Gearbox fault diagnosis method and system based on cyclic pulse characteristic decomposition

Abstract

The invention belongs to the technical field of fault diagnosis, and particularly relates to a gearbox fault diagnosis method and system based on cyclic pulse characteristic decomposition, and the method comprises the steps: constructing a new dictionary matrix, namely a cyclic pulse dictionary matrix, to accurately estimate a pulse cycle period contained in a signal formed by mixing a plurality of periodic pulse signals; a Goertzel algorithm is adopted to obtain corresponding frequency amplitudes according to the magnitude sequence of the energy values of the pulse cycle periods; and separating and reconstructing the CPCC corresponding to the pulse cycle period, and decomposing the signal into a plurality of CPCCs. According to the invention, the cyclic pulse dictionary matrix is constructed to carry out sparse representation on the pulse cycle period of the periodic pulse signal, and the cycle period of the fault pulse characteristic in the gear vibration signal is accurately estimated; the Goertzel algorithm is adopted to decompose and reconstruct the CPCC in the gear vibration signal according to the pulse cycle period, and the decomposed CPCC contains abundant periodic pulse characteristic information.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of fault diagnosis, and in particular relates to a gearbox fault diagnosis method and system based on cyclic pulse feature decomposition. Background Art

[0002] Gears are key components for power and torque transmission in mechanical equipment. When they fail, they will cause the mechanical equipment to shut down or even cause safety accidents. Through the analysis of the principle of gear meshing, it is found that when two gears are in meshing state, the vibration signal generated by meshing has periodic characteristics. When a gear fails, it will generate periodic impact pulses. The periodic impact pulse signal contains fault information, and these periodic impact pulse signals are often mixed with the vibration and noise information generated by other parts of the mechanical equipment, resulting in the periodic fault information being submerged. Therefore, how to separate the periodic pulse signal from a lot of interference information is the key to diagnosing gear faults and accurately extracting fault information. Most researchers use different signal decomposition methods to separate the interference information and fault feature information in the original signal, extract the fault information in the vibration signal to obtain the current health status of the gear.

[0003] Empirical mode decomposition (EMD) can adaptively decompose the signal into multiple intrinsic mode functions (IMFs), but EMD will cause modal confusion when decomposing the signal. In order to overcome this defect, EEMD was proposed. EEMD repeatedly calculates the mean of IMF by adding white noise to the original signal to alleviate the problem of modal confusion. However, when the frequency components in the signal are too complex, the modal confusion problem still exists. WT, as an algorithm for decomposing signals from frequency domain characteristics, has been widely used in signal processing and rotating machinery fault diagnosis. WT decomposes vibration signals into single component signals with different characteristics by selecting appropriate wavelet basis functions, and fully mines the fault feature information hidden in the noise. However, WT has certain limitations. First, manual intervention is required to select appropriate wavelet basis functions before signal decomposition, which makes the algorithm not adaptive. Second, the essence of WT transformation is to replace the Fourier transform of window functions of different sizes, and the modal confusion phenomenon in the decomposition result cannot be eliminated. VMD has better performance than the current adaptive decomposition method in fault state monitoring, has equivalent filtering characteristics and good anti-interference ability, and can extract fault features from non-stationary data. However, the decomposition performance of VMD is constrained by the decomposition parameters. There is a problem that parameters such as the number of decomposed modes and decomposition bandwidth need to be set manually, and improper parameter selection will still cause modal confusion in the decomposition results.

[0004] Several classic signal decomposition methods were selected for analysis above, which can be classified into two categories. One is to decompose the signal from the perspective of the signal time domain: such as EMD and EEMD, which decompose by obtaining the time scale characteristics based on the signal itself; the other is to decompose the signal from the signal frequency domain characteristics: such as WT decomposes the signal into a series of linear combinations of wavelet functions to analyze the frequency characteristics of the signal; VMD decomposes the signal into multiple modal components with different frequency characteristics in a variational manner. However, the characteristic information of gear faults exists in the vibration signal in the form of periodic pulses. Through the analysis of the decomposition principles and decomposition components of the above decomposition methods, it is not difficult to find that when the original signal is a complex non-sinusoidal waveform signal, these methods will force it to be decomposed into several single waveform components. The decomposed components cannot represent the periodic pulse characteristics, resulting in the lack of physical meaning of the decomposition results.

