Fault diagnosis method and system for gearbox of tower crane

Abstract

The invention discloses a fault diagnosis method and system for a gearbox of a tower crane, and relates to the technical field of fault diagnosis of mechanical equipment, and the method comprises the steps: converting global signal features into local features through employing a narrowband technology; obtaining an optimal filter through iteration based on the sparse characteristic of the spectrum negentropy; and filtering the collected original signal of the gear box of the tower crane for envelope analysis. According to the fault diagnosis method for the gearbox of the tower crane, the square envelope spectrum is used for forming a narrow band special for the fault, and the influence of actual speed fluctuation is relieved. Based on periodicity and stationary characteristics, correlation spectrum negentropies are introduced. The CSNED can eliminate cyclostationary characteristics in non-fault signals, eliminate grid noise interference and finally extract fault related characteristics, has high robustness in practical application, can quickly and accurately judge the running state of the tower crane, and achieves better effects in the aspects of fault detection obvious degree, operation efficiency and safety.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical equipment fault diagnosis, and in particular to a tower crane gearbox fault diagnosis method and system. Background Art

[0002] As a large-scale construction machinery, the importance of condition monitoring for tower crane gearboxes cannot be underestimated. Tower crane gearboxes are subject to huge loads and continuous operation, and are prone to wear and potential failures. Monitoring the health of gearboxes is essential to prevent unexpected downtime, ensure operational safety, and optimize maintenance plans. Early detection of signs of wear or failure, such as abnormal vibrations or temperature changes, allows for proactive maintenance, reduces the risk of catastrophic failures, and extends the service life of the crane. This proactive approach not only enhances reliability, but also reduces maintenance costs and improves overall operational efficiency.

[0003] Deconvolution aims to construct an inverse filter by optimizing fault indicators to mitigate the impact of the signal transmission path. Minimum entropy deconvolution (MED) maximizes the kurtosis of the filter design, but it usually recovers a single pulse rather than multiple repetitive pulses. Optimal MED (OMED) maximizes the D norm to update the filter coefficients, but it tends to converge to a single pulse, which is particularly problematic for bearing fault signals. Sparse filtering (SF) uses the generalized Lp / Lq norm to recover bearing fault pulses and has shown greater resilience than MED in experimental results. However, the correct choice of the norm order p is crucial; an improper choice may lead to convergence to a single pulse. Deconvolution converges to a single pulse because it prioritizes the impulsive nature of the bearing signal. Periodicity-based indicators, such as correlation kurtosis (CK), harmonic noise ratio (HNR), and multiple D norm (MDN), have been developed to recover repetitive pulses in rolling element bearings. Based on these indices, maximum CK deconvolution (MCKD), sparse maximum harmonic noise ratio deconvolution (HNRD) and multi-point OMED adjustment (MOMEDA) are proposed to effectively address the tendency of MED to converge to a single pulse. The vibration signals in rotating machines exhibit cyclostationary (CS) behavior rather than strict periodic behavior. It is worth noting that the above-mentioned "periodicity" indicators are completely developed based on periodicity criteria and lack a solid theoretical foundation.

[0004] In the traditional blind deconvolution fault diagnosis method, blind filtering based on spectral negentropy index is an effective method to extract weak periodic pulses related to bearing faults. However, in practical applications, tower cranes may have multiple cyclostationary sources, including periodic pulses generated by the operation of the shaft. Traditional fault diagnosis methods are easily interfered by non-fault stationary sources and thus cannot extract fault features. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the existing blind deconvolution fault diagnosis method has the problems of low reliability, high maintenance cost, low overall operating efficiency, and susceptibility to interference from non-fault stable sources.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a fault diagnosis method for a tower crane gearbox, comprising converting global signal features into local features using narrowband technology; obtaining an optimal filter through iteration based on the sparse characteristics of the spectral negative entropy; and filtering the collected original signal of the tower crane gearbox for envelope analysis.

