1.5D Spectrum Active Frequency Planetary Gearbox Fault Diagnosis Method and System

Abstract

The present invention relates to a fault diagnosis method and system for a planetary gearbox based on the active frequency of a 1.5D spectrum, which includes: collecting the original vibration signal of the planetary gearbox and decomposing the original vibration signal into multiple modal components; reconstructing the modal components to obtain a reconstructed signal; performing 1.5D spectrum active frequency analysis on the reconstructed signal to extract the fault characteristic frequency, so as to achieve the fault diagnosis of the planetary gearbox. The present invention can accurately extract the fault characteristic frequency of the planetary gearbox, and further achieve the fault diagnosis of the planetary gearbox. The present invention can be widely applied in the technical field of rotating machinery fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of rotary machinery fault diagnosis, and particularly to a planetary gearbox fault diagnosis method and system based on the active frequency of the 1.5D spectrum. Background Art

[0002] As a part of rotary machinery, the faults of the planetary gearbox will directly affect the stable operation of the equipment and even cause damage to the entire equipment. Therefore, it is of great significance to diagnose the faults of the planetary gearbox. The operating conditions of the planetary gearbox are complex, resulting in the components of the gearbox being subjected to changing loads for a long time, causing faults such as tooth surface wear, pitting, and fatigue fracture. The main goal of planetary gearbox fault diagnosis is to extract fault characteristics from vibration signals. Summary of the Invention

[0003] Aiming at the above problems, the purpose of the present invention is to provide a planetary gearbox fault diagnosis method and system based on the active frequency of the 1.5D spectrum, which can accurately extract the fault characteristic frequencies of the planetary gearbox and then realize the fault diagnosis of the planetary gearbox.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions: A planetary gearbox fault diagnosis method based on the active frequency of the 1.5D spectrum, which includes: collecting the original vibration signal of the planetary gearbox, decomposing the original vibration signal into multiple modal components; reconstructing the modal components to obtain a reconstructed signal; performing 1.5D spectrum active frequency analysis on the reconstructed signal to extract the fault characteristic frequency and realize the fault diagnosis of the planetary gearbox.

[0005] Further, the method for decomposing the original vibration signal into several modal components by using the salp swarm algorithm to optimize the variational modal decomposition and determine the number of modal components includes:

[0006] Setting the upper and lower bounds of the penalty factor α and the number of modal components k, and defining [α,k] as the individual position, setting the population size and the number of iterations;

[0007] Calculating the fitness value of the salp individual according to the fitness function, and determining the initial positions of the leader and the food;

[0008] Updating the positions of the leader and the followers;

[0009] Calculating the fitness value of the updated salp individual and comparing it with the fitness value of the food. If the fitness value of the updated salp individual is greater than the fitness value of the food, its position is defined as the new food position;

[0010] Repeat the update of the leader's position and the follower's position, and determine the new food position until the set number of iterations is reached. Then, the iteration terminates, and the food coordinates, the optimal penalty factor α, and the number of modal components k are output.

[0011] Further, the determination of the initial positions of the leader and the food includes: sorting the fitness values of the salp individuals, setting the largest fitness value as the leader, and setting the position of the smallest fitness value as the initial position of the food.

[0012] Further, for the reconstruction of the modal components to obtain a reconstructed signal, the method of autocorrelation coefficient is used to select the optimal components to form the reconstructed signal, including: calculating the correlation between the decomposed modal components and the original vibration signal, and selecting the two groups of modal components with the best correlation for reconstruction to obtain the reconstructed signal.

[0013] Further, the 1.5D spectrum active frequency analysis of the reconstructed signal to extract the fault characteristic frequency includes:

[0014] The 1.5D spectrum decouples the coupling frequency of the reconstructed signal into two parts: the participating coupling frequency and the coupling-generated frequency, and takes the frequency that repeats between the participating coupling frequency and the coupling-generated frequency as the active frequency;

[0015] Multiply the participating coupling frequency and the coupling-generated frequency point by point, so that the amplitude of the repeated frequency is highlighted. According to this active frequency, it is judged whether the planetary gearbox has a fault. If a fault occurs, this active frequency is the fault characteristic frequency.

[0016] Further, the judgment of whether the planetary gearbox has a fault according to this active frequency includes:

[0017] Judging whether the active frequency is equal to the known planetary gear meshing frequency ± the known fault characteristic frequency;

[0018] If it is equal, the planetary gearbox has a fault; otherwise, the planetary gearbox is in a normal state.