[0005] Although the existing decomposition methods can extract the fault characteristics of gears from vibration signals to a certain extent, the decomposed components are still lacking in explaining the periodic impact pulse characteristics of gear faults. In view of the strong periodicity of the impact pulses generated by gear faults, the existing periodic signal separation algorithms are analyzed. Sethares and Staley proposed the periodic transform (PT) to obtain the periodic components in the signal by constructing a "periodic subspace". However, when the periodic components in the signal are complex, the periodic subspace cannot accurately obtain the hidden period in the signal, so PT cannot accurately characterize the periodic components in the signal. Vaidyanathan and Pal proposed to use the Farey dictionary to sparsely represent the hidden period of the signal and identify the hidden period in the signal. However, when the noise energy value in the signal is too high, it is necessary to introduce an optimal threshold to obtain the periodic information in the signal, and the different noise conditions of the signal make it particularly difficult to select the optimal threshold. Summary of the invention

[0006] In view of the problem in the prior art that it is difficult for current gear fault diagnosis methods to accurately extract the pulse cycle period of complex periodic pulse signals and separate the cyclic pulse feature information, the present invention provides a gearbox fault diagnosis method and system based on cyclic pulse feature decomposition.

[0007] The present invention is implemented as follows: a gearbox fault diagnosis method based on cyclic pulse feature decomposition, the method comprising:

[0008] S1: Construct a new dictionary matrix "cyclic pulse dictionary matrix" to accurately estimate the pulse cycle period contained in the signal composed of multiple periodic pulse signals;

[0009] S2: According to the energy value of the pulse cycle, the Goertzel algorithm is used to obtain the corresponding frequency amplitude;

[0010] S3: Separate and reconstruct the CPCC corresponding to the pulse cycle period, and decompose the signal into several CPCCs.

[0011] Further, the S1 specifically includes:

[0012] (1) DFT matrix

[0013] Since the DFT matrix satisfies the Vandermonde matrix condition and has different rows, the DFT matrix is ​​a full-rank matrix; in addition, given a vector Where W P =e -j2π / P , k is an integer and 1≤k≤P, rewrite it as where d i It is a divisor of P and is denoted by d i |P; as a vector in n, The vector has period d i ; Let φ(n) denote the Eulertotient function evaluated at n. Since k i There is φ(d i ) such values, so φ(d i ) in the form of The signal is exactly equal to d i If you consider For all values ​​of k in 1≤k≤P, each divisor d of P i The generated form is For each d i , obviously φ(d i ) such sequences, because k i There is φ(d i ) values; the total number of such sequences is:

[0014]

[0015] In summary, the P columns of the DFT matrix with dimension P×P can be divided into K categories, each of which is a factor d i |P; in class i there is φ(d i ) columns, can be aggregated into P×φ(d i )matrix Their form is The period is d i ; No column of this DFT matrix has d i | Cycles other than P;

[0016] For example, for the 8×8 DFT matrix in equation (5), the first column is a vector with a period of 1, the second column is a vector with a period of 2, the third and fourth columns are vectors with a period of 4, and the rest are vectors with a period of 8;

[0017]

[0018] (2) Construction of Circular Pulse Dictionary Matrix

[0019] According to the periodic characteristics of the above DFT matrix, the P×P DFT matrix D is expressed as:

[0020] D=(c1,c2,…,c P ) (6)

[0021] Among them, c P is the vector with period value P in the Pth column of matrix D, c P The dimension is P×1; let m=1,2,…,P-1, and the number of values ​​in m that are prime to the period P is count(■) and is denoted by L P :

[0022] L P =count(gcd(m,P)=1) (7)

[0023] Where gcd(m,P) means obtaining the greatest common factor of m and P, and reconstructing the DFT matrix into C P :

[0024]

[0025] in It is c P The cyclic shift form, for example, C8 in equation (9) is:

[0026]

[0027] Assume that the input data is x(n), the data length is N, and the matrix C is periodically P Copy and expand to form N×L P The matrix B P , truncate the last one if necessary Matrix C P The first h rows, where q = ceil(N / P), satisfying N = P × (q-1) + h;

[0028]

[0029] Building from 1 to P max B P Matrix, forming the circulant impulse dictionary matrix A:

[0030]

[0031] Among them, is a matrix of N×L;

[0032] (3) Cyclic pulse period estimation

[0033] If the given signal has a period less than P max If it is a periodic signal, then it must be a linear combination of dictionary columns; this is because, if it is a period with period P, then a column with period P as a divisor must be able to span it, so the system of equations in equation (12) must have a solution y, where x = (x(0), x(1), …, x(N-1)) T , the equations are:

[0034] x=Ay (12)

[0035] To solve the equation system (12), the orthogonal matching pursuit algorithm (OMP) is used, and the residual γ0 = x and the atomic support set is Support matrix A0 = 0, sparsity I = round (P max ×4), the number of iterations t = 1; first find the maximum column inner product index λ t :

[0036]

[0037] In the above formula, a j is the jth column of A, and then updates the atomic support set Λ t =Λ t-1 ∪λ t , update the support matrix A t =A t-1 ∪a λ , then find x=A t y t The least squares solution of :

[0038]

[0039] Finally, update the residual When t>I, the iteration ends, otherwise t=t+1, and the search for the maximum column inner product index λ continues t ; Let G(P) be the periodic energy value of x(n) in the current period P, and G(P) is defined as follows:

[0040]

[0041] The periodic energy value of the signal x(n) ranges from 1 to P max The change is recorded as S(U), where U = 1, 2, ..., P max , S(U) is defined as follows:

[0042] S(U)=(G(1),G(2),…,G(P max )) (16).

