Acoustic diagnosis method, system, medium and equipment for corner drop fault of aero-engine fan blade

Abstract

An aero-engine fan blade corner loss fault acoustic diagnosis method, system, medium and equipment, in the method, the microphone array layout is determined through the fan model parameters of the aero-engine, the multi-channel sound pressure time domain signals of the aero-engine fan are synchronously collected through the microphone array; the multi-channel sound pressure time domain signals are sequentially arranged to construct a time domain signal matrix; the time domain signal matrix is converted into a frequency domain signal matrix by using a fast Fourier transform algorithm, the frequency spectrum diagram of each channel is obtained, whether an abnormal single tone frequency peak value other than the blade passing frequency appears is monitored through the frequency spectrum diagram, the acoustic modal spectrum diagram of the abnormal single tone frequency peak value is obtained through a single frequency acoustic modal decomposition method, whether the blade corner loss fault characteristic with the frequency component being 1 times the rotation frequency and the acoustic modal order being 1 appears in the acoustic modal spectrum diagram is monitored to judge whether the fan appears the blade corner loss fault under the subsonic working condition or the supersonic working condition.

Description

Technical Field

[0001] This invention relates to the field of aero-engine fault testing technology, and in particular to an acoustic diagnostic method, system, medium, and equipment for aero-engine fan blade cornering fault. Background Technology

[0002] With the rapid development of the aviation industry, the reliability and safety of aircraft engines have become crucial factors that cannot be ignored in air transport. In recent years, the damage caused by bird strikes to aircraft engines has increasingly attracted widespread attention. Bird strikes refer to the phenomenon of birds colliding with aircraft engines during flight, which not only threatens flight safety but can also lead to serious structural damage and economic losses. In bird strike accidents, damage to engine blades, especially fan blades, is particularly prominent, with blade chipping being a common form of damage.

[0003] Fan blades are critical components in aero engines that come into direct contact with airflow. Their design and manufacturing quality directly affect the engine's performance, efficiency, and safety. When a bird strike occurs, the impact force of the bird acts instantly on the blade surface, causing material to fracture or detach from the blade's edge, resulting in a so-called "angle breakage" failure. Angle breakage not only alters the blade's aerodynamic shape, affecting the engine's aerodynamic efficiency, but can also lead to blade imbalance, causing vibration and noise problems, and in severe cases, even engine failure.

[0004] For engine blade deflection caused by bird strikes, accurate identification and analysis of fault characteristics are crucial for the maintenance and repair of aero engines. Traditional fault detection methods mainly rely on visual inspection, non-destructive testing techniques, and vibration analysis; however, these methods often have limitations when dealing with complex blade deflection faults.

[0005] 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

[0006] This invention provides an acoustic diagnostic method, system, medium, and device for blade cornering faults in aero-engine fans. It employs a uniformly arranged ring acoustic array to measure acoustic signals, achieving time-domain-frequency-wavenumber domain conversion of the acoustic field signal. This fully extracts the acoustic signal fault characteristics when blade cornering faults occur, resulting in a more effective blade cornering detection index, acoustic pattern map, which identifies the acoustic signal fault characteristics in blade cornering faults.

[0007] The objective of this invention is achieved through the following technical solution: an acoustic diagnostic method for aero-engine fan blade angle-drop faults includes:

[0008] In the first step, the microphone array layout is determined using the fan model parameters of the aero-engine, based on the number of fan rotor blades. With the number of stator blades Calculate the static-to-static interference mode order of fan single-tone noise Based on the order of the static interference mode The maximum modal order determines the acoustic modal monitoring range and the number of microphones. and the circumferential mounting angle of the microphone ;

[0009] In the second step, through the number of microphones and the installation angle of the microphone Construct a microphone array; synchronously acquire the multi-channel sound pressure time-domain signals of the aero-engine fan using the microphone array; construct a time-domain signal matrix by arranging the multi-channel sound pressure time-domain signals in sequence;

[0010] In the third step, the fast Fourier transform algorithm is used to transform the time domain signal matrix into the frequency domain signal matrix to obtain the spectrum of each channel. The spectrum is monitored to see if there are any abnormal single-tone frequency peaks other than the blade passing frequency. If there are none, it means that the fan is in normal working condition. If there are, proceed to the fourth step.

[0011] In the fourth step, the acoustic modal spectrum of the abnormal single-tone frequency peak is obtained by the single-frequency acoustic modal decomposition method. By monitoring whether the blade cornering fault feature with a frequency component of 1 times the rotational frequency and an acoustic modal order of 1 appears in the acoustic modal spectrum, it is determined whether the fan has a blade cornering fault under subsonic or supersonic conditions.

