Method and system for fault feature identification of rotating components in a tachometer-less scenario

Abstract

The present application belongs to the field of aero-engine test and fault diagnosis, and relates to engine rotating component fault positioning technology, and provides a rotating component fault feature identification method and system in a tachometer-free scene, the method comprising: obtaining a vibration signal of a rotating component and a theoretical fault feature order; obtaining an instantaneous rotational frequency signal of the rotating component through a constructed non-convex linear transformation regularization term sparse representation model; obtaining a fault feature signal of the rotating component through iterative solution of the constructed sparse representation model; obtaining an order envelope spectrum according to the instantaneous rotational frequency signal and the fault feature signal; and performing health assessment and fault identification on the rotating component according to the order envelope spectrum and the theoretical fault feature order. The method of the present application can solve the problem that the variable speed fault of an aero-engine rotating shaft component cannot be identified in a tachometer-free scene. Through implementation of the method of the present application, the fault of the aero-engine rotating shaft component can be effectively identified, thereby providing technical support for troubleshooting in aero-engine testing.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine testing and fault diagnosis, and relates to fault location technology for rotating engine components, specifically to a method and system for identifying fault characteristics of rotating components in the absence of a tachometer. Background Technology

[0002] Under harsh service environments such as high temperature, high pressure, and heavy load, rotating components of aero engines, such as rotors, bearings, and accessory gears, are prone to localized damage that can continue to spread. Failure to detect this damage in a timely manner can lead to serious economic losses or even safety risks. Therefore, the extraction of early fault characteristics of rotating components on aero engines is of great significance for aero engine operational status perception and maintenance decision-making.

[0003] Compared to general rotating mechanical systems, the operating conditions of aero engines differ in the following ways:

[0004] 1. Variable speed operating conditions: The flight mission profiles of aero engines, such as climb, acceleration, roll, and descent, are all typical variable speed operating conditions;

[0005] 2. Application scenarios without tachometers: Due to limitations in installation space or harsh service environments, there are application scenarios where it is impossible to install a tachometer.

[0006] 3. Strong noise interference environment leads to weak fault features: During operation, aero-engines are affected by strong aerodynamic, vibration, and combustion noise. Fault features of rotating components can be submerged in the strong background noise, making it difficult to extract weak fault features. Currently, the main methods for characterizing fault features of rotating components under variable speed conditions include order tracking, envelope order spectrum, and order spectrum correlation. These methods are easily affected by noise signals, weakening their effectiveness in extracting fault features in early damage or strong background noise environments.

[0007] In recent years, a wealth of sparse representation models for fault characteristic signals of rotating components have been constructed for constant speed operating conditions. However, the fault characteristics under variable speed operating conditions are different from those under constant speed operating conditions, making it difficult to directly apply these methods to variable speed operating conditions. The construction of a few sparse representation models requires the use of the rotor's instantaneous rotational frequency signal and the optimization calculation of a large number of undetermined parameters, resulting in low computational efficiency and making it difficult to apply to application scenarios without a tachometer.

[0008] Therefore, designing a method for identifying variable speed fault characteristics that is easy to implement, consumes few resources, and is not limited by noise, and is applicable to the absence of rotational speed fields, is of great engineering significance for the diagnosis of aero-engine shaft faults. Summary of the Invention

[0009] To address the limitation of existing methods in failing to identify variable-speed faults in rotating mechanical components of aero-engines in scenarios without rotational speed, this invention discloses a method for identifying fault features of rotating components in scenarios without a tachometer. The method includes the following steps:

[0010] S1. Obtain vibration signals and theoretical fault characteristic orders of rotating components during the operation of the aero-engine;

[0011] S2. Construct a non-convex linear transformation regularized term sparse representation model using the vibration signal, and obtain the instantaneous rotation frequency signal of the rotating component through the non-convex linear transformation regularized term sparse representation model;

[0012] S3. Construct a sparse representation model with Moro envelope enhancement based on the vibration signal, and iteratively solve the sparse representation model to obtain the fault characteristic signal of the rotating component;

[0013] S4. Based on the instantaneous frequency conversion signal and the fault characteristic signal, the characteristic signal is obtained by interpolation, and the envelope calculation and Fourier transform of the characteristic signal are performed to obtain the order envelope spectrum.