[0043] Further, the S2 specifically includes:

[0044] The Goertzel filtering algorithm is used to calculate the kth DFT component of a signal X(n) of length N. The specific expression is shown in formula (17):

[0045]

[0046] By multiplying both sides of the equation After sorting, we get:

[0047]

[0048] The right side of the above equation can be understood as X(n) and h k (n) discrete linear convolution, where u(l) is a unit step function, and l is an integer. The result of the convolution is expressed in the form of convolution as Y(n), where m is an integer, and we get:

[0049]

[0050] Comparing X(n) with Y(n), we can see that X(k) is the Nth sampling of the convolution result, as shown in formula (20):

[0051] X(k)=Y k (N) (20)

[0052] Where k is any non-negative integer, and the transformation equation is obtained by performing a Z transform on the output response:

[0053]

[0054] The above formula can be regarded as First, is a geometric series of common ratios. For |q|<1, that is, |z|>1, this sequence tends to converge. Integrating the summation results gives:

[0055]

[0056] The corresponding difference equation is:

[0057]

[0058] where y k (-1) = 0; introduce conjugate factors into both the numerator and denominator of the fraction (22) Construct a filter with second-order conjugate poles and obtain the transfer function of the second-order Goertzel algorithm:

[0059]

[0060] Goertzel filtering can obtain the amplitude information corresponding to a specific frequency. Therefore, the signal pulse cycle period can be estimated by combining the cyclic pulse dictionary matrix, and the frequency amplitude of the corresponding period can be extracted by Goertzel filtering.

[0061] Further, the S3 specifically includes:

[0062] According to formula (1), it can be deduced that, assuming that x(n) is a mixture of three non-sinusoidal periodic signals, n = 1, 2, ..., N, n∈Z, and their periods are P = {P1, P2, P3}, respectively, their exponential sum model is:

[0063]

[0064] The specific decomposition steps are as follows:

[0065] (1) Let r i =x(n), i=1, according to the signal r i The length and maximum estimated period P max (P max >P), construct from 1 to P max The cyclic pulse dictionary matrix A;

[0066]

[0067] (2) Using the OMP algorithm to solve the signal r i The solution y of the equation formed by the cyclic pulse dictionary matrix A is:

[0068] r i =Ay (27)

[0069] (3) Calculate the number of y from 1 to P max The periodic energy value sequence S(U) is obtained, and the period PM where the maximum energy value of S(U) is located is obtained;

[0070]

[0071] S(U)=(G(1),G(2),…,G(P max )) (29)

[0072] PM=max(S(U)) (30)

[0073] (4) Let S(PM) = 0. According to the period PM, the signal r is extracted by the Goertzel filtering algorithm shown in equation (20). i The frequency amplitude sequence X corresponding to the PM in the middle period PM :

[0074] X PM =(Y1(PM),Y2(PM),…,Y PM-1 (PM)) (31)

[0075] Among them, X PM The corresponding frequency is

[0076] (5) For non-frequency F PM Amplitude within index X PM Perform zero-filling interpolation. After zero-filling interpolation, X PM1 The data length is And X PM1 Mirror flip constitutes bilateral spectrum X PM2 , where X PM2 The length is N, and for X PM2 Perform IFFT transformation to restore the cyclic pulse characteristic component CPCC corresponding to the periodic PM i , to achieve the reconstruction of the characteristic components of the cyclic pulse;

[0077] CPCC i =IFFT(X PM2 ,N) (32)

[0078] r i+1 =r i -CPCC i (33)

[0079] (6) If i>count(S(U)>0) or i>P max Then the iteration is terminated, otherwise i=i+1, and the process returns to step (3) and repeats the iteration process.

[0080] Another object of the present invention is to provide a gearbox fault diagnosis system based on cyclic pulse feature decomposition based on the gearbox fault diagnosis method based on cyclic pulse feature decomposition, the system specifically comprising:

[0081] A matrix construction module is used to construct a new dictionary matrix "cyclic pulse dictionary matrix" to accurately estimate the pulse cycle period contained in a signal formed by mixing multiple periodic pulse signals;

[0082] The frequency amplitude acquisition module is connected to the matrix construction module, and uses the Goertzel algorithm to obtain the corresponding frequency amplitude according to the energy value order of the pulse cycle period;

[0083] The separation and reconstruction module is connected to the frequency amplitude acquisition module, separates and reconstructs the CPCC corresponding to the pulse cycle period, and decomposes the signal into several CPCCs.

[0084] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the gearbox fault diagnosis method based on cyclic pulse feature decomposition.