[0012] The acoustic diagnostic method for aero-engine fan blade angle loss fault also includes,

[0013] In the fifth step, the spectrum of the sound pressure time-domain signal is obtained through continuous broadband acoustic mode decomposition. The horizontal axis of the spectrum represents the frequency analysis range, and the vertical axis represents the acoustic mode monitoring range. The depth of the color represents the amplitude; the darker the color, the larger the amplitude. When the fan is operating under subsonic conditions and exhibits blade corner-dropping fault characteristics, the main components of the spectrum are single-tone components such as blade passage frequency and rotational frequency, along with broadband noise components. In the wavenumber domain, the dominant mode order of the rotational frequency sound source is 1. When the fan is operating under supersonic conditions and exhibits blade corner-dropping fault characteristics, the main components of the spectrum are blade passage frequency and rotational frequency components. The broadband noise disappears, and harmonics and modulation frequencies that are integer multiples of the rotational frequency appear. In the wavenumber domain, abnormal rotational frequency sound sources and harmonic and modulation phenomena are observed on the spectrum.

[0014] In the acoustic diagnosis method for blade angle loss fault in aero-engines, in the first step, the order of the fan's rotation-to-station interference modes... for, ,in, This indicates the order of pressure pulsations caused by unsteady aerodynamic forces resulting from the interference of the fan's rotation and stationary motion. Represents an integer, determining the number of microphones in the microphone array. And in the microphone installation angle,

[0015] When using a uniform acoustic array layout, calculate the number of sensors required for modal detection based on the Nyquist sampling theorem. Its order with the static interference mode The relationship is: When using a non-uniform acoustic array layout with few measurement points, in a virtually uniform layout... Randomly selected from the positions Install sensors at various locations. ;

[0016] When using a uniform acoustic array layout scheme, A circular acoustic array is composed of several sensors, with a spacing between the sensors being [missing information]. The microphone is installed at an angle of 100°. i = [ i 1 , i 2 , … , i n ] ,in , , And so on; when using a non-uniform acoustic array layout with few measurement points, the microphone installation angle... Random selection i = [ i k 1 , i k 2 , … , i k k ] .

[0017] In the aforementioned acoustic diagnostic method for aero-engine fan blade angle loss fault, the second step includes the following steps:

[0018] S201. The sound pressure signal of the aircraft engine fan is measured using a ring acoustic array. The measured sound pressure time-domain signal is: p i ( t ) = [ p i ( t 1 ) , p i ( t 2 ) , ⋯ , p i ( t r ) ] The length of the time-domain signal sequence measured by a single microphone is [length missing]. subscript These are microphones positioned at corresponding installation angles;

[0019] S202. Based on the sound pressure time-domain signals measured by the microphones at different installation angles, construct a time-domain signal matrix. , of which elements express The first measurement obtained by the microphone at the corresponding installation angle position Signal,

[0020] p = [ p 1 ( t 1 ) p 2 ( t 1 ) p 3 ( t 1 ) p 4 ( t 1 ) ⋯ p n ( t 1 ) p 1 ( t 2 ) p 2 ( t 2 ) p 3 ( t 2 ) p 4 ( t 2 ) ⋯ p n ( t 2 ) ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ p 1 ( t r ) p 2 ( t r ) p 3 ( t r ) p 4 ( t r ) ⋯ p n ( t r ) ] When using a uniform acoustic array layout, the time-domain signal matrix The size is When using a non-uniform acoustic array layout with few measurement points, the time-domain signal matrix... The size is .

[0021] In the aforementioned acoustic diagnostic method for aero-engine fan blade corner-dropping faults, the third step includes:

[0022] S301, Measure the time-domain signal matrix of the circular acoustic array. The frequency domain matrix is ​​obtained by performing a Fourier transform on each column. , of which elements express The signal measured by the microphone at the corresponding installation angle position is in Amplitude at frequency, The length is According to the Nyquist sampling theorem, ,

[0023] P = [ P 1 ( oh 1 ) P 2 ( oh 1 ) P 3 ( oh 1 ) P 4 ( oh 1 ) ⋯ P n ( oh 1 ) P 1 ( oh 2 ) P 2 ( oh 2 ) P 3 ( oh 2 ) P 4 ( oh 2 ) ⋯ P n ( oh 2 ) ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ P 1 ( oh l ) P 2 ( oh l ) P 3 ( oh l ) P 4 ( oh l ) ⋯ P n ( oh l ) ] In a uniform acoustic array layout, the frequency domain matrix The size is In a non-uniform acoustic array layout, the frequency domain matrix The size is ,

[0024] S302, According to the frequency domain matrix Plot the spectrum of signals measured by sensors at different installation angles and observe whether there are frequencies other than those of the passing blades. In addition to the abnormal single-tone frequency peaks outside the rotation frequency, are there any Otsu's frequency and wideband noise components under subsonic conditions? In supersonic conditions, are there any other harmonic components and modulation phenomena besides Otsu's frequency?