[0014] S5. Perform health assessment and fault identification on the rotating component based on the order envelope spectrum and the theoretical fault characteristic order.

[0015] Furthermore, in step S1 above, obtaining the vibration signals and theoretical fault characteristic orders of the rotating components during the operation of the aero-engine includes:

[0016] S11. Install a vibration acceleration sensor on the outer or inner casing of the support point on the rotating component, and collect the vibration signal of the rotating component through the vibration acceleration sensor;

[0017] S12. Calculate the theoretical fault characteristic order of the rotating component based on its type and geometric parameters.

[0018] Furthermore, in step S12 above, when the rotating component is a bearing, the calculation formula for the theoretical fault characteristic order includes:

[0019] , ;

[0020] , ;

[0021] in, f o 、f i 、f b 、f cThe theoretical fault characteristic orders of the bearing are, in order: outer ring, inner ring, rolling elements, and cage. For the number of rolling elements, The diameter of the rolling element, For rolling bearing pitch diameter, It represents the contact angle.

[0022] Furthermore, in step S12 above, when the rotating component is a gear, the calculation formula for the theoretical fault characteristic order includes:

[0023] , ; where, given The driving gear of the meshing gear pair, For the driven gear of a meshing gear pair, f g1 f g2 These are the theoretical fault characteristic orders of the driving and driven wheels, respectively. The number of teeth on the driving gear. This represents the number of teeth on the driven gear.

[0024] Further, in step S2 above, a non-convex linear transformation regularized sparse representation model is constructed using the vibration signal, and the instantaneous rotational frequency signal of the rotating component is obtained through the non-convex linear transformation regularized sparse representation model, including:

[0025] S21. Perform Fourier transform on the vibration signal to obtain the Fourier spectrum, extract the frequency spectral coefficients within a set frequency band range in the Fourier spectrum, and perform inverse Fourier transform on the frequency spectral coefficients to obtain the filtered vibration signal.

[0026] S22. Construct a sparse representation model of non-convex linear transformation regularization terms using the filtered vibration signal;

[0027] S23. The frequency conversion information related signal is obtained by iteratively solving the sparse representation model of the non-convex linear transformation regularization term through a linear transformation regularization term denoising algorithm;

[0028] S24. Perform a short-time Fourier transform on the frequency conversion information related signal to obtain the time spectrum, and perform a synchronous compression transform on the time spectrum to obtain the time-frequency distribution;

[0029] S25. The ridge line extraction algorithm is used to extract the ridge line of the time-frequency distribution to obtain the instantaneous frequency signal of the rotating component.

[0030] Further, in step S2 above, the expression for the sparse representation model of the non-convex linear transformation regularization term is:

[0031] ;in, For frequency conversion information related signals, As an intermediate variable, This is the inverse discrete cosine transform. and These are the regularization parameters. , ;when When, regular term for nonconvex functions, for convex function, This is the filtered vibration signal.

[0032] Furthermore, in step S3 above, the expression for the Moro envelope-enhanced sparse representation model is:

[0033] ,in, To optimize the objective function globally, These are fault characteristic signals. It is a first-order difference matrix. Group size; and These are the regularization parameters, satisfying... and , x is an intermediate variable; n For any signal sequence, it is a vibration signal; , This is the sequence number of the signal data point. Represented as a signal sequence The regularization function, It is an integer. Indicates the signal sequence from to Calculate the sum of squares of the data points. and These correspond to the signal sequences respectively and The regularization function.

[0034] Further, in step S4 above, based on the instantaneous frequency switching signal and the fault characteristic signal, an interpolation method is used to obtain the characteristic signal, and envelope calculation and Fourier transform are performed on the characteristic signal to obtain the order envelope spectrum, including:

[0035] S41. Integrate the instantaneous frequency conversion signal to obtain a rotation angle signal, and sample the rotation angle signal to obtain an equal angle sampling point signal;

[0036] S42. Based on the mapping relationship between the angle signal and the fault characteristic signal, obtain the characteristic signal corresponding to the equal angle sampling point signal through spline interpolation or polynomial interpolation;

[0037] S43. Perform envelope calculation and Fourier transform on the feature signal to obtain the order envelope spectrum.