[0085] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the gearbox fault diagnosis method based on cyclic pulse feature decomposition.

[0086] Another object of the present invention is to provide an information data processing terminal, which is used to implement the gearbox fault diagnosis method system based on cyclic pulse feature decomposition.

[0087] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0088] First, the current gear fault diagnosis method is difficult to accurately extract the pulse cycle period of complex periodic pulse signals and separate the cycle pulse feature information. The present invention proposes a new signal decomposition method, namely the CICD method, which accurately estimates the pulse cycle period in the vibration signal by constructing a cycle pulse dictionary matrix, and uses the Goertzel algorithm to complete the separation of the pulse component information in the vibration signal according to the pulse cycle period, and reconstructs the CPCC of each pulse cycle period, thereby realizing the extraction of periodic impact fault information in the gear fault vibration signal. The specific conclusions are as follows:

[0089] (1) A cyclic pulse dictionary matrix is ​​constructed to sparsely represent the pulse cycle period of the periodic pulse signal, and the cycle period of the fault pulse feature in the gear vibration signal is accurately estimated.

[0090] (2) The Goertzel algorithm is used to decompose and reconstruct the CPCC in the gear vibration signal according to the pulse cycle period. The decomposed CPCC contains rich periodic pulse feature information.

[0091] (3) The simulation and experimental wave analysis results show that the CICD method can accurately extract the fault characteristics of the gear. The decomposed CPCC envelope spectrum is concise and not affected by other interference components. Compared with the existing signal decomposition methods, the decomposition results of this method are more explanatory of the gear fault characteristics, and the decomposition ability and noise robustness of complex periodic pulse signals are superior.

[0092] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0093] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0094] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0095] The current fault diagnosis method for gear shock pulse extraction provides a gearbox fault diagnosis method and system based on cyclic pulse feature decomposition, which solves the problem of difficulty in obtaining gear shock pulse feature information in vibration signals under noise interference, and fills the technical gap in the method of extracting shock pulse features from vibration signals at home and abroad.

[0096] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0097] The technical solution of the present invention accurately estimates the pulse cycle period in the vibration signal by constructing a cyclic pulse dictionary matrix, uses the Goertzel algorithm to separate the pulse component information in the vibration signal according to the pulse cycle period, and reconstructs the CPCC of each pulse cycle period, thereby realizing the automatic extraction of periodic impact fault information in the gear fault vibration signal and the intelligent diagnosis of gear faults.

[0098] Thirdly, the technical solution of the present invention provides a diagnostic method based on cyclic pulse feature decomposition in the field of gearbox fault diagnosis, which solves the key problems in the prior art, mainly manifested in the solution of the following technical problems and the significant technical progress brought about:

[0099] 1. Technical problems solved

[0100] (1) Periodic pulse signals are difficult to separate accurately

[0101] Existing fault diagnosis methods are easily interfered by noise and other periodic components when processing complex periodic pulse signals, making it difficult to accurately identify the cycle period of the fault signal, especially when multiple pulse signals are mixed, and the signal decomposition accuracy is low. The present invention constructs a "cyclic pulse dictionary matrix" and uses its periodic characteristics to effectively decompose the signal, which can more accurately estimate the cycle period of multiple periodic pulse signals and improve the accuracy of fault feature separation.

[0102] (2) Inaccurate extraction of frequency amplitude

[0103] In the process of frequency amplitude extraction, the traditional fault diagnosis method cannot accurately extract key frequency information due to method limitations, which affects the accuracy of fault diagnosis. The present invention adopts the Goertzel algorithm to analyze the frequencies corresponding to different pulse periods and extract the amplitudes of the corresponding frequencies, which effectively overcomes the deviation problem in frequency extraction and ensures the accuracy of the diagnosis results.

[0104] (3) Unable to effectively handle the separation and reconstruction of multiple periodic signals

[0105] When facing the separation of multiple periodic signals, the existing technology often cannot effectively realize accurate signal reconstruction, resulting in incomplete capture of fault characteristics. The present invention uses the pulse cycle decomposition and reconstruction technology to achieve accurate signal decomposition and reconstruction using the cyclic pulse characteristic component (CPCC), so that the characteristic information of each pulse cycle can be effectively captured and restored, thereby improving the accuracy and comprehensiveness of fault diagnosis.

[0106] 2. Technological advancement

[0107] (1) The accuracy of fault feature extraction is greatly improved

[0108] By introducing the eigendecomposition technology based on the cyclic pulse dictionary matrix, the present invention can accurately separate the complex periodic signals in the gearbox and extract the precise frequency amplitude through the Goertzel algorithm. This method significantly improves the resolution of fault signals, so that subtle fault features that were originally difficult to identify can also be captured, greatly improving the accuracy of gearbox fault diagnosis.