[0025] In the aforementioned acoustic diagnostic method for aero-engine fan blade corner-drop faults, the fourth step includes:

[0026] S401, When using a uniform acoustic array layout, the subscript... The microphone at the corresponding installation angle position at the predetermined frequency Frequency domain signal at It can be viewed as a linear superposition of different circumferential acoustic modes, i.e. Construct the transformation matrix The format is as follows:

[0027] P = [ e i − m i 1 e i ( − m + 1 ) i 1 e i ( − m + 2 ) i 1 e i ( − m + 3 ) i 1 ⋯ e i m i 1 e i − m i 2 e i ( − m + 1 ) i 2 e i ( − m + 2 ) i 2 e i ( − m + 3 ) i 2 ⋯ e i m i 2 ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ e i − m i n e i ( − m + 1 ) i n e i ( − m + 2 ) i n e i ( − m + 3 ) i n ⋯ e i m i n ] ,

[0028] When using a non-uniform acoustic array layout with few measurement points, an observation matrix is ​​constructed based on randomly selected sensor installation angles. Observation matrix Size is ,

[0029] f = [ 0 1 0 0 ⋯ 0 0 0 0 1 ⋯ 0 ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ 0 0 0 0 ⋯ 0 ] ,

[0030] S402. When using a uniform acoustic array layout, the frequency domain matrix... Perform a spatial Fourier transform to obtain the wavenumber domain matrix. , ,in Frequency domain matrix The transpose of the matrix, ,in Represents the transformation matrix The false rebellion,

[0031] When using a non-uniform acoustic array layout with few measurement points, the compressed sensing model is: sparse dictionary Composed of orthogonal Fourier transform bases, For the sensing matrix, sparsely reconstruct the wavenumber domain matrix based on the compressed sensing model. ;

[0032] S403. Based on the obtained wavenumber domain matrix Plot the acoustic modal spectrum at 1x RPM; observe whether the dominant mode order of the acoustic modal spectrum at 1x RPM is 1 under subsonic or supersonic conditions.

[0033] In the aforementioned acoustic diagnostic method for aero-engine fan blade angle loss fault, the fifth step includes:

[0034] S501, the wavenumber domain matrix Extending to the full frequency domain, wavenumber domain matrix Size expansion to , of which elements Indicates frequency First The amplitude of the step sound mode,

[0035] P ˜ = [ P ˜ − m ( oh 1 ) P ˜ − m ( oh 2 ) P ˜ − m ( oh 3 ) P ˜ − m ( oh 4 ) ⋯ P ˜ − m ( oh l ) P ˜ − m + 1 ( oh 1 ) P ˜ − m + 1 ( oh 2 ) P ˜ − m + 1 ( oh 3 ) P ˜ − m + 1 ( oh 4 ) ⋯ P ˜ − m + 1 ( oh l ) ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ P ˜ m ( oh 1 ) P ˜ m ( oh 2 ) P ˜ m ( oh 3 ) P ˜ m ( oh 4 ) ⋯ P ˜ m ( oh l ) ] ,

[0036] S502, According to the wavenumber domain matrix Plot the spectrum diagram. Under subsonic conditions, observe whether there are abnormal sound source terms with a dominant mode order of 1 times the rotation frequency and broadband noise abnormal sound source terms. Under supersonic conditions, observe whether there are frequency harmonics and modulation phenomena as well as abnormal sound source terms with rotation frequency. Define all features in the spectrum, acoustic mode spectrum diagram and spectrum diagram as the acoustic signature features of fan blade corner drop fault, and judge whether the fan has a blade corner drop fault based on this.

[0037] A diagnostic system implementing the method includes:

[0038] The sound field measurement module includes a sound array measurement submodule and a data acquisition system submodule, which are used to measure the sound field information propagated in the pipe to the sound array installation location when the fan is working;

[0039] Foreign object launching device, used to launch hard particles and soft simulated fan blades into aero engines;

[0040] The spectrum analysis module is used to transform the time-domain sound field signal at the location of the acoustic array to the frequency domain and detect whether there are any abnormal frequencies other than the frequency of the blades passing through.

[0041] The acoustic mode decomposition module is used to transform the acoustic field information from the frequency domain to the wavenumber domain and detect whether blade cornering fault characteristics appear in the wave spectrum.

[0042] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.

[0043] An electronic device, the electronic device comprising:

[0044] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0045] The processor implements the method when executing the program.

[0046] Compared with existing technologies, this invention has the following advantages: The invention utilizes acoustic information for fault diagnosis of fan blade cornering faults, which is more sensitive and adaptable than traditional methods using vibration signals and interstage pressure signals. It obtains the fan blade cornering fault characteristics in the frequency domain using Fast Fourier Transform, resulting in clear features and a simple process. It also obtains the fan blade cornering fault characteristics in the wavenumber domain using Spatial Fourier Transform, offering high sensitivity and adaptability to different speeds. Even with limited testing resources, a non-uniform layout scheme can be used to extract acoustic signature features of aero-engine fan cornering faults. Using the spectrum, modal spectrum, and abnormal components in the wave spectrum of the acoustic signal as acoustic signature features of the fan cornering fault significantly improves diagnostic accuracy. Attached Figure Description

[0047] 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.

[0048] In the attached diagram:

[0049] Figure 1 This is the flowchart of this disclosure;

[0050] Figure 2 This is a schematic diagram of a diagnostic system for a fan blade corner-drop fault diagnosis method based on acoustic signature features, provided in one embodiment of this disclosure.

[0051] Figures 3(a) to 3(c) are spectrum diagrams of the fan blade angle drop fault under different operating conditions and without fault provided in an embodiment of the present disclosure; wherein, Figure 3(a) is the spectrum diagram when no fault occurs, Figure 3(b) is the spectrum diagram when the fan blade angle drop fault occurs under subsonic operating conditions, and Figure 3(c) is the spectrum diagram when the fan blade angle drop fault occurs under supersonic operating conditions.