[0038] Further, in step S5 above, the health assessment and fault identification of the rotating component are performed based on the order envelope spectrum and the theoretical fault characteristic order, including:

[0039] S51. When the deviation between the order envelope spectrum and the theoretical fault characteristic order is less than a threshold, or when the order envelope spectrum contains an integer multiple of the theoretical fault characteristic order, it is determined that the rotating component is unhealthy and has a fault.

[0040] S52. When the deviation between the order envelope spectrum and the theoretical fault characteristic order is greater than or equal to a threshold, and the order envelope spectrum does not contain an integer multiple of the theoretical fault characteristic order, the rotating component is determined to be healthy and without fault.

[0041] This invention also provides a fault feature identification system for rotating components in a scenario without a tachometer, comprising a vibration signal acquisition module, a first calculation module, a model construction module, a second calculation module, an order envelope spectrum acquisition module, and a health assessment and fault identification module.

[0042] The vibration signal acquisition module is used to acquire vibration signals of rotating components during the operation of the aero-engine.

[0043] The first calculation module is used to obtain the theoretical fault characteristic order of rotating components during the operation of the aero-engine;

[0044] The model construction module is used to construct a Moro envelope-enhanced sparse representation model using the vibration signal.

[0045] The second calculation module is used to iteratively solve the sparse representation model to obtain the fault characteristic signal of the rotating component.

[0046] The order envelope spectrum acquisition module is used to obtain the feature signal by interpolation based on the instantaneous frequency switching signal and the fault feature signal, and to perform envelope calculation and Fourier transform on the feature signal to obtain the order envelope spectrum.

[0047] The health assessment and fault identification module is used to perform health assessment and fault identification on the rotating component based on the order envelope spectrum and the theoretical fault characteristic order.

[0048] Compared with the prior art, the beneficial effects of the fault feature identification method of the present invention include at least the following:

[0049] 1) By extracting the instantaneous rotational frequency information and fault characteristic signals of rotating components from the vibration monitoring signals of the aero-engine casing, it is applicable to the extraction and characterization of the variable speed fault characteristics of rotating components in scenarios without a tachometer.

[0050] 2) The constructed Moro envelope-enhanced sparse representation model is based on the time-domain group sparse features of non-periodic fault feature signals and uses the first-order difference matrix as the sparse matching. Compared with the traditional sparse representation model, the constructed sparse model has a much smaller number of hyperparameters, which eliminates the model hyperparameter optimization process and improves the efficiency of fault diagnosis. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of the fault feature identification method for rotating components in the absence of a tachometer according to the present invention;

[0053] Figure 2 The vibration signal is from the rolling bearing of the test bearing on the dual rotor of a certain type of engine.

[0054] Figure 3 To test the bearing's rotational frequency information related signals;

[0055] Figure 4 To test the time-frequency distribution of the rolling bearing on the bearing;

[0056] Figure 5 To test the instantaneous rotational frequency signal of the rolling bearing on the bearing;

[0057] Figure 6 To test the rolling bearing fault characteristic signals of the rolling bearing on the bearing;

[0058] Figure 7 To test the envelope order spectrum of rolling bearings on the bearing;

[0059] Figure 8 This is an architecture diagram of the fault feature identification system for rotating components in the absence of a tachometer according to the present invention;

[0060] Among them, 801 is the vibration signal acquisition module; 802 is the first calculation module; 803 is the model construction module; 804 is the second calculation module; 805 is the order envelope spectrum acquisition module; and 806 is the health assessment and fault identification module. Detailed Implementation

[0061] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0062] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] This invention discloses a method for identifying fault features of rotating components in a scenario without a tachometer. (See also...) Figure 1 As shown, the method includes the following steps:

[0064] S1. Obtain vibration signals and theoretical fault characteristic orders of rotating components during the operation of the aero-engine;

[0065] S2. Construct a non-convex linear transformation regularized term sparse representation model using the vibration signal, and obtain the instantaneous rotation frequency signal of the rotating component through the non-convex linear transformation regularized term sparse representation model;

[0066] S3. Construct a sparse representation model with Moro envelope enhancement based on the vibration signal, and iteratively solve the sparse representation model to obtain the fault characteristic signal of the rotating component;

[0067] S4. Based on the instantaneous frequency conversion signal and the fault characteristic signal, the characteristic signal is obtained by interpolation, and the envelope calculation and Fourier transform of the characteristic signal are performed to obtain the order envelope spectrum.