[0008] As a preferred solution of the tower crane gearbox fault diagnosis method of the present invention, the method of converting the global signal feature into the local feature by using the narrowband technology includes constructing a simulation signal for the bearing fault according to the expression:

[0009]

[0010] Where C represents the pulse amplitude, ε represents the damping coefficient, ω represents the resonant frequency, and T = f f ault -1 ,f fault represents the fault characteristic frequency of the simulation signal, h(t) represents the step function and τ i It is a random fluctuation usually between 1-2%;

[0011] There are random pulses, harmonic interference and Gaussian noise in the collected signal. The interference model is constructed to represent the simulation scenario as follows:

[0012] b(t)=c(t)+s(t)+n(t)

[0013] Among them, c(t) represents harmonic interference, and n(t) represents Gaussian noise that obeys normal distribution;

[0014] Harmonic interference c(t) is expressed as:

[0015] c(t)=P1 sin(2πh1t+θ1)+P2 sin(2πh2t+θ2)+P1(0.5+0.3cos(2πh3t+θ3))

[0016] Among them, P1, P2, P3 represent the amplitude of each order, h1, h2, h3 are the frequencies of each order, and θ1, θ2, θ3 represent the phase of each order;

[0017] The sampling frequency of all simulation signals is 10kHz and the sampling time is 1s.

[0018] As a preferred solution of the tower crane gearbox fault diagnosis method of the present invention, the method of converting the global signal feature into a local feature by using narrowband technology includes designing a specific narrowband structure represented as:

[0019]

[0020]

[0021] Among them, s represents the original signal, μ represents the length of a specific frequency band, and α μ (k) and Δf represent the k-order cycle frequency and resolution, and the specific frequency band [α μA (k),α μB (k)] 2μ+1, μ is the length of a specific frequency band, α μ (k) and Δf represent the k-order cycle frequency and resolution;

[0022] The specific frequency band is used to transform the global characteristics of the envelope spectrum into specific frequency band characteristics related to the fault and identify the fault characteristics;

[0023] The construction process of cyclic spectrum negative entropy is to design a specific narrow-band structure;

[0024] The transformed form of the input signal x is expressed as:

[0025] y=x*f i

[0026] Where y represents the filtered signal, f i and * denote the initial filter and the convolution operator, respectively.

[0027] As a preferred solution of the tower crane gearbox fault diagnosis method of the present invention, wherein: the sparse characteristics based on spectral negentropy are iterated to obtain the optimal filter including the square envelope spectrum expression EX of the fault feature in a specific frequency band ψ It is expressed as:

[0028] ∈ x =|y| 2 =|X f | 2

[0029]

[0030] Among them, ∈ x represents the square envelope spectrum of signal x, diag(y H ) represents the diagonal matrix of the transposed filtered signal, X represents the matrix form of the signal x, f is the iterative filter, represents the Fourier matrix containing the fault characteristics.

[0031] As a preferred solution of the tower crane gearbox fault diagnosis method of the present invention, wherein: the collected original signal of the tower crane gearbox is filtered and envelope analyzed, and the Fourier matrix including the fault characteristics is expressed as:

[0032]

[0033] Where μ represents the length of a specific frequency band, N represents the length of the signal, L represents the filter length, and P k Indicates the position of the fault characteristic frequency in the total sampling points.

[0034] As a preferred solution of the tower crane gearbox fault diagnosis method of the present invention, wherein: the original signal of the tower crane gearbox collected is filtered and envelope analyzed, including the position of the fault characteristic frequency in the total sampling points is expressed as:

[0035]

[0036] Where k = 1, 2, ... K, T and f s Indicates the fault cycle and sampling point;

[0037] Cyclic spectrum negative entropy ΔI CEX The defined envelope spectrum is expressed as:

[0038]

[0039] Among them, ΔI CEX represents the cyclic spectrum negative entropy, H CEX represents the signal spectral entropy, EX ψ (*) indicates the square envelope spectrum, P k The position corresponding to the fault characteristic frequency, N represents the signal length.

[0040] As a preferred solution of the tower crane gearbox fault diagnosis method of the present invention, wherein: the collected original signal of the tower crane gearbox is filtered to perform envelope analysis including the generalized Rayleigh entropy form ΔI of the correlation spectrum negative entropy CEX It is expressed as:

[0041]

[0042] Among them, ΔI CEX represents the cyclic spectral negative entropy, f H and X H They represent the transposed matrices of the filter matrix f and the original signal matrix X, respectively. diag(*) represents the diagonal matrix. Represents the corresponding fault characteristic component in the envelope spectrum, is the Fourier matrix The transpose of

[0043] The maximum eigenvalue λ and the corresponding eigenvector output by the eigenvalue decomposition algorithm are equivalent to the maximum value of the generalized Rayleigh entropy of the filter coefficient f;

[0044] The maximized eigenvalue is expressed as:

[0045]

[0046] in, represents the fault feature matrix;

[0047] Represents the original signal matrix:

[0048]

[0049] in, represents the fault feature matrix, represents the original signal matrix;

[0050] The cyclic spectrum negative entropy ΔI of the envelope spectrum CEX The maximization is expressed as:

[0051]

[0052] in, represents the fault feature matrix, represents the original signal matrix, and f represents the filter matrix.