[0052] Figures 4(a) and 4(b) are acoustic modal spectra at the rotor frequency and the blade passing frequency provided in an embodiment of the present disclosure; wherein, Figure 4(a) is the acoustic modal spectra at the rotor frequency and Figure 4(b) is the acoustic modal spectra at the blade passing frequency;

[0053] Figures 5(a) to 5(c) are spectrum diagrams of the fan blade angle-dropping fault under different speed conditions and without fault, provided in an embodiment of the present disclosure; wherein, Figure 5(a) is the spectrum diagram without fault, Figure 5(b) is the spectrum diagram of the fan blade angle-dropping fault under subsonic conditions, and Figure 5(c) is the spectrum diagram of the fan blade angle-dropping fault under supersonic conditions.

[0054] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0055] 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.

[0056] 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.

[0057] 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.

[0058] like Figure 1 As shown in Figure 5(c), the acoustic diagnosis method for aero-engine fan blade cornering fault includes the following steps:

[0059] In the first step S1, the microphone array layout is determined using the fan model parameters of the aero-engine, based on the number of fan rotor blades. With the number of stator blades Calculate the static-to-static interference mode order of fan single-tone noise Based on the order of the static interference mode The maximum modal order determines the acoustic modal monitoring range and the number of microphones. and the circumferential mounting angle of the microphone ;

[0060] In the second step S2, through the number of microphones and the installation angle of the microphone Construct a microphone array; synchronously acquire the multi-channel sound pressure time-domain signals of the aero-engine fan using the microphone array; construct a time-domain signal matrix by arranging the multi-channel sound pressure time-domain signals in sequence;

[0061] In the third step S3, the fast Fourier transform algorithm is used to transform the time domain signal matrix into the frequency domain signal matrix to obtain the spectrum of each channel. The spectrum is monitored to see if there are any abnormal single-tone frequency peaks other than the blade passing frequency. If there are none, it means that the fan is in normal working condition. If there are, proceed to the fourth step S4.

[0062] In the fourth step S4, the acoustic mode spectrum of the abnormal single-tone frequency peak is obtained by the single-frequency acoustic mode decomposition method. By monitoring whether the blade cornering fault feature with a frequency component of 1 times the rotational frequency and an acoustic mode order of 1 appears in the acoustic mode spectrum, it is determined whether the fan has a blade cornering fault under subsonic or supersonic conditions.

[0063] In a preferred embodiment of the acoustic diagnosis method for aero-engine fan blade corner-drop fault, it further includes,

[0064] In step S5, the spectrum of the sound pressure time-domain signal is obtained through continuous broadband acoustic mode decomposition. The horizontal axis of the spectrum represents the frequency analysis range, and the vertical axis represents the acoustic mode monitoring range. The intensity of the color represents the amplitude; the darker the color, the larger the amplitude. When the fan is operating under subsonic conditions and exhibits blade corner-dropping fault characteristics, the main components of the spectrum are single-tone components such as blade passage frequency and rotational frequency, along with broadband noise components. In the wavenumber domain, the dominant mode order of the rotational frequency sound source is 1. When the fan is operating under supersonic conditions and exhibits blade corner-dropping fault characteristics, the main components of the spectrum are blade passage frequency and rotational frequency components. The broadband noise disappears, and harmonics and modulation frequencies that are integer multiples of the rotational frequency appear. In the wavenumber domain, abnormal rotational frequency sound sources and harmonic and modulation phenomena are observed on the spectrum.

[0065] In a preferred embodiment of the acoustic diagnosis method for blade angle loss fault in aero-engine fans, in the first step S1, the order of the fan's rotation-to-station interference modes... for, ,in, This indicates the order of pressure pulsations caused by unsteady aerodynamic forces resulting from the interference of the fan's rotation and stationary motion. Represents an integer, determining the number of microphones in the microphone array. And in the microphone installation angle,

[0066] When using a uniform acoustic array layout, calculate the number of sensors required for modal detection based on the Nyquist sampling theorem. Its order with the static interference mode The relationship is: When using a non-uniform acoustic array layout with few measurement points, in a virtually uniform layout... Randomly selected from the positions Install sensors at various locations. ;

[0067] When using a uniform acoustic array layout scheme, A circular acoustic array is composed of several sensors, with a spacing between the sensors being [missing information]. The microphone is installed at an angle of 100°. i = [ i 1 , i 2 , … , i n ] ,in , , And so on; when using a non-uniform acoustic array layout with few measurement points, the microphone installation angle... Random selection i = [ i k 1 , i k 2 , … , i k k ] .