[0068] S5. Perform health assessment and fault identification on the rotating component based on the order envelope spectrum and the theoretical fault characteristic order.

[0069] Furthermore, in step S1 above, obtaining the vibration signals and theoretical fault characteristic orders of the rotating components during the operation of the aero-engine includes:

[0070] S11. A vibration acceleration sensor is installed on the outer or inner casing of the support point on the rotating component. The vibration signal of the rotating component is collected by the vibration acceleration sensor. The vibration signal can be expressed as... ,in This is the sampling sequence number of the vibration signal. Let be the total length of the vibration signal, and let be the sampling frequency of the vibration signal. .

[0071] S12. Calculate the theoretical fault characteristic order of the rotating component based on its type and geometric parameters.

[0072] Furthermore, the types of rotating components typically include bearings, gears, etc. When the type of rotating component is a bearing, the calculation formula for the theoretical fault characteristic order includes:

[0073] , ;

[0074] , ;

[0075] in, f o 、f i 、f b 、f c The theoretical fault characteristic orders of the bearing are, in order: outer ring, inner ring, rolling elements, and cage. For the number of rolling elements, The diameter of the rolling element, For rolling bearing pitch diameter, It represents the contact angle.

[0076] When the rotating component is a gear, the formula for calculating the theoretical fault characteristic order includes: , ; where, given The driving gear of the meshing gear pair, For the driven gear of a meshing gear pair, f g1 f g2 These are the theoretical fault characteristic orders of the driving and driven wheels, respectively. The number of teeth on the driving gear. This represents the number of teeth on the driven gear.

[0077] Further, in step S2 above, a non-convex linear transformation regularized sparse representation model is constructed using the vibration signal, and the instantaneous rotational frequency signal of the rotating component is obtained through the non-convex linear transformation regularized sparse representation model, including:

[0078] S21. Perform Fourier transform on the vibration signal to obtain the Fourier spectrum, extract the frequency spectral coefficients within a set frequency band range in the Fourier spectrum, and perform inverse Fourier transform on the frequency spectral coefficients to obtain the filtered vibration signal.

[0079] During implementation, the frequency band range can be set to... The filtered vibration signal can be expressed as ;in The highest rotational frequency for the design or routine operation of an aero-engine; the frequency spectral coefficients outside this frequency band are set to 0.

[0080] S22. Using the filtered vibration signal, construct a sparse representation model of the non-convex linear transformation regularization term. The expression of this model is:

[0081] ;in, It is an instantaneous frequency switching signal. As an intermediate variable, This is the inverse discrete cosine transform. and These are the regularization parameters. , ;when When, regular term for nonconvex functions, for A convex function.

[0082] S23. The frequency conversion information related signal is obtained by iteratively solving the sparse representation model of the non-convex linear transformation regularization term using a linear transformation regularization term denoising algorithm. During the iterative solution, the transformation rate of the frequency conversion information related signal obtained from two consecutive iterations is calculated. The iteration calculation is terminated when the maximum transformation rate is less than a given threshold or when the maximum number of iterations is reached. This can be expressed as:

[0083] or ;

[0084] in, , The first Second and third The frequency conversion information related signal obtained in the next iteration process For iteration rounds; Divide two signal points of the same length by an operator; For finding the maximum value operator; Given a threshold, it is generally selected as . ; The maximum number of iterations is usually chosen as . When the termination condition of the iterative calculation is met, the last round of iterative calculation will be performed. As the final frequency conversion information correlation signal, the process of obtaining the frequency conversion information correlation signal is as shown in Algorithm 1 below:

[0085] Algorithm 1: Linear Transformation Regularization Denoising Algorithm

[0086] Input: Low-pass filtered vibration signal , , , , ;

[0087] Initialization: Let , ;

[0088] cycle :

[0089] 1. ;

[0090] 2. ;

[0091] 3. Determine the termination condition: or ;

[0092] End: Output Y0.