[0109] (2) Improved efficiency of multi-periodic signal decomposition

[0110] The present invention uses the periodic characteristics of the DFT matrix to construct an efficient cyclic pulse dictionary matrix, which can quickly decompose and reconstruct multiple periodic pulse signals. Compared with traditional methods, the efficiency of processing multi-periodic signals is greatly improved, which not only shortens the diagnosis time, but also ensures the accuracy and completeness of the diagnosis results. It is particularly suitable for online monitoring and real-time fault detection in industrial scenarios.

[0111] (3) Enhanced robustness of diagnostic methods

[0112] Since the orthogonal matching pursuit algorithm (OMP) is used to solve the sparse solution between the signal and the dictionary matrix, the robustness of the present invention to noise is enhanced, and the accurate extraction of fault signals can be guaranteed even under high noise interference. This technological advancement effectively solves the impact of noise interference on fault diagnosis accuracy in industrial environments, allowing the system to maintain high performance under complex working conditions.

[0113] (4) Improved automation level of fault diagnosis

[0114] Through a series of algorithm optimization, the present invention realizes the automated processing flow from signal acquisition, cycle estimation, frequency extraction to fault feature reconstruction, reduces the necessity of human intervention, and improves the automation level of the diagnostic system. This technology is particularly suitable for online monitoring of complex mechanical systems, and can automatically detect and alarm gearbox faults in real time, reducing labor costs and improving operation and maintenance efficiency.

[0115] (5) Comprehensiveness of signal decomposition

[0116] Traditional fault diagnosis methods have limited processing capabilities for single or a small number of periodic signals. The present invention achieves comprehensive decomposition and reconstruction of multi-periodic pulse signals by constructing a cyclic pulse dictionary matrix. This technological advancement ensures that no matter how many different periodic signals appear in the gearbox, they can be accurately identified and decomposed, greatly improving the comprehensiveness and applicability of diagnosis.

[0117] (6) Expansion of industrial application scope

[0118] The gearbox fault diagnosis method of the present invention is not only applicable to traditional gearboxes, but can also be extended to other mechanical equipment fault diagnosis fields containing periodic signals, such as motors, bearings, transmission systems, etc. This scalability provides fault diagnosis technical support for more industries and improves the maintenance efficiency and reliability of the overall industrial system.

[0119] In summary, the present invention solves the problems of periodic signal separation and frequency extraction in gearbox fault diagnosis by combining cyclic pulse feature decomposition and Goertzel algorithm, and achieves significant technical progress in terms of accuracy, efficiency, robustness and automation level. BRIEF DESCRIPTION OF THE DRAWINGS

[0120] Figure 1 is a flow chart of a gearbox fault diagnosis method based on cyclic pulse feature decomposition provided by an embodiment of the present invention;

[0121] Figure 2 is a flow chart of the CICD method provided by an embodiment of the present invention;

[0122] Figure 3 It is a gear impact fault signal simulation provided by an embodiment of the present invention;

[0123] Figure 4 It is a time domain diagram of the impact attenuation simulation signal and the period unit pulse simulation signal of the faulty gear provided by the embodiment of the present invention;

[0124] Figure 5 The gear fault vibration impact pulse sequence simulation signal and its envelope spectrum provided by the embodiment of the present invention; (a) simulation signal time domain diagram; (b) simulation signal envelope spectrum;

[0125] Figure 6 The components and envelope spectra of the simulated signal x(t) decomposed by the CICD method provided by an embodiment of the present invention; (a) the first four CPCC components decomposed and the decomposition residuals; (b) the envelope spectra of the first four CPCC components;

[0126] Figure 7 The components and envelope spectra of the simulation signal x(t) decomposed by the WT decomposition method provided by the embodiment of the present invention; (a) the first four d components decomposed and the decomposition residual; (b) the envelope spectra of the first four d components;

[0127] Figure 8 The components and envelope spectra of the simulated signal x(t) decomposed by the VMD decomposition method provided by the embodiment of the present invention; (a) the first four IMF components decomposed and the decomposition residual; (b) the envelope spectra of the first four IMF components;

[0128] Fig. 9 The components and envelope spectra of the simulation signal x(t) decomposed by the EEMD decomposition method provided in the embodiment of the present invention; (a) the first four IMF components decomposed and the decomposition residual; (b) the envelope spectra of the first four IMF components;

[0129] Fig.10 A steering system gear pump fault test bench provided by an embodiment of the present invention;

[0130] Fig.11 The faulty driven gear of the gear pump provided by the embodiment of the present invention is: (a) broken gear; (b) tooth surface wear;

[0131] Fig.12 The following are the time domain diagram of the vibration signal of the broken tooth fault and its envelope spectrum provided by the embodiment of the present invention; (a) the time domain diagram of the vibration signal; (b) the envelope spectrum of the vibration signal;

[0132] Fig.13 The components and envelope spectra of the broken tooth fault vibration signal decomposed by the CICD method provided in the embodiment of the present invention; (a) the first four CPCC components decomposed and the decomposition residual; (b) the envelope spectra of the first four CPCC components;