[0053] Another object of the present invention is to provide a tower crane gearbox fault diagnosis system, which can derive the filter coefficient acquisition process by utilizing the Rayleigh entropy expression of CSNE, thereby solving the problem of low overall operating efficiency of the current blind deconvolution fault diagnosis method.

[0054] As a preferred solution of the tower crane gearbox fault diagnosis system described in the present invention, it includes: a local feature conversion module, an optimal filter acquisition module, and an envelope analysis module; the local feature conversion module is used to convert global signal features into local features using narrowband technology; the optimal filter acquisition module is used to use the sparse characteristics of spectral negative entropy to obtain the optimal filter through iteration; the envelope analysis module is used to filter the collected original signal of the tower crane gearbox, and then perform envelope analysis.

[0055] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a fault diagnosis method for a tower crane gearbox.

[0056] A computer-readable storage medium stores a computer program, which implements the steps of a tower crane gearbox fault diagnosis method when executed by a processor.

[0057] Beneficial effects of the present invention: The tower crane gearbox fault diagnosis method provided by the present invention utilizes the square envelope spectrum to form a fault-specific narrow band to reduce the impact of actual speed fluctuations. Based on periodicity and stationary characteristics, a new fault pulse metric, correlation spectrum negative entropy (CSNE), is introduced. The process of obtaining the filter coefficient is derived using the CSNE Rayleigh entropy expression, and a new deconvolution method, called correlation spectrum negative entropy deconvolution, is proposed. CSNED can eliminate the cyclostationary features in non-fault signals, eliminate gate noise interference, and ultimately extract fault-related features. It has strong robustness in practical applications and can quickly and accurately determine the operating status of a tower crane. The present invention achieves better results in terms of fault detection prominence, operational efficiency, and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0059] Figure 1 An overall flow chart of a tower crane gearbox fault diagnosis method provided by the first embodiment of the present invention.

[0060] Figure 2 A time domain waveform diagram of various components of a simulation signal of a tower crane gearbox fault diagnosis method provided by the first embodiment of the present invention.

[0061] Figure 3 A simulation signal and its envelope spectrum of a tower crane gearbox fault diagnosis method provided by the first embodiment of the present invention.

[0062] Figure 4 A time domain signal and its envelope spectrum of a simulation signal after CSNED filtering of a tower crane gearbox fault diagnosis method provided in the first embodiment of the present invention.

[0063] Figure 5 An experimental signal diagram of tooth root cracks in a tower crane gearbox fault diagnosis method provided by the second embodiment of the present invention.

[0064] Figure 6A method for diagnosing a gearbox fault of a tower crane provided in accordance with a second embodiment of the present invention provides a time domain signal of a tooth root crack signal after CSNED filtering and its envelope spectrum.

[0065] Figure 7 An overall flow chart of a fault diagnosis system for a tower crane gearbox provided in accordance with a third embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] Example 1, reference Figure 1-Figure 4 , as an embodiment of the present invention, provides a tower crane gearbox fault diagnosis method, comprising:

[0068] S1: Convert global signal features into local features using narrowband technology.

[0069] Furthermore, a set of simulation signals for bearing faults is constructed as follows:

[0070]

[0071] Where C represents the pulse amplitude, ε represents the damping coefficient, ω represents the resonant frequency, and T = f fault -1 ,f fault represents the fault characteristic frequency of the simulation signal, h(t) represents the step function and τ i The random fluctuation is usually around 1-2%, and in actual situations, there are random pulses, harmonic interference and Gaussian noise in the collected signal, so the above scenario can be simulated by the following interference model:

[0072] b(t)=c(t)+s(t)+n(t)

[0073] Among them, c(t) represents harmonic interference, n(t) represents Gaussian noise that obeys normal distribution, and the expression of harmonic interference c(t) is as follows:

[0074] c(t)=P1 sin(2πh1t+θ1)+P2 sin(2πh2t+θ2)+P1(0.5+0.3cos(2πh3t+θ3))

[0075] Among them, P1, P2, P3 represent the amplitude of each order, h1, h2, h3 are the frequencies of each order, and θ1, θ2, θ3 represent the phase of each order.