[0019] A planetary gearbox fault diagnosis system based on 1.5D spectrum active frequency includes: a signal decomposition module, a reconstruction module, and a fault feature extraction and diagnosis module; the signal decomposition module is used to collect the original vibration signal of the planetary gearbox and decompose the original vibration signal into multiple modal components; the reconstruction module reconstructs the modal components to obtain a reconstructed signal; the fault feature extraction and diagnosis module performs 1.5D spectrum active frequency analysis on the reconstructed signal to extract fault features and realizes the fault diagnosis of the planetary gearbox.

[0020] Further, in the fault feature extraction and diagnosis module, the extraction of fault features by performing 1.5D spectral active frequency analysis on the reconstructed signal includes:

[0021] A decoupling module decouples the coupled frequencies of the reconstructed signal into two parts, namely the participating coupling frequencies and the coupling-generated frequencies, by means of 1.5D spectrum, and takes the frequencies that are repeated between the participating coupling frequencies and the coupling-generated frequencies as the active frequencies;

[0022] A judgment module multiplies the participating coupling frequencies and the coupling-generated frequencies point by point, so as to highlight the amplitudes of the repeated frequencies, and determines whether the planetary gearbox has a fault according to the active frequencies. If a fault occurs, the active frequency is the fault characteristic frequency.

[0023] A computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to execute any of the above methods.

[0024] A computing device includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0025] Due to the above technical solutions adopted by the present invention, it has the following advantages:

[0026] 1. The present invention uses the salp swarm algorithm to optimize the variational mode decomposition parameters (SSA-VMD), which has the advantages of fast convergence and short operation time.

[0027] 2. The present invention uses 1.5D spectral active frequencies to quickly and intuitively extract fault characteristic frequencies, decouple the fault characteristic frequencies, and achieve the qualitative determination of faults.

[0028] 3. The present invention uses SSA-VMD to perform noise reduction preprocessing on the signal, and then combines 1.5D active frequencies to clearly decouple the fault characteristic frequencies. If only 1.5D active frequencies are used alone, there will be a large amount of noise interference, and the fault characteristic frequencies cannot be decoupled, and the fault characteristic frequencies of the planetary gearbox cannot be accurately extracted. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic flow chart of a planetary gearbox fault diagnosis method in an embodiment of the present invention;

[0030] Figure 2 is a schematic flow chart of SSA-VMD in an embodiment of the present invention;

[0031] Figure 3It is a schematic structural diagram of a computing device in an embodiment of the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the protection scope of the present invention.

[0033] It should be noted that the terms used herein are only for describing the specific implementation manners and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless otherwise clearly specified in the context, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0034] The present invention provides a method and system for diagnosing faults in a planetary gearbox based on the 1.5D spectrum active frequency. It is based on experiments and uses the Salp Swarm Algorithm (SSA) to optimize the variational mode decomposition. The original vibration signal is decomposed into several modal components, and then the modal components are reconstructed using the autocorrelation coefficient method to obtain a reconstructed signal. The 1.5D spectrum active frequency analysis is performed on the reconstructed signal to extract fault features, thereby achieving the fault diagnosis of the planetary gearbox. The present invention combines the Salp Swarm Algorithm with the 1.5D spectrum active frequency to accurately extract the fault characteristic frequencies of the planetary gearbox and achieve the fault diagnosis of the planetary gearbox.

[0035] In an embodiment of the present invention, as Figure 1 shown, a method for diagnosing faults in a planetary gearbox based on the 1.5D spectrum active frequency is provided. In this embodiment, an example is given where this method is applied to a terminal. It can be understood that this method can also be applied to a server or a system including a terminal and a server and can be realized through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0036] Step 1: Collect the original vibration signal of the planetary gearbox and decompose the original vibration signal into multiple modal components;

[0037] In this embodiment, the original vibration signal is collected using a planetary gearbox fault test bench; the original vibration signal is decomposed into multiple modal components by performing SSA-VMD;

[0038] Step 2: Reconstruct the modal components to obtain a reconstructed signal;

[0039] Step 3: Perform 1.5D spectral active frequency analysis on the reconstructed signal to extract the fault characteristic frequency, and realize the fault diagnosis of the planetary gearbox.

[0040] In the above step 1, the original vibration signal is decomposed into several modal components, and the variational mode decomposition is optimized by the salp swarm algorithm to determine the number of modal components.