[0093] S24. Perform a short-time Fourier transform on the frequency conversion information related signal to obtain the time spectrum, and perform a synchronous compression transform on the time spectrum to obtain the time-frequency distribution. This process can be expressed as:

[0094] ;

[0095] in, For time spectrum, For the Dirac function, It is a time-frequency distribution. It is a time series. For frequency, This refers to the sign substitution for frequencies during integration. j imaginary number , The time-varying frequency spectrum is the partial derivative with respect to time.

[0096] S25. The ridges of the time-frequency distribution are extracted using a ridge extraction algorithm to obtain the instantaneous frequency signal of the rotating component. The instantaneous frequency signal can be expressed as: Instantaneous frequency switching signal Vibration monitoring signals They are the same length.

[0097] Further, in step S3 above, a sparse representation model with Moro envelope enhancement is constructed using the vibration signal, and the fault characteristic signal of the rotating component is obtained by iteratively solving the sparse representation model, including:

[0098] S31, via vibration signal Construct a sparse representation model with Moro envelope enhancement, expressed as:

[0099] ,in, To optimize the objective function globally, These are fault characteristic signals. It is a first-order difference matrix. Group size; and These are the regularization parameters, satisfying... and , x is an intermediate variable; n For any signal sequence, it is a vibration signal; , This is the sequence number of the signal data point. Represented as a signal sequence The regularization function, It is an integer. Indicates the signal sequence from to Calculate the sum of squares of the data points. and These correspond to the signal sequences respectively and The regularization function.

[0100] The first-order difference matrix D can be expressed as:

[0101] ;matrix The size is .

[0102] S32. The global optimization objective function in the above sparse representation model yes The convex function is solved iteratively using a group sparse total variational denoising algorithm to obtain the fault characteristic signal of the rotating component. .

[0103] In implementation, the process of iteratively solving the fault feature signal using the group sparse total variational denoising algorithm is shown in Algorithm 2 below, where... For the transpose of , the termination condition for iterative calculation is the result obtained from two consecutive iterations. The maximum transformation rate or maximum number of iterations of a sequence is expressed as:

[0104] or

[0105] in, , The first Second and third The signal sequence obtained from the next iteration; Divide two signal points of the same length by an operator; For finding the maximum value operator; The maximum rate of change threshold is typically chosen as [value]. ; The maximum number of iterations is usually chosen as . When the termination condition of the iterative calculation is met, the last round of iterative calculation... The sequence is the extracted frequency conversion information related signal.

[0106] Algorithm: Group-Sparse Total Variational Denoising Algorithm 2:

[0107] Constructor: ; ; ; ;

[0108] Input: Vibration signal , , , ;

[0109] Initialization: Let , ;

[0110] cycle :

[0111] 1. ;

[0112] 2. ;

[0113] 3. Determine the termination condition: or .

[0114] Further, in step S4 above, based on the instantaneous frequency switching signal and the fault characteristic signal, an interpolation method is used to obtain the characteristic signal, and envelope calculation and Fourier transform are performed on the characteristic signal to obtain the order envelope spectrum, including:

[0115] S41. Integrate the instantaneous frequency conversion signal to obtain the angle signal, and sample the angle signal to obtain the equal angle sampling point signal.

[0116] During implementation, the instantaneous frequency signal of the rotating component and fault characteristic signals All are sampled signals of equal length and equal time intervals, with a sampling frequency of [missing value]. ,at the same time, and With the same signal data points, the corresponding rotation angle signal is calculated by frequency integration. Corner signal It can be represented as:

[0117] , The first in the instantaneous frequency switching signal i Data points, It is a natural number.

[0118] By sampling the corner signal within the corner range according to the corner sampling interval, equal-angle sampling point signals can be obtained. This is because the corner signal... and The sequences have the same length, therefore within the corner range Inside, it can be By setting the angle interval, corner sampling points are obtained to obtain equal angle sampling point signals. ,in The sampling frequency is the angle sampling frequency, and the signal is obtained from sampling points at equal angles. It can be represented as:

[0119] ,in, A sequence of equal-angle sampling points The length.

[0120] S42. Based on the mapping relationship between the angle signal and the fault characteristic signal, obtain the characteristic signal corresponding to the equal-angle sampling point signal through spline interpolation or polynomial interpolation. In implementation, according to... The mapping relationship is used to calculate the signal of the equal-angle sampling points through spline interpolation or polynomial interpolation methods. Corresponding characteristic signal .