[0133] Fig.14 The components and envelope spectra of the broken tooth fault vibration signal decomposed by the WT method provided in the embodiment of the present invention; (a) the first four d components decomposed and the decomposition residual; (b) the envelope spectra of the first four d components;

[0134] Fig.15 The components and envelope spectra of the broken tooth fault vibration signal decomposed by the VMD method provided in the embodiment of the present invention; (a) the first four IMF components decomposed and the decomposition residual; (b) the envelope spectra of the first four IMF components;

[0135] Fig.16 The components and envelope spectra of the broken tooth fault vibration signal decomposed by the EEMD method provided in the embodiment of the present invention; (a) the first four IMF components decomposed and the decomposition residual; (b) the envelope spectra of the first four IMF components;

[0136] Fig.17 The figure is a time domain diagram of a tooth surface wear fault vibration signal and its envelope spectrum provided by an embodiment of the present invention; (a) a time domain diagram of a vibration signal; (b) an envelope spectrum of a vibration signal;

[0137] Fig.18 The components and envelope spectra of the tooth surface wear fault vibration signal decomposed by the CICD method provided in the embodiment of the present invention; (a) the first four CPCC components decomposed and the decomposition residual; (b) the envelope spectra of the first four CPCC components;

[0138] Fig.19 The components and envelope spectra of the tooth surface wear fault vibration signal decomposed by the WT method provided in the embodiment of the present invention; (a) the first four d components decomposed and the decomposition residual; (b) the envelope spectra of the first four d components;

[0139] Fig. 20 The components and envelope spectra of the tooth surface wear fault vibration signal decomposed by the VMD method provided in the embodiment of the present invention; (a) the first four IMF components decomposed and the decomposition residual; (b) the envelope spectra of the first four IMF components;

[0140] Fig.21 The components and envelope spectra of the tooth surface wear fault vibration signal decomposed by the EEMD method provided in an embodiment of the present invention; (a) the first four IMF components decomposed and the decomposition residual; (b) the envelope spectra of the first four IMF components. DETAILED DESCRIPTION

[0141] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0142] The following are two specific industrial application embodiments of the gearbox fault diagnosis method based on cyclic pulse feature decomposition according to the present invention:

[0143] Example 1: Wind turbine gearbox fault diagnosis system

[0144] In the field of wind power generation, the gearbox is one of the core components of wind turbines. Its operating environment is complex and the load changes greatly. Gearbox failure will directly affect the power generation efficiency of the wind turbine and even cause the unit to shut down. Since wind power generation equipment is usually located in remote areas, the cost of regular maintenance and inspection is high, and there is a problem of untimely detection. Therefore, an efficient and accurate fault diagnosis method is needed.

[0145] Application scenarios:

[0146] Vibration signal collection: The gearbox of the wind turbine generates a periodic vibration signal during operation. The present invention continuously collects vibration data of the equipment during operation by installing a vibration sensor on the gearbox.

[0147] Application of cyclic pulse dictionary matrix: Utilizing the cyclic pulse feature decomposition method of the present invention, a cyclic pulse dictionary matrix is ​​constructed to separate and reconstruct the periodic pulse signals of the gearbox, accurately estimate the cycle period of each pulse signal, and extract potential fault features.

[0148] Real-time fault detection and early warning: The Goertzel algorithm is used to quickly extract key frequency amplitudes and perform spectrum analysis to determine whether the gearbox is operating abnormally. If an abnormal signal is detected, the system automatically triggers a fault alarm and sends an early warning message to the operation and maintenance team.

[0149] Industrial benefits:

[0150] Improve operation and maintenance efficiency: The present invention can effectively improve the accuracy and real-time performance of gearbox fault diagnosis, reduce the frequency of on-site manual inspections, and reduce maintenance costs.

[0151] Reduce downtime: By real-time monitoring of the gearbox operating status, timely warning can be given before a fault occurs, avoiding long equipment downtime due to major faults, thereby improving the power generation efficiency and equipment utilization of wind turbines.

[0152] Example 2: Mine belt conveyor gearbox fault monitoring system

[0153] In the mining process, the belt conveyor is the core conveying equipment. Its gearbox often works in harsh environments such as high load, dust and high humidity, and is prone to fatigue damage and wear. Once the gearbox fails, the entire conveying system will stop operating, directly affecting the transportation efficiency of the ore. Therefore, early diagnosis and real-time monitoring of the fault must be carried out.

[0154] Application scenarios:

[0155] Gearbox vibration signal monitoring: The gearbox of the conveying equipment in the mine is in long-term operation, and the vibration sensor can be installed in the key position to collect the vibration signal during the operation. Through the method of the present invention, these signals are input into the cyclic pulse dictionary matrix for decomposition and analysis.