[0068] In a preferred embodiment of the acoustic diagnosis method for aero-engine fan blade corner-drop fault, the second step S2 includes the following steps:

[0069] S201. The sound pressure signal of the aircraft engine fan is measured using a ring acoustic array. The measured sound pressure time-domain signal is: p i ( t ) = [ p i ( t 1 ) , p i ( t 2 ) , ⋯ , p i ( t r ) ] The length of the time-domain signal sequence measured by a single microphone is [length missing]. subscript These are microphones positioned at corresponding installation angles;

[0070] S202. Based on the sound pressure time-domain signals measured by the microphones at different installation angles, construct a time-domain signal matrix. , of which elements express The first measurement obtained by the microphone at the corresponding installation angle position Signal,

[0071] p = [ p 1 ( t 1 ) p 2 ( t 1 ) p 3 ( t 1 ) p 4 ( t 1 ) ⋯ p n ( t 1 ) p 1 ( t 2 ) p 2 ( t 2 ) p 3 ( t 2 ) p 4 ( t 2 ) ⋯ p n ( t 2 ) ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ p 1 ( t r ) p 2 ( t r ) p 3 ( t r ) p 4 ( t r ) ⋯ p n ( t r ) ] When using a uniform acoustic array layout, the time-domain signal matrix The size is When using a non-uniform acoustic array layout with few measurement points, the time-domain signal matrix... The size is .

[0072] In a preferred embodiment of the acoustic diagnosis method for aero-engine fan blade corner-drop fault, the third step S3 includes:

[0073] S301, Measure the time-domain signal matrix of the circular acoustic array. The frequency domain matrix is ​​obtained by performing a Fourier transform on each column. , of which elements express The signal measured by the microphone at the corresponding installation angle position is in Amplitude at frequency, The length is According to the Nyquist sampling theorem, ,

[0074] P = [ P 1 ( oh 1 ) P 2 ( oh 1 ) P 3 ( oh 1 ) P 4 ( oh 1 ) ⋯ P n ( oh 1 ) P 1 ( oh 2 ) P 2 ( oh 2 ) P 3 ( oh 2 ) P 4 ( oh 2 ) ⋯ P n ( oh 2 ) ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ P 1 ( oh l ) P 2 ( oh l ) P 3 ( oh l ) P 4 ( oh l ) ⋯ P n ( oh l ) ] In a uniform acoustic array layout, the frequency domain matrix The size is In a non-uniform acoustic array layout, the frequency domain matrix The size is ,

[0075] S302, According to the frequency domain matrix Plot the spectrum of signals measured by sensors at different installation angles and observe whether there are frequencies other than those of the passing blades. In addition to abnormal single-tone frequency peaks outside the rotation frequency, are there OCR and wideband noise components in subsonic conditions? In supersonic conditions, are there other harmonic components and modulation phenomena in addition to OCR?

[0076] In a preferred embodiment of the acoustic diagnosis method for aero-engine fan blade corner-drop fault, the fourth step S4 includes:

[0077] S401, When using a uniform acoustic array layout, the subscript... The microphone at the corresponding installation angle position at the predetermined frequency Frequency domain signal at It can be viewed as a linear superposition of different circumferential acoustic modes, i.e. Construct the transformation matrix The format is as follows:

[0078] P = [ e i − m i 1 e i ( − m + 1 ) i 1 e i ( − m + 2 ) i 1 e i ( − m + 3 ) i 1 ⋯ e i m i 1 e i − m i 2 e i ( − m + 1 ) i 2 e i ( − m + 2 ) i 2 e i ( − m + 3 ) i 2 ⋯ e i m i 2 ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ e i − m i n e i ( − m + 1 ) i n e i ( − m + 2 ) i n e i ( − m + 3 ) i n ⋯ e i m i n ] ,

[0079] When using a non-uniform acoustic array layout with few measurement points, an observation matrix is ​​constructed based on randomly selected sensor installation angles. Observation matrix Size is ,

[0080] f = [ 0 1 0 0 ⋯ 0 0 0 0 1 ⋯ 0 ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ 0 0 0 0 ⋯ 0 ] ,

[0081] S402. When using a uniform acoustic array layout, the frequency domain matrix... Perform a spatial Fourier transform to obtain the wavenumber domain matrix. , ,in Frequency domain matrix The transpose of the matrix, ,in Represents the transformation matrix The false rebellion,

[0082] When using a non-uniform acoustic array layout with few measurement points, the compressed sensing model is: sparse dictionary Composed of orthogonal Fourier transform bases, For the sensing matrix, sparsely reconstruct the wavenumber domain matrix based on the compressed sensing model. ;

[0083] S403. Based on the obtained wavenumber domain matrix Plot the acoustic modal spectrum at 1x RPM; observe whether the dominant mode order of the acoustic modal spectrum at 1x RPM is 1 under subsonic or supersonic conditions.

[0084] In a preferred embodiment of the acoustic diagnosis method for aero-engine fan blade corner-drop fault, the fifth step S5 includes:

[0085] S501, the wavenumber domain matrix Extending to the full frequency domain, wavenumber domain matrix Size expansion to , of which elements Indicates frequency First The amplitude of the step sound mode,

[0086] P ˜ = [ P ˜ − m ( oh 1 ) P ˜ − m ( oh 2 ) P ˜ − m ( oh 3 ) P ˜ − m ( oh 4 ) ⋯ P ˜ − m ( oh l ) P ˜ − m + 1 ( oh 1 ) P ˜ − m + 1 ( oh 2 ) P ˜ − m + 1 ( oh 3 ) P ˜ − m + 1 ( oh 4 ) ⋯ P ˜ − m + 1 ( oh l ) ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ P ˜ m ( oh 1 ) P ˜ m ( oh 2 ) P ˜ m ( oh 3 ) P ˜ m ( oh 4 ) ⋯ P ˜ m ( oh l ) ] ,