[0121] S43. The envelope of the feature signal is calculated and Fourier transformed to obtain the order envelope spectrum. This process can be expressed as:

[0122] ,in, For Hilbert transform operators; For modulo operators; This is a Fourier transform.

[0123] Further, in step S5 above, the health assessment and fault identification of the rotating component are performed based on the order envelope spectrum and the theoretical fault characteristic order, including:

[0124] S51. When the deviation between the order envelope spectrum and the theoretical fault characteristic order is less than a threshold, or when the order envelope spectrum contains an integer multiple of the theoretical fault characteristic order, it is determined that the rotating component is unhealthy and has a fault.

[0125] S52. When the deviation between the order envelope spectrum and the theoretical fault characteristic order is greater than or equal to a threshold, and the order envelope spectrum does not contain an integer multiple of the theoretical fault characteristic order, the rotating component is determined to be healthy and without fault. The threshold can be set to 1%, and the multiple relationship can be set to 2 to 4 times the order components.

[0126] This invention uses a test bearing for a dual-rotor engine of a certain type as an example to illustrate the above method. Before the experiment, a crack fault was pre-fabricated in the inner ring of the test bearing. Based on the geometric dimensions of the test bearing, the theoretical fault characteristic order of each component of the test rolling bearing is calculated as shown in Table 1 below:

[0127] Table 1: Geometric parameters of the tested bearings and the theoretical fault characteristic order

[0128]

[0129] In the experiment, an accelerometer installed on the bearing housing collected vibration signals from the rolling bearing on the test bearing. The sampling frequency in the experiment was 200kHz, and the collected vibration signals were as follows: Figure 2 As shown. The data signal has a length of 2,000,000 data points, and the low-pass filter passband range is set to... The vibration test signal is low-pass filtered. Based on this, a sparse representation model of the non-convex linear transform regularization term is constructed, where the regularization term parameter is set to... , The frequency conversion information related signals obtained by solving this sparse representation model are as follows: Figure 3 As shown, the signal is mainly a frequency-modulated harmonic signal. A synchronous compression transform based on short-time Fourier transform is performed on the frequency conversion information-related signal, resulting in the following time-frequency distribution: Figure 4 As shown, the time-frequency distribution is then analyzed using a time-frequency surface ridge extraction algorithm, and the extracted instantaneous frequency conversion signal is as follows. Figure 5 As shown.

[0130] By constructing a sparse representation model enhanced by Moro envelope, the parameters in this model are set to... , The rolling bearing fault characteristic signals obtained by solving this sparse representation model are as follows: Figure 6 As shown, a significant impact component can be seen in the extracted fault feature signal.

[0131] Based on this, by extracting the instantaneous frequency switching signal and fault characteristic signal, the characteristic signal can be calculated as follows: Figure 7 The envelope order spectrum shown, Figure 7 In the diagram, FCO represents the theoretical fault characteristic order of a specific component on the bearing, calculated from various parameters. The main components of this envelope order spectrum are... This component corresponds to the order of theoretical failure characteristics of the inner ring of the tested bearing. They are close, with a relative deviation of 0.07%, and both contain [something] in their envelope order spectra. The integer multiples of 2, 3, etc., indicate that a fault exists in the tested bearing, and the fault location is on the inner ring. This data shows that the method of the present invention can accurately extract the instantaneous rotational frequency and non-periodic fault impact characteristic signals of the rotating components of an aero-engine under variable speed conditions, and characterize and identify faults in the rotating components.

[0132] Based on the same inventive concept, this invention also provides a fault feature identification system for rotating components in a tachometer-less scenario, as described in the following embodiments. Since the principle of the fault feature identification system for rotating components in a tachometer-less scenario is similar to the fault feature identification method for rotating components in a tachometer-less scenario disclosed in the above embodiments, the implementation of the fault feature identification system for rotating components in a tachometer-less scenario can refer to the implementation of the fault feature identification method for rotating components in a tachometer-less scenario described above, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0133] Figure 8 This is a structural block diagram of a fault feature identification system for rotating components in a scenario without a tachometer, as disclosed in an embodiment of the present invention. Figure 8 As shown, the system includes a vibration signal acquisition module 801, a first calculation module 802, a model construction module 803, a second calculation module 804, an order envelope spectrum acquisition module 805, and a health assessment and fault identification module 806. The structure is described below.