[0076] The sampling frequency of all simulation signals is 10kHz and the sampling time is 1s. The parameters of the simulation signals and the parameters of the harmonic interference components are listed in Table 1 and Table 2 respectively. Fault pulses and harmonic interference, noise components, random pulses and combined signals are shown in Table 1 and Table 2. Figure 2 As shown, Figure 3 The simulation signal time domain waveform and its envelope spectrum are displayed.

[0077] Table 1 Parameters of simulation signal

[0078] C <![CDATA[f s (Hz)]]> ω(Hz) <![CDATA[f fault ]]> <![CDATA[f r (Hz)]]> ε SNR 1 10000 2500 86 10.29 800 -10

[0079] Table 2 Parameters of harmonic interference components

[0080] It should be noted that since the filter designed by the traditional blind deconvolution method cannot accurately identify the fault frequency band, this algorithm designs a specific narrowband structure as follows:

[0081]

[0082]

[0083] Among them, s represents the original signal, μ represents the length of a specific frequency band, and α μ (k) and Δf represent the k-order cycle frequency and resolution, and the specific frequency band [α μA (k),α μB (k)] 2μ+1, μ is the length of a specific frequency band, α μ (k) and Δf represent the k-th order cycle frequency and resolution.

[0084] The specific frequency band is used to transform the global features of the envelope spectrum into specific frequency band features related to the fault, so as to better identify the fault features. The construction process of the cyclic spectrum negative entropy is shown in Algorithm 1. The transformed form of the input signal x is

[0085] y=x*f i

[0086] Where y represents the filtered signal, f i and * denote the initial filter and the convolution operator, respectively.

[0087] S2: Based on the sparse characteristics of spectral negentropy, the optimal filter is obtained through iteration.

[0088] Furthermore, the square envelope spectrum expression of the fault characteristic in a specific frequency band is EX ψ As shown below:

[0089] ∈ x =|y| 2 =|Xf| 2

[0090]

[0091] Among them, ∈ x represents the square envelope spectrum of signal x, diag(y H ) represents the diagonal matrix of the transposed filtered signal, X represents the matrix form of the signal x, f represents the filter matrix of the iteration, and F represents the Fourier matrix containing the fault characteristics.

[0092]

[0093] Where μ represents the length of a specific frequency band, N represents the length of the signal, L represents the filter length, and P k Indicates the position of the fault characteristic frequency in the total sampling points.

[0094] Furthermore, P k It represents the position of the fault characteristic frequency in the total sampling points, and its equation is:

[0095]

[0096] Where k = 1, 2, ... K, T and f s Indicates the fault period and sampling point.

[0097] Cyclic spectrum negative entropy ΔI CEX The envelope spectrum is defined as:

[0098]

[0099] Among them, ΔI CEX represents the cyclic spectrum negative entropy, H CEX represents the signal spectral entropy, EX ψ (*) indicates the square envelope spectrum, P k The position corresponding to the fault characteristic frequency, N represents the signal length.

[0100] It should be noted that the generalized Rayleigh entropy form of the relevant spectral negative entropy ΔI CEX It is expressed as follows:

[0101]

[0102] Among them, ΔI CEX represents the cyclic spectral negative entropy, f H and X H They represent the transposed matrices of the filter matrix f and the original signal matrix X, respectively. diag(*) represents the diagonal matrix. Represents the corresponding fault characteristic component in the envelope spectrum, is the Fourier matrix The transpose of .

[0103] The maximum eigenvalue λ and its corresponding eigenvector obtained by the eigenvalue decomposition algorithm are equivalent to the maximum value of the generalized Rayleigh entropy of the filter coefficient f. The expression for maximizing the eigenvalue is defined as follows:

[0104]

[0105] Among them, λ represents the maximum eigenvalue corresponding to the eigendecomposition, represents the fault feature matrix, The original signal matrix is ​​as follows

[0106]

[0107] in, represents the fault feature matrix, Represents the original signal matrix.

[0108] The cyclic spectrum negative entropy ΔI of the envelope spectrum CEX Maximization, the formula is as follows:

[0109]

[0110] in, represents the fault feature matrix, represents the original signal matrix, f represents the filter matrix, and the above iterations are repeated until the eigenvalues ​​converge to obtain the optimal filter f corresponding to the maximum eigenvalue. final .