[0087] S502, According to the wavenumber domain matrix Plot the spectrum diagram. Under subsonic conditions, observe whether there are abnormal sound source terms with a dominant mode order of 1 times the rotation frequency and broadband noise abnormal sound source terms. Under supersonic conditions, observe whether there are frequency harmonics and modulation phenomena as well as abnormal sound source terms with rotation frequency. Define all features in the spectrum, acoustic mode spectrum diagram and spectrum diagram as the acoustic signature features of fan blade corner drop fault, and judge whether the fan has a blade corner drop fault based on this.

[0088] A diagnostic system implementing the method includes:

[0089] The sound field measurement module includes a sound array measurement submodule and a data acquisition system submodule, which are used to measure the sound field information propagated in the pipe to the sound array installation location when the fan is working;

[0090] Foreign object launching device, used to launch hard particles and soft simulated fan blades into aero engines;

[0091] The spectrum analysis module is used to transform the time-domain sound field signal at the location of the acoustic array to the frequency domain and detect whether there are any abnormal frequencies other than the frequency of the blades passing through.

[0092] The acoustic mode decomposition module is used to transform the acoustic field information from the frequency domain to the wavenumber domain and detect whether blade cornering fault characteristics appear in the wave spectrum.

[0093] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.

[0094] An electronic device, the electronic device comprising:

[0095] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,

[0096] The processor implements the method when executing the program.

[0097] In one embodiment, the sound field information is measured by a microphone array to obtain a time-domain signal matrix. , of which elements express The first angular position measured by the microphone Signal.

[0098] In one embodiment, the method includes,

[0099] The monitoring range of acoustic modes was determined by using model parameters of the aero-engine fan; based on this, the required microphone positions and installation angles for uniform or non-uniform layouts were determined. Bird-swallowing tests were conducted on the fan under subsonic and supersonic operating conditions, and circumferential sound pressure signals were simultaneously measured using an array of microphones. The sound pressure signals were analyzed to obtain the spectrum of each channel; abnormal single-frequency acoustic mode spectra were obtained through single-frequency acoustic mode decomposition; and the array signal spectrum was obtained through continuous broadband acoustic mode decomposition. By monitoring the frequency domain characteristics and circumferential mode characteristics in the fan spectrum, acoustic mode spectrum, and wave spectrum, it was observed whether abnormal frequency peaks appeared in the spectrum other than the blade passage frequency, and whether acoustic mode components with a modal order of 1 or a large number of rotational harmonic components appeared in the acoustic mode spectrum and wave spectrum. These characteristics were defined as the acoustic signature characteristics for diagnosing fan blade corner-drop faults, and based on this, it was determined whether a corner-drop fault occurred when the fan blade was impacted by a foreign object.

[0100] In one embodiment, Figure 1 This is a flowchart of the acoustic diagnosis method for aero-engine fan blade cornering fault based on acoustic signature features, which is the result of this invention. The method calculates the order of the switching-to-stationary interference mode in the single-tone noise of the aero-engine using a fan model. Based on the maximum order of the mode of interest, it determines the acoustic mode monitoring range, the number of microphones, the axial mounting position, and the circumferential mounting angle. A time-domain signal matrix is ​​constructed from the acoustic signals measured by sensors at different circumferential mounting angles. The frequency domain signal matrix obtained by performing a fast Fourier transform on the time-domain signal matrix. The system outputs a spectrum diagram. Under subsonic conditions, it observes whether 1st rotation frequency and broadband noise appear; under supersonic conditions, it observes whether numerous harmonics and modulation phenomena appear. A transformation matrix is ​​constructed based on the characteristics of acoustic mode propagation. Based on the constructed transformation matrix, the acoustic mode spectrum of the peak frequency at 1x RPM is obtained through single-frequency acoustic mode decomposition, and the order of the dominant mode is observed to be 1. The spectrum of the array signal is obtained through continuous broadband acoustic mode decomposition. Under subsonic conditions, it is observed whether there are anomalous sound source terms with a dominant mode order of 1x RPM. Under supersonic conditions, it is observed whether there are a large number of anomalous RPM harmonic sound source terms. The specific steps are as follows:

[0101] Assuming the number of rotor blades of an aircraft engine fan With the number of stator blades 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... Representing the first-order acoustic mode, the calculation yields... ,Pick Representing the second-order acoustic mode, the calculation yields... According to the Nyquist sampling theorem: The required number of sensors Therefore, the number of sensors is selected as follows: ;

[0102] Several sensors are arranged in a uniform circular acoustic array, with a sampling frequency set to 20000 Hz and a spacing between the sensors of [missing information]. The microphone is installed at an angle of 100°. i = [ i 1 , i 2 , … , i n C S ] ,in , , And so on.