[0134] The vibration signal acquisition module 801 is used to acquire vibration signals of rotating components during the operation of the aero-engine.

[0135] The first calculation module 802 is used to obtain the theoretical fault characteristic order of rotating components during the operation of the aero-engine;

[0136] The model construction module 803 is used to construct a Moro envelope-enhanced sparse representation model using the vibration signal.

[0137] The second calculation module 804 is used to iteratively solve the sparse representation model to obtain the fault characteristic signal of the rotating component.

[0138] The order envelope spectrum acquisition module 805 is used to acquire the feature signal by interpolation based on the instantaneous frequency switching signal and the fault feature signal, and to perform envelope calculation and Fourier transform on the feature signal to obtain the order envelope spectrum.

[0139] The health assessment and fault identification module 806 is used to perform health assessment and fault identification on the rotating component based on the order envelope spectrum and the theoretical fault characteristic order.

[0140] Compared with the prior art, the beneficial effects of the fault feature identification method of the present invention include at least the following:

[0141] 1) By extracting the instantaneous rotational frequency information and fault characteristic signals of rotating components from the vibration monitoring signals of the aero-engine casing, it is applicable to the extraction and characterization of the variable speed fault characteristics of rotating components in scenarios without a tachometer.

[0142] 2) The constructed Moro envelope-enhanced sparse representation model is based on the time-domain group sparse features of non-periodic fault feature signals and uses the first-order difference matrix as the sparse matching. Compared with the traditional sparse representation model, the constructed sparse model has a much smaller number of hyperparameters, which eliminates the model hyperparameter optimization process and improves the efficiency of fault diagnosis.

[0143] In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for identifying fault characteristics of rotating components in any scenario without a tachometer.

[0144] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0145] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes the above-described method for identifying fault characteristics of rotating components in any of the scenarios without a tachometer.

[0146] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0147] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0148] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying fault characteristics of rotating components in a scenario without a tachometer, characterized in that, include: Acquiring vibration signals and theoretical fault characteristic orders of rotating components during aero-engine operation includes: installing vibration acceleration sensors on the outer or inner casing of the rotating component's support points, collecting vibration signals of the rotating component through the vibration acceleration sensors, and calculating the theoretical fault characteristic order of the rotating component based on the type and geometric parameters of the rotating component. A non-convex linear transform regularized sparse representation model is constructed using the vibration signal. The instantaneous rotational frequency signal of the rotating component is obtained through this model, including: performing a Fourier transform on the vibration signal to obtain a Fourier spectrum; extracting frequency spectral coefficients within a defined frequency band within the Fourier spectrum; performing an inverse Fourier transform on the frequency spectral coefficients to obtain a filtered vibration signal; constructing a non-convex linear transform regularized sparse representation model using the filtered vibration signal; iteratively solving the non-convex linear transform regularized sparse representation model using a linear transform regularized denoising algorithm to obtain a rotational frequency information related signal; performing a short-time Fourier transform on the rotational frequency information related signal to obtain a time spectrum; performing a synchronous compression transform on the time spectrum to obtain a time-frequency distribution; and extracting the ridges of the time-frequency distribution using a ridge extraction algorithm to obtain the instantaneous rotational frequency signal of the rotating component. A Moro envelope-enhanced sparse representation model is constructed using the vibration signal, and the fault characteristic signal of the rotating component is obtained by iteratively solving the Moro envelope-enhanced sparse representation model. Based on the instantaneous frequency switching signal and the fault characteristic signal, the characteristic signal is obtained by interpolation, and the envelope is calculated and Fourier transformed to obtain the order envelope spectrum. The rotating component is subjected to health assessment and fault identification based on the order envelope spectrum and the order of the theoretical fault characteristics.

2. The method for identifying fault characteristics of rotating components in a scenario without a tachometer, as described in claim 1, is characterized in that... When the rotating component is a bearing, the formula for calculating the theoretical fault characteristic order includes: ， ； ， ； in, f o 、f i 、f b 、f c The theoretical fault characteristic orders of the bearing are, in order: outer ring, inner ring, rolling elements, and cage. For the number of rolling elements, The diameter of the rolling element, For rolling bearing pitch diameter, It represents the contact angle.