[0156] Gearbox periodic signal analysis and reconstruction: The present invention separates the pulse cycle period in the mixed signal, uses the Goertzel filtering algorithm to extract the gearbox fault frequency information, compares these frequencies with the normal operating frequency, and detects the abnormality of the gearbox.

[0157] Intelligent fault diagnosis and operation and maintenance decision support: Through the method of the present invention, the system can quickly determine the severity of the gearbox failure in the early stage of mining conveying equipment, and provide maintenance suggestions to the operator to support the intelligent maintenance decision.

[0158] Industrial benefits:

[0159] Extend equipment life: By timely detecting minor faults in the gearbox, it is possible to avoid the equipment from continuing to operate in a serious fault state, thereby reducing wear and extending the service life of the gearbox.

[0160] Improve production efficiency: The present invention can monitor the gearbox status in real time, reduce production stagnation caused by sudden failures, ensure the stable operation of mine conveying equipment, and thus improve mine production efficiency.

[0161] These two embodiments demonstrate the practical application of the present invention in the wind power generation and mining industries. Through accurate fault diagnosis methods, real-time monitoring and fault warning of equipment are achieved, which significantly improves the equipment operation efficiency and the economic benefits of the enterprise.

[0162] like Figure 1 , 2 As shown, an embodiment of the present invention provides a gearbox fault diagnosis method based on cyclic pulse feature decomposition, the method comprising:

[0163] S1: Construct a new dictionary matrix "cyclic pulse dictionary matrix" to accurately estimate the pulse cycle period contained in the signal composed of multiple periodic pulse signals;

[0164] S2: According to the energy value of the pulse cycle, the Goertzel algorithm is used to obtain the corresponding frequency amplitude;

[0165] S3: Separate and reconstruct the CPCC corresponding to the pulse cycle period, and decompose the signal into several CPCCs.

[0166] The S1 specifically includes:

[0167] (1) DFT matrix

[0168] Since the DFT matrix satisfies the Vandermonde matrix condition and has different rows, the DFT matrix is ​​a full-rank matrix; in addition, given a vector Where W P =e -j2π / P , k is an integer and 1≤k≤P, rewrite it as where d i It is a divisor of P and is denoted by d i |P; as a vector in n, The vector has period d i ; Let φ(n) denote the Eulertotient function evaluated at n. Since k i There is φ(d i ) such values, so φ(d i ) in the form of The signal is exactly equal to d i If you consider For all values ​​of k in 1≤k≤P, each divisor d of P i The generated form is For each d i , obviously φ(d i ) such sequences, because k i There is φ(d i ) values; the total number of such sequences is:

[0169]

[0170] In summary, the P columns of the DFT matrix with dimension P×P can be divided into K categories, each of which is a factor d i |P; in class i there is φ(d i ) columns, can be aggregated into P×φ(d i )matrix Their form is The period is d i ; No column of this DFT matrix has d i | Cycles other than P;

[0171] For example, for the 8×8 DFT matrix in equation (5), the first column is a vector with a period of 1, the second column is a vector with a period of 2, the third and fourth columns are vectors with a period of 4, and the rest are vectors with a period of 8;

[0172]

[0173] (2) Construction of Circular Pulse Dictionary Matrix

[0174] According to the periodic characteristics of the above DFT matrix, the P×P DFT matrix D is expressed as:

[0175] D=(c1,c2,…,c P ) (6)

[0176] Among them, c P is the vector with period value P in the Pth column of matrix D, c P The dimension is P×1; let m=1,2,…,P-1, and the number of values ​​in m that are prime to the period P is count(■) and is denoted by L P :

[0177] L P =count(gcd(m,P)=1) (7)

[0178] Where gcd(m,P) means obtaining the greatest common factor of m and P, and reconstructing the DFT matrix into C P :

[0179]

[0180] in It is c P The cyclic shift form, for example, C8 in equation (9) is:

[0181]

[0182] Assume that the input data is x(n), the data length is N, and the matrix C is periodically P Copy and expand to form N×L P The matrix B P , truncate the last one if necessary Matrix C P The first h rows, where q = ceil(N / P), satisfying N = P × (q-1) + h;

[0183]

[0184] Building from 1 to P max B P Matrix, forming the circulant impulse dictionary matrix A:

[0185]

[0186] Among them, is a matrix of N×L;

[0187] (3) Cyclic pulse period estimation

[0188] If the given signal has a period less than P maxIf it is a periodic signal, then it must be a linear combination of dictionary columns; this is because, if it is a period with period P, then a column with period P as a divisor must be able to span it, so the system of equations in equation (12) must have a solution y, where x = (x(0), x)1), ..., x(N-1)) T , the equations are:

[0189] x=Ay (12)