[0103] The sound pressure signal of an aircraft engine fan was measured using a circular acoustic array, with each channel measured as follows: The constructed time-domain signal matrix consists of 10 data points. The size is The time-domain signal matrix measured for the circular acoustic array Perform a Fourier transform on each column to transform the signal from the time domain to the frequency domain, obtaining the frequency domain matrix. Frequency domain matrix The size is According to the Nyquist sampling theorem, .

[0104] Based on the obtained frequency domain matrix Plot the spectrum of signals measured by the sensor at different angles, with the installation position as the baseline. Taking the microphone signal as an example, Figure 3(a) is the spectrum diagram when the fan is not malfunctioning. The main component in the spectrum diagram is the frequency of the blades passing through. Figure 3(b) is the spectrum diagram of the fan when the blade angle falls off under subsonic conditions. In addition to the blade passing frequency, there are double frequency and wideband noise components. Figure 3(c) is the spectrum diagram of the fan when the blade angle falls off under supersonic conditions. There are a large number of double frequency and modulation components.

[0105] Elements in the frequency domain matrix Indicates subscript A microphone at a corresponding angular position at a specific frequency The acoustic signal at that location can be considered as a linear superposition of different circumferential acoustic modes, i.e. Therefore, a transformation matrix can be constructed. Size is .

[0106] Single-frequency acoustic mode decomposition at the abnormal peak frequency yields a value of... wavenumber domain matrix And plot the acoustic modal spectrum. When the fan experiences blade corner drop fault, the acoustic mode observed in the acoustic modal spectrum at 1 times the fan speed in Figure 4(a) is as follows: Figure 4(b) shows the acoustic mode observed in the acoustic mode spectrum at the passing frequency of the blade as follows: .

[0107] For frequency domain matrix Performing a spatial Fourier transform yields a value of wavenumber domain matrix wavenumber domain matrix medium elements Indicates frequency First The amplitude of the graded acoustic mode. Based on the obtained wavenumber domain matrix. Spectrum diagrams were plotted. When the fan blades are functioning correctly, the main component of the spectrum in Figure 5(a) is the frequency of blade passage. Under subsonic conditions, the spectrum in Figure 5(b) shows an anomalous sound source term with a dominant mode order of 1 times the rotation frequency and a broadband noise anomalous sound source term; under supersonic conditions, the spectrum in Figure 5(c) shows obvious harmonic and modulation phenomena as well as a rotation frequency anomalous sound source term.

[0108] 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. An acoustic diagnostic method for aero-engine fan blade corner-drop fault, characterized in that, Includes the following steps: In the first step (S1), the microphone array layout is determined using the fan model parameters of the aero-engine, based on the number of fan rotor blades. With the number of stator blades Calculate the static-to-static interference mode order of fan single-tone noise Based on the order of the static interference mode The maximum modal order determines the acoustic modal monitoring range and the number of microphones. and the circumferential mounting angle of the microphone ; In the second step (S2), the number of microphones and the installation angle of the microphone Construct a microphone array; synchronously acquire the multi-channel sound pressure time-domain signals of the aero-engine fan using the microphone array; construct a time-domain signal matrix by arranging the multi-channel sound pressure time-domain signals in sequence; In the third step (S3), the fast Fourier transform algorithm is used to transform the time domain signal matrix into the frequency domain signal matrix to obtain the spectrum of each channel. The spectrum is monitored to see if there are any abnormal single-tone frequency peaks other than the blade passing frequency. If there are none, it means that the fan is in normal working condition. If there are, proceed to the fourth step (S4). In the fourth step (S4), the acoustic mode spectrum of the abnormal single-frequency peak is obtained by the single-frequency acoustic mode decomposition method. By monitoring whether the blade cornering fault feature with a frequency component of 1 times the rotational frequency and an acoustic mode order of 1 appears in the acoustic mode spectrum, it is determined whether the fan has a blade cornering fault under subsonic or supersonic conditions.

2. The acoustic diagnosis method for aero-engine fan blade angle loss fault according to claim 1, characterized in that, Preferred options also include, In the fifth step (S5), the spectrum of the sound pressure time-domain signal is obtained through continuous broadband acoustic mode decomposition. The horizontal axis of the spectrum represents the frequency analysis range, and the vertical axis represents the acoustic mode monitoring range. The depth of the color represents the amplitude, with darker colors indicating larger amplitudes. When the fan is operating under subsonic conditions and exhibits blade corner-dropping fault characteristics, the main components of the spectrum are blade passing frequency and rotational frequency, along with broadband noise components. In the wavenumber domain, the dominant mode order of the rotational frequency sound source is 1. When the fan is operating under supersonic conditions and exhibits blade corner-dropping fault characteristics, the main components of the spectrum are blade passing frequency and rotational frequency components. The broadband noise disappears, and harmonics and modulation frequencies that are integer multiples of the rotational frequency appear. In the wavenumber domain, abnormal rotational frequency sound sources and harmonic and modulation phenomena are observed on the spectrum.

