S406: Update the residual matrix ; Step S407: Check the number of iterations Has the structural sparsity been achieved? If the target value is reached, the amplitude monitoring result of the compressor near-stall characteristic mode reconstruction model will be output; otherwise, return to step S402.

2. The method for continuous acoustic signature monitoring of a compressor near-stall state according to claim 1, characterized in that, The compressor is the compressor of an aircraft engine.

3. The method for continuous acoustic signature monitoring of a compressor near stall state according to claim 1, characterized in that, The compressor includes a fan structure.

4. A fan blade synchronous vibration identification system implementing the method of any one of claims 1-3, characterized in that, It includes: The sound field measurement module is used to measure the number of rotor blades of the compressor. With the number of stator blades Calculate the highest circumferential modal order of the compressor With the number of microphones At the entrance, a predetermined number of microphone measurement points are randomly selected to form a microphone array, and multi-channel sound pressure time-domain signals are synchronously acquired. The spectrum analysis module is used to add rectangular windows to the multi-channel sound pressure time-domain signal, construct acoustic time-domain signal segments, and perform fast Fourier transform on the multi-channel acoustic time-domain signal segments within the current time-domain window to generate multiple sound pressure spectra. It then determines whether there are multiple asynchronous single-tone characteristic frequencies in the sound pressure spectrum that are not integer multiples of the current frequency. The module is used to construct a multi-channel microphone continuous frequency domain observation matrix for asynchronous single-tone characteristic frequencies, construct an orthogonal Fourier transfer matrix based on the microphone sensor installation azimuth angle and the highest order of the mode, and establish a compressor near-stall characteristic mode reconstruction model under non-uniformity and few measurement points. The monitoring module uses a block orthogonal matching pursuit algorithm to solve the amplitude of the compressor near-stall characteristic mode reconstruction model, thereby realizing compressor near-stall condition monitoring based on acoustic signature features.

5. A computer storage medium, characterized in that, The storage medium includes computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-3.

6. An electronic device, characterized in that, The electronic device includes: Memory, processor, and computer programs stored in memory and executable on the processor, wherein, When the processor executes the program, it implements the method as described in any one of claims 1-3.

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