[0041] In this embodiment, for example, the sampling frequency is 20.48 kHz, and the meshing frequency of the third-stage gear is 147 Hz. The known fault characteristic frequencies of each stage of the gear are shown in Table 1. The vibration data is preprocessed by SSA-VMD.

[0042] Table 1 Known gear fault characteristic frequencies

[0043]

[0044] The salp swarm algorithm optimizing the variational mode decomposition includes the following steps:

[0045] Step 11: Set the upper and lower bounds of the penalty factor α and the number of modal components k, and define [α, k] as the individual position, and set the population size and the number of iterations;

[0046] In the salp swarm algorithm, if the number of iterations is too large, it will increase the optimization time of the algorithm and affect the calculation speed of the algorithm; while if the number of iterations is too small, the calculation result will be inaccurate and the optimal solution cannot be found. Therefore, for the VMD parameter optimization, the number of iterations is set to 30, the population size is set to 20, and [α, k] is defined as the individual position.

[0047] Step 12: Calculate the fitness value of the salp individuals according to the fitness function, and determine the initial positions of the leader and the food;

[0048] Determining the initial positions of the leader and the food includes: sorting the magnitudes of the fitness values of the salp individuals, setting the largest fitness value as the leader, and setting the position of the smallest fitness value as the initial position of the food.

[0049] Step 13: Update the position of the leader and the positions of the followers;

[0050] Among them, updating the position of the leader is:

[0051]

[0052] In the formula, is the updated position of the leader in the d-th dimension, d = 1, 2; F d is the position of the food in the d-th dimension; Ub d is the upper bound of the individual in the d-th dimension; Db dis the lower bound of the individual in the d-th dimension; c2 and c3 are random numbers within [0,1], and c1 is the convergence factor of the algorithm, whose expression is:

[0053]

[0054] In the formula, l is the current iteration number; L is the maximum iteration number, and the convergence factor is a decreasing function from 2 to 0.

[0055] Update the position of the followers as:

[0056]

[0057] In the formula, is the position of the updated follower in the d-th dimension; is the position of the follower before update.

[0058] Step 14: Calculate the fitness value of the updated salp individual and compare it with the fitness value of the food. If the fitness value of the updated salp individual is greater than the fitness value of the food, its position is defined as the new food position;

[0059] Step 15: Repeat updating the position of the leader and the followers and determining the new food position until the set number of iterations is reached. The iteration terminates, and the food coordinates, the optimal penalty factor α, and the number of modal components k are output.

[0060] In the above Step 2, the modal components are reconstructed to obtain a reconstructed signal. The optimal components are selected to form the reconstructed signal by using the method of autocorrelation coefficient: Calculate the correlation between the decomposed modal components and the original vibration signal, and select the two groups of modal components with the best correlation for reconstruction to obtain the reconstructed signal.

[0061] In this embodiment, the autocorrelation function R f (τ) is:

[0062]

[0063] In the formula, * is the convolution operator; f(τ) is the input signal; t is the time variable; τ is the time delay; f * (τ) is the conjugate of f(τ).

[0064] The correlation coefficient r(X,Y):

[0065]

[0066] In the formula, X and Y are two random signals; Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; Var[Y] is the variance of Y.

[0067] The correlation between the modal components after SSA-VMD decomposition and the original data is calculated by using the method of autocorrelation coefficient, and the reconstructed signal is obtained by reconstructing the best one or two groups of modal components with the highest correlation.

[0068] In the above step 3, the active frequency analysis of the 1.5D spectrum is carried out on the reconstructed signal to extract the fault characteristic frequency, including the following steps:

[0069] Step 31: Decouple the coupling frequency of the reconstructed signal into the participating coupling frequency and the coupling-generated frequency by the 1.5D spectrum, and regard the frequency that repeats between the participating coupling frequency and the coupling-generated frequency as the active frequency;

[0070] Due to the harsh working environment of the planetary gearbox and a large amount of noise around it, the fault characteristics of the signal cannot be directly extracted by simply using the 1.5D spectrum. The 1.5D spectrum can decouple the frequency coupling part into the participating coupling frequency and the coupling-generated frequency. There are repeated frequencies in the participating coupling frequency and the coupling-generated frequency. The repeated frequency that both participates in the coupling and is generated by the coupling is regarded as active and defined as the active frequency.