3. The acoustic diagnosis method for aero-engine fan blade angle loss fault according to claim 2, characterized in that, In the first step (S1), the order of the fan's rotation-to-stationary interference mode. for, ,in, This indicates the order of pressure pulsations caused by unsteady aerodynamic forces resulting from the interference of the fan's rotation and stationary motion. Represents an integer, determining the number of microphones in the microphone array. And in the microphone installation angle, When using a uniform acoustic array layout, calculate the number of sensors required for modal detection based on the Nyquist sampling theorem. Its order with the static interference mode The relationship is: When using a non-uniform acoustic array layout with few measurement points, in a virtually uniform layout... Randomly selected from the positions Install sensors at various locations. ; When using a uniform acoustic array layout scheme, A circular acoustic array is composed of several sensors, with a spacing between the sensors being [missing information]. The microphone is installed at an angle of 100°. ,in , , And so on; when using a non-uniform acoustic array layout with few measurement points, the microphone installation angle... Random selection .

4. The acoustic diagnosis method for aero-engine fan blade angle loss fault according to claim 1, characterized in that, The second step (S2) includes the following steps: S201. The sound pressure signal of the aircraft engine fan is measured using a ring acoustic array. The measured sound pressure time-domain signal is: The length of the time-domain signal sequence measured by a single microphone is [length missing]. subscript These are microphones positioned at corresponding installation angles; S202. Based on the sound pressure time-domain signals measured by the microphones at different installation angles, construct a time-domain signal matrix. , of which elements express The first measurement obtained by the microphone at the corresponding installation angle position Signal, When using a uniform acoustic array layout, the time-domain signal matrix The size is When using a non-uniform acoustic array layout with few measurement points, the time-domain signal matrix... The size is .

5. The acoustic diagnosis method for aero-engine fan blade angle loss fault according to claim 4, characterized in that, The third step (S3) includes, S301, Measure the time-domain signal matrix of the circular acoustic array. The frequency domain matrix is ​​obtained by performing a Fourier transform on each column. , of which elements express The signal measured by the microphone at the corresponding installation angle position is in Amplitude at frequency, The length is According to the Nyquist sampling theorem, , In a uniform acoustic array layout, the frequency domain matrix The size is In a non-uniform acoustic array layout, the frequency domain matrix The size is , S302, According to the frequency domain matrix Plot the spectrum of signals measured by sensors at different installation angles and observe whether there are frequencies other than those of the passing blades. In addition to the abnormal single-tone frequency peaks outside the rotation frequency, are there any Otsu's frequency and wideband noise components under subsonic conditions? In supersonic conditions, are there any other harmonic components and modulation phenomena besides Otsu's frequency? 6. The acoustic diagnosis method for aero-engine fan blade angle loss fault according to claim 5, characterized in that, The fourth step (S4) includes, S401, When using a uniform acoustic array layout, the subscript... The microphone at the corresponding installation angle position at the predetermined frequency Frequency domain signal at It can be viewed as a linear superposition of different circumferential acoustic modes, i.e. Construct the transformation matrix The format is as follows: ， When using a non-uniform acoustic array layout with few measurement points, an observation matrix is ​​constructed based on randomly selected sensor installation angles. Observation matrix Size is , ， S402. When using a uniform acoustic array layout, the frequency domain matrix... Perform a spatial Fourier transform to obtain the wavenumber domain matrix. , ,in Frequency domain matrix The transpose of the matrix, ,in Represents the transformation matrix The false rebellion, When using a non-uniform acoustic array layout with few measurement points, the compressed sensing model is: sparse dictionary Composed of orthogonal Fourier transform bases, For the sensing matrix, sparsely reconstruct the wavenumber domain matrix based on the compressed sensing model. ; S403. Based on the obtained wavenumber domain matrix Plot the acoustic modal spectrum at 1x frequency conversion. Observe whether the dominant mode order of the acoustic modal spectrum at 1x frequency component is 1 under subsonic or supersonic conditions.

7. The acoustic diagnosis method for aero-engine fan blade angle loss fault according to claim 6, characterized in that, The fifth step (S5) includes, S501, the wavenumber domain matrix Extending to the full frequency domain, wavenumber domain matrix Size expansion to , of which elements Indicates frequency First The amplitude of the step sound mode, ， S502, According to the wavenumber domain matrix Plot the spectrum and observe whether there are any abnormal sound source terms with a dominant mode order of 1 times the rotation frequency and broadband noise abnormal sound source terms under subsonic conditions. Under supersonic conditions, observe whether there are frequency harmonics and modulation phenomena, as well as abnormal frequency conversion sound source terms. Define all features in the spectrum, acoustic mode spectrum, and wave spectrum as the acoustic signature of fan blade corner drop fault, and determine whether the fan has experienced a blade corner drop fault.

8. A diagnostic system implementing the method of any one of claims 1-7, characterized in that, It includes: The sound field measurement module includes a sound array measurement submodule and a data acquisition system submodule, which are used to measure the sound field information propagated in the pipe to the sound array installation location when the fan is working; Foreign object launching device, used to launch hard particles and soft simulated fan blades into aero engines; The spectrum analysis module is used to transform the time-domain sound field signal at the location of the acoustic array to the frequency domain and detect whether there are any abnormal frequencies other than the frequency of the blades passing through. The acoustic mode decomposition module is used to transform the acoustic field information from the frequency domain to the wavenumber domain and detect whether blade cornering fault characteristics appear in the wave spectrum.

9. 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 1-7.

10. 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 1-7.

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