[0190] To solve the equation system (12), the orthogonal matching pursuit algorithm (OMP) is used, and the residual γ0 = x and the atomic support set is Support matrix A0 = 0, sparsity I = round (P max ×4), the number of iterations t = 1; first find the maximum column inner product index λ t :

[0191]

[0192] In the above formula, a j is the jth column of A, and then updates the atomic support set Λ t =Λ t-1 ∪λ t , update the support matrix A t =A t-1 ∪a λ , then find x=A t y t The least squares solution of :

[0193]

[0194] Finally, update the residual When t>I, the iteration ends, otherwise t=t+1, and the search for the maximum column inner product index λ continues t ; Let G(P) be the periodic energy value of x(n) in the current period P, and G(P) is defined as follows:

[0195]

[0196] The periodic energy value of the signal x(n) ranges from 1 to P max The change is recorded as S(U), where U = 1, 2, ..., P max , S(U) is defined as follows:

[0197] S(U)=(G(1),G(2),…,G(P max )) (16).

[0198] The S2 specifically includes:

[0199] The Goertzel filtering algorithm is used to calculate the kth DFT component of a signal X(n) of length N. The specific expression is shown in formula (17):

[0200]

[0201] By multiplying both sides of the equation After sorting, we get:

[0202]

[0203] The right side of the above equation can be understood as X(n) and h k (n) discrete linear convolution, where u(l) is a unit step function, and l is an integer. The result of the convolution is expressed in the form of convolution as Y(n), where m is an integer, and we get:

[0204]

[0205] Comparing X(n) with Y(n), we can see that X(k) is the Nth sampling of the convolution result, as shown in formula (20):

[0206] X(k)=Y k (N) (20)

[0207] Where k is any non-negative integer, and the transformation equation is obtained by performing a Z transform on the output response:

[0208]

[0209] The above formula can be regarded as First, is a geometric series of common ratios. For |q|<1, that is, |z|>1, this sequence tends to converge. Integrating the summation results gives:

[0210]

[0211] The corresponding difference equation is:

[0212]

[0213] Where yk(-1) = 0; introduce conjugate factors into both the numerator and denominator of the fraction (22): Construct a filter with second-order conjugate poles and obtain the transfer function of the second-order Goertzel algorithm:

[0214]

[0215] Goertzel filtering can obtain the amplitude information corresponding to a specific frequency. Therefore, the signal pulse cycle period can be estimated by combining the cyclic pulse dictionary matrix, and the frequency amplitude of the corresponding period can be extracted by Goertzel filtering.

[0216] The S3 specifically includes:

[0217] According to formula (1), it can be deduced that, assuming that x(n) is a mixture of three non-sinusoidal periodic signals, n = 1, 2, ..., N, n∈Z, and their periods are P = {P1, P2, P3}, respectively, their exponential sum model is:

[0218]

[0219] The specific decomposition steps are as follows:

[0220] (1) Let r i =x(n), i=1, according to the signal r i The length and maximum estimated period P max (P max >P), construct from 1 to P max The cyclic pulse dictionary matrix A;

[0221]

[0222] (2) Using the OMP algorithm to solve the signal r i The solution y of the equation formed by the cyclic pulse dictionary matrix A is:

[0223] r i =Ay (27)

[0224] (3) Calculate the number of y from 1 to P max The periodic energy value sequence S(U) is obtained, and the period PM where the maximum energy value of S(U) is located is obtained;

[0225]

[0226] S(U)=(G(1),G(2),…,G(P max )) (29)

[0227] PM=max(S(U)) (30)

[0228] (4) Let S(PM) = 0. According to the period PM, the signal r is extracted by the Goertzel filtering algorithm shown in equation (20). i The frequency amplitude sequence X corresponding to the PM in the middle period PM :

[0229] X PM=(Y1(PM),Y2(PM),…,Y PM-1 (PM)) (31)

[0230] Among them, X PM The corresponding frequency is

[0231] (5) For non-frequency F PM Amplitude within index X PM Perform zero-filling interpolation. After zero-filling interpolation, X PM1 The data length is And X PM1 Mirror flip constitutes bilateral spectrum X PM2 , where X PM2 The length is N, and for X PM2 Perform IFFT transformation to restore the cyclic pulse characteristic component CPCC corresponding to the periodic PM i , to achieve the reconstruction of the characteristic components of the cyclic pulse;

[0232] CPCC i =IFFT(X PM2 ,N) (32)

[0233] r i+1 =r i -CPCC i (33)

[0234] (6) If i>count(S(U)>0) or i>P max Then the iteration is terminated, otherwise i=i+1, and the process returns to step (3) and repeats the iteration process.

[0235] Gear impact type fault vibration mechanism:

[0236] When an impact fault occurs in a gear pair, since the gear meshing lasts for a short time, the gear impact fault can be represented by a periodic impact pulse sequence, and the interval between periodic pulses is the gear rotation frequency. Through the analysis of the gear meshing principle, it can be seen that gear meshing is divided