[0071] Let the participating coupling frequency component A(ω k ) of the 1.5D spectrum be:

[0072]

[0073] Let the coupling-generated frequency component B(ω k ) of the 1.5D spectrum be:

[0074]

[0075] Then the active frequency component C(ω k ) of the 1.5D spectrum is:

[0076] C(ω k ) = A(ω k ) · B(ω k ) (8)

[0077] In the formula, δ is the impulse function; j is the imaginary unit; q, p, k are all arbitrary integer moments; ω q , ω p , ω k are the frequencies corresponding to the integer q, p, k respectively; a q , a p , a k are the amplitudes corresponding to the integer q, p, k respectively; are independent random variables uniformly distributed in [0, 2π) corresponding to the integer q, p, k respectively, and n represents the total number of components at the q, p moments.

[0078] Step 32: Multiply the participating coupling frequency and the coupling-generated frequency point by point to highlight the repeated frequency amplitudes, and determine whether the planetary gearbox has a fault based on this active frequency. If a fault occurs, this active frequency is the fault characteristic frequency;

[0079] Among them, determining whether the planetary gearbox has a fault based on this active frequency includes:

[0080] Determine whether the active frequency is equal to the known planetary gear mesh frequency ± the known fault characteristic frequency;

[0081] If it is equal, that is, the active frequency = the planetary gear mesh frequency ± the known fault characteristic frequency, then the planetary gearbox has a fault; otherwise, the planetary gearbox is in a normal state.

[0082] In an embodiment of the present invention, a planetary gearbox fault diagnosis system for the active frequency of 1.5D spectrum is provided, which includes a signal decomposition module, a reconstruction module, and a fault feature extraction and diagnosis module;

[0083] The signal decomposition module is used to collect the original vibration signal of the planetary gearbox and decompose the original vibration signal into multiple modal components;

[0084] The reconstruction module reconstructs the modal components to obtain a reconstructed signal;

[0085] The fault feature extraction and diagnosis module performs 1.5D spectrum active frequency analysis on the reconstructed signal to extract the fault characteristic frequency and achieve the fault diagnosis of the planetary gearbox.

[0086] In the above embodiment, in the fault feature extraction and diagnosis module, performing 1.5D spectrum active frequency analysis on the reconstructed signal to extract the fault feature includes a decoupling module and a judgment module:

[0087] The decoupling module decouples the coupling frequency of the reconstructed signal into two parts, the participating coupling frequency and the coupling-generated frequency, by means of 1.5D spectrum, and takes the frequency where the participating coupling frequency and the coupling-generated frequency overlap as the active frequency;

[0088] The judgment module multiplies the participating coupling frequency and the coupling-generated frequency point by point to highlight the repeated frequency amplitudes, and determines whether the planetary gearbox has a fault based on this active frequency. If a fault occurs, this active frequency is the fault characteristic frequency.

[0089] The system provided in this embodiment is used to execute the above method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.

[0090] Such as Figure 3As shown, it is a schematic structural diagram of a computing device provided in an embodiment of the present invention. The computing device may be a terminal, which may include: a processor, a communications interface, a memory, a display screen, and an input device. Among them, the processor, the communications interface, and the memory complete their mutual communication through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, a fault diagnosis method is implemented; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communications interface is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen. The input device may be a touch layer covering the display screen, or may be a button, a trackball, or a touchpad provided on the housing of the computing device, or may also be an external keyboard, a touchpad, or a mouse, etc. The processor can call the logical instructions in the memory to execute the following method: collect the original vibration signal of the planetary gearbox, decompose the original vibration signal into multiple modal components; reconstruct the modal components to obtain a reconstructed signal; perform 1.5D spectrum active frequency analysis on the reconstructed signal to extract the fault characteristic frequency, and realize the fault diagnosis of the planetary gearbox.

[0091] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0092] Those skilled in the art can understand that Figure 3 the structure shown in merely represents a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computing device to which the solution of the present application is applied. The specific computing device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0093] In one embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided in the above method embodiments, for example, including: collecting the original vibration signal of the planetary gearbox, decomposing the original vibration signal into multiple modal components; reconstructing the modal components to obtain a reconstructed signal; performing 1.5D spectrum active frequency analysis on the reconstructed signal to extract fault characteristic frequencies, and realizing the fault diagnosis of the planetary gearbox.

[0094] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium stores server instructions. The computer instructions cause the computer to execute the methods provided in the above embodiments, for example, including: collecting the original vibration signal of the planetary gearbox, decomposing the original vibration signal into multiple modal components; reconstructing the modal components to obtain a reconstructed signal; performing 1.5D spectrum active frequency analysis on the reconstructed signal to extract fault characteristics, and realizing the fault diagnosis of the planetary gearbox.