3. The method for identifying fault characteristics of rotating components in a scenario without a tachometer, as described in claim 1, is characterized in that... When the rotating component is a gear, the formula for calculating the theoretical fault characteristic order includes: , ; where, given For the driving gear of the meshing gear pair, For the driven gear of a meshing gear pair, f g1 f g2 These are the theoretical fault characteristic orders of the driving and driven wheels, respectively. The number of teeth on the driving gear. This represents the number of teeth on the driven gear.

4. The method for identifying fault characteristics of rotating components in a scenario without a tachometer, as described in claim 1, is characterized in that... The expression for the sparse representation model of the non-convex linear transformation regularization term is: ;in, For frequency conversion information related signals, As an intermediate variable, This is the inverse discrete cosine transform. and These are the regularization parameters. , ;when When, regular term for nonconvex functions, for convex function, This is the filtered vibration signal.

5. The method for identifying fault characteristics of rotating components in a scenario without a tachometer, as described in claim 1, is characterized in that... The expression for the Moro envelope-enhanced sparse representation model is as follows: ,in, To optimize the objective function globally, These are fault characteristic signals. It is a first-order difference matrix. Group size; and These are the regularization parameters, satisfying... and , x is an intermediate variable; n For any signal sequence, it is a vibration signal; , This is the sequence number of the signal data point. Represented as a signal sequence The regularization function, It is an integer. Indicates the signal sequence from to Calculate the sum of squares of the data points. and These correspond to the signal sequences respectively and The regularization function.

6. The method for identifying fault characteristics of rotating components in a scenario without a tachometer, as described in claim 1, is characterized in that... Based on the instantaneous frequency switching signal and the fault characteristic signal, the characteristic signal is obtained by interpolation, and the envelope of the characteristic signal is calculated and Fourier transformed to obtain the order envelope spectrum, including: The instantaneous frequency conversion signal is integrated to obtain a rotation angle signal, and the rotation angle signal is sampled to obtain an equal angle sampling point signal; Based on the mapping relationship between the angle signal and the fault characteristic signal, the characteristic signal corresponding to the equal angle sampling point signal is obtained by spline interpolation or polynomial interpolation; The envelope spectrum is obtained by performing envelope calculation and Fourier transform on the feature signal.

7. The method for identifying fault characteristics of rotating components in a scenario without a tachometer, as described in claim 1, is characterized in that... Based on the order envelope spectrum and the theoretical fault characteristic order, a health assessment and fault identification are performed on the rotating component, including: When the deviation between the order envelope spectrum and the theoretical fault characteristic order is less than a threshold, or when the order envelope spectrum contains an integer multiple of the theoretical fault characteristic order, the rotating component is determined to be unhealthy and has a fault. When the deviation between the order envelope spectrum and the theoretical fault characteristic order is greater than or equal to a threshold, and the order envelope spectrum does not contain an integer multiple of the theoretical fault characteristic order, the rotating component is determined to be healthy and without fault.

8. A fault feature identification system for rotating components in a scenario without a tachometer, implementing the fault feature identification method according to any one of claims 1 to 7, characterized in that, include: A vibration signal acquisition module is used to acquire vibration signals of rotating components during the operation of an aero-engine. The first calculation module is used to obtain the theoretical fault characteristic order of rotating components during the operation of the aero-engine; The model construction module is used to construct a non-convex linear transformation regularization term sparse representation model using the vibration signal, and to construct a Moro envelope enhanced sparse representation model using the vibration signal. The second calculation module is used to obtain the instantaneous frequency signal of the rotating component through the non-convex linear transformation regularization term sparse representation model, and to iteratively solve the Moro envelope enhanced sparse representation model to obtain the fault characteristic signal of the rotating component. The order envelope spectrum acquisition module is used to obtain the characteristic signal by interpolation based on the instantaneous frequency switching signal and the fault characteristic signal, and to perform envelope calculation and Fourier transform on the characteristic signal to obtain the order envelope spectrum. A health assessment and fault identification module is used to perform health assessment and fault identification on the rotating component based on the order envelope spectrum and the theoretical fault characteristic order.

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