[0111] S3: Use the optimal filter to filter the original signal of the tower crane gearbox and perform envelope analysis.

[0112] y=x*f final

[0113] y e = abs(Hilbert(y))

[0114] Where abs(*) represents the absolute value of the value, Hilbert(*) represents the Hilbert envelope, and y e Represents the enveloped signal.

[0115] like Figure 3(b) shows the envelope spectrum of the simulated signal. It is observed that the simulated signal contains strong noise and has two harmonic interferences (150Hz, 169Hz) to two harmonic interferences. It can be seen that the fault component has been completely masked and the fault characteristics cannot be identified in the envelope spectrum. Therefore, by using the CSNED proposed by the present invention to perform envelope demodulation on the simulated signal and the filtered signal, the analysis results are shown as follows: Figure 4 As shown, the envelope spectrum of the filtered signal Figure 4 It can be seen in (b) that compared with the original signal, the filtered signal not only reduces the interference of strong noise, but also enhances the fault characteristics. The fault characteristic frequency (83.3Hz) and its harmonics (166.6Hz) related to the simulation signal can be observed in the envelope spectrum. The noise contained in the filtered signal is obviously reduced, and the harmonic interference component set has also disappeared. This proves that the method proposed in this chapter can effectively avoid the interference of harmonics and noise, and can enhance the fault pulse to achieve the fault feature extraction of the simulation signal. Based on the analysis of the simulation signal, it can be concluded that the method proposed in the present invention can effectively reduce the influence of Gaussian noise and enhance the fault pulse, thereby realizing the fault diagnosis of the bearing simulation signal.

[0116] Example 2, reference Figure 5-Figure 6 , which is an embodiment of the present invention, provides a fault diagnosis method for a tower crane gearbox. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0117] First, tooth root cracks are common early failures under high alternating loads. It is critical to detect faults in the early stages of cracks because small cracks can expand rapidly under harsh working conditions and may develop into serious faults such as tooth breakage. When cracks appear, the meshing stiffness of the faulty tooth decreases. Therefore, the stiffness of all teeth is not exactly the same, resulting in speed fluctuations in circular motion. This case takes the fault signal of a real gearbox, such as Figure 5 As shown, after calculation, the gear Figure 5 In (b), the characteristic frequency (FCF) associated with the fault cannot be observed. CSNED is used to improve the signal-to-noise ratio of the original signal. The processed signal is shown in Figure 6 As shown in (a), the effect of torsional vibration caused by cracks can be seen more clearly. Figure 6 In (b), after denoising, the peak of the fault feature is more clearly distributed on the envelope spectrum, and the amplitude of the peak is significantly larger than other peaks. Therefore, after CSNED processing, the impact of the fault is perfectly enhanced.

[0118] Example 3, reference Figure 7, as an embodiment of the present invention, provides a tower crane gearbox fault diagnosis system, including a local feature conversion module, an optimal filter acquisition module, and an envelope analysis module.

[0119] The local feature conversion module is used to convert the global signal features into local features using narrowband technology. The optimal filter acquisition module is used to utilize the sparse characteristics of spectral negative entropy to obtain the optimal filter through iteration. The envelope analysis module is used to filter the collected original signal of the tower crane gearbox and then perform envelope analysis.

[0120] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0122] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0123] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A tower crane gearbox fault diagnosis method, characterized in that: include: Use narrowband technology to convert global signal features into local features; Based on the sparse characteristics of spectral negative entropy, the optimal filter is obtained through iteration; The collected original signal of the tower crane gearbox is filtered for envelope analysis.

2. The tower crane gearbox fault diagnosis method according to claim 1, characterized in that: The method of converting the global signal features into local features by using narrowband technology includes constructing a simulation signal for bearing faults as: Where C represents the pulse amplitude, ε represents the damping coefficient, ω represents the resonant frequency, and T = f fault -1 ,f fault represents the fault characteristic frequency of the simulation signal, h(t) represents the step function and τ i It is a random fluctuation usually between 1-2%; There are random pulses, harmonic interference and Gaussian noise in the collected signal. The interference model is constructed to represent the simulation scenario as follows: b(t)=c(t)+s(t)+n(t) Among them, c(t) represents harmonic interference, and n(t) represents Gaussian noise that obeys normal distribution; Harmonic interference c(t) is expressed as: c(t)=P1sin(2πh1t+θ1)+P2sin(2πh2t+θ2)+P1(0.5+0.3cos(2πh3t+θ3)) Among them, P1, P2, P3 represent the amplitude of each order, h1, h2, h3 are the frequencies of each order, and θ1, θ2, θ3 represent the phase of each order; The sampling frequency of all simulation signals is 10kHz and the sampling time is 1s.

3. The tower crane gearbox fault diagnosis method according to claim 2, characterized in that: The method of converting the global signal feature into the local feature by using the narrowband technology includes designing a specific narrowband structure represented as: Among them, s represents the original signal, μ represents the length of a specific frequency band, and α μ (k) and Δf represent the k-order cycle frequency and resolution, and the specific frequency band [α μA (k),α μB (k)] 2μ+1, μ is the length of a specific frequency band, α μ (k) and Δf represent the k-order cycle frequency and resolution; The specific frequency band is used to transform the global characteristics of the envelope spectrum into specific frequency band characteristics related to the fault and identify the fault characteristics; The construction process of cyclic spectrum negative entropy is to design a specific narrow-band structure; The transformed form of the input signal x is expressed as: y=x*f i Where y represents the filtered signal, f i and * denote the initial filter and the convolution operator, respectively.

4. The tower crane gearbox fault diagnosis method according to claim 3, characterized in that: The sparse characteristics based on spectral negative entropy are iterated to obtain the optimal filter including the square envelope spectrum expression EX of the fault feature in a specific frequency band. ψ It is expressed as: ∈ x =|y| 2 =|Xf| 2 Among them, ∈ x represents the square envelope spectrum of signal x, diag(y H ) represents the diagonal matrix of the transposed filtered signal, X represents the matrix form of the signal x, f represents the iterative filter matrix, represents the Fourier matrix containing the fault characteristics.

5. The tower crane gearbox fault diagnosis method according to claim 4, characterized in that: The original signal of the tower crane gearbox collected is filtered and envelope analyzed, and the Fourier matrix including the fault characteristics is expressed as: Where μ represents the length of a specific frequency band, N represents the length of the signal, L represents the filter length, and P k Indicates the position of the fault characteristic frequency in the total sampling points.

6. The tower crane gearbox fault diagnosis method according to claim 5, characterized in that: The original signal of the tower crane gearbox collected is filtered and envelope analyzed, including the position of the fault characteristic frequency in the total sampling points, which is expressed as: Where k = 1, 2, ... K, T and f s Indicates the fault cycle and sampling point; Cyclic spectrum negative entropy ΔI CEX The defined envelope spectrum is expressed as: Among them, ΔI CEX represents the cyclic spectrum negative entropy, H CEX represents the signal spectral entropy, EX ψ (*) indicates the square envelope spectrum, P k The position corresponding to the fault characteristic frequency, N represents the signal length.

7. The tower crane gearbox fault diagnosis method according to claim 6, characterized in that: The collected original signal of the tower crane gearbox is filtered to perform envelope analysis including the generalized Rayleigh entropy form ΔI of the related spectrum negative entropy CEX It is expressed as: Among them, ΔI CEX represents the cyclic spectral negative entropy, f H and X H They represent the transposed matrices of the filter matrix f and the original signal matrix X, respectively. diag(*) represents the diagonal matrix. Represents the corresponding fault characteristic component in the envelope spectrum, is the Fourier matrix The transpose of The maximum eigenvalue λ and the corresponding eigenvector output by the eigenvalue decomposition algorithm are equivalent to the maximum value of the generalized Rayleigh entropy of the filter coefficient f; The maximized eigenvalue is expressed as: in, represents the fault feature matrix; Represents the original signal matrix: in, represents the fault feature matrix, represents the original signal matrix; The cyclic spectrum negative entropy ΔI of the envelope spectrum CEX The maximization is expressed as: in, represents the fault feature matrix, represents the original signal matrix, and f represents the filter matrix.

8. A system using the tower crane gearbox fault diagnosis method according to any one of claims 1 to 7, characterized in that: Including local feature conversion module, optimal filter acquisition module, envelope analysis module; The local feature conversion module is used to convert the global signal features into local features using narrowband technology; The optimal filter acquisition module is used to utilize the sparse characteristics of spectral negentropy to obtain the optimal filter through iteration; The envelope analysis module is used to filter the collected original signal of the tower crane gearbox and then perform envelope analysis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for diagnosing a fault of a tower crane gearbox according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fault diagnosis method for a tower crane gearbox according to any one of claims 1 to 7 are implemented.

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