[0095] The implementation principle and technical effects of the computer-readable storage medium provided in the above embodiments are similar to those of the above method embodiments, and will not be elaborated herein.

[0096] This application is described by referring to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 steps for implementing the functions specified in one block or multiple blocks.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A planetary gearbox fault diagnosis method based on 1.5-dimensional spectrum active frequency, characterized in that: include: Collecting an original vibration signal of a planetary gearbox, and decomposing the original vibration signal into multiple modal components; reconstructing the modal components to obtain a reconstructed signal; Performing 1.5-dimensional spectrum active frequency analysis on the reconstructed signal to extract fault characteristic frequencies to achieve fault diagnosis of the planetary gearbox; Performing 1.5-dimensional spectrum active frequency analysis on the reconstructed signal to extract fault characteristic frequencies includes: Decoupling the coupling frequency of the reconstructed signal into two parts, a participating coupling frequency and a coupling generation frequency, using a 1.5-dimensional spectrum, and taking the frequency of the repeated participating coupling frequency and the coupling generation frequency as the active frequency; The participating coupling frequency and the coupling generation frequency are multiplied point by point to highlight the repeated frequency amplitude. Whether the planetary gearbox fails is determined based on the active frequency. If a failure occurs, the active frequency is the fault characteristic frequency.

2. The planetary gearbox fault diagnosis method according to claim 1, characterized in that: Decomposing the original vibration signal into a plurality of modal components, optimizing the variational modal decomposition using a salp swarm algorithm, and determining the number of the modal components includes: Set the upper and lower bounds of the penalty factor α and the number of modal components k, define [α, k] as the individual position, set the population size and the number of iterations; Calculate the fitness value of the individual salps according to the fitness function and determine the initial position of the leader and food; Update the leader position and follower position; Calculate the fitness value of the updated salp individual and compare it with the fitness value of the food. If the fitness value of the updated salp individual is greater than the fitness value of the food, its position is defined as the new food position. Repeatedly update the leader position and follower position and determine the new food position until the set number of iterations is reached. The iteration terminates and the food coordinates are output, which is the optimal penalty factor α and the number of modal components k.

3. The planetary gearbox fault diagnosis method according to claim 2, characterized in that: Determining the initial positions of the leader and the food includes: sorting the fitness values of the salps, setting the largest fitness value as the leader, and setting the position of the smallest fitness value as the initial position of the food.

4. The planetary gearbox fault diagnosis method according to claim 1, characterized in that: The modal components are reconstructed to obtain a reconstructed signal, and the optimal components are selected using the autocorrelation coefficient method to form the reconstructed signal, including: calculating the correlation between the decomposed modal components and the original vibration signal, and selecting the two groups of modal components with the best correlation to reconstruct the reconstructed signal.

5. The planetary gearbox fault diagnosis method according to claim 1, characterized in that: The determining whether the planetary gearbox fails according to the active frequency includes: Determining whether the active frequency is equal to a known planetary gear meshing frequency ± a known fault characteristic frequency; If they are equal, the planetary gearbox fails; otherwise, the planetary gearbox is in normal state.

6. A planetary gearbox fault diagnosis system with 1.5-dimensional spectrum active frequency, characterized in that: include: Signal decomposition module, reconstruction module and fault feature extraction and diagnosis module; The signal decomposition module is used to collect the original vibration signal of the planetary gearbox and decompose the original vibration signal into multiple modal components; The reconstruction module reconstructs the modal components to obtain a reconstructed signal; The fault feature extraction and diagnosis module performs 1.5-dimensional spectrum active frequency analysis on the reconstructed signal to extract fault features and realize fault diagnosis of the planetary gearbox; In the fault feature extraction and diagnosis module, performing 1.5-dimensional spectrum active frequency analysis on the reconstructed signal to extract fault features includes: a decoupling module that decouples the coupling frequency of the reconstructed signal into two parts, a participating coupling frequency and a coupling generation frequency, using a 1.5-dimensional spectrum, and uses the frequency of the repeated participating coupling frequency and the coupling generation frequency as the active frequency; The judgment module multiplies the participating coupling frequency and the coupling generating frequency point by point to highlight the repeated frequency amplitude, and judges whether the planetary gearbox has a fault according to the active frequency. If a fault occurs, the active frequency is the fault characteristic frequency.

7. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 5 .

8. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods according to claims 1 to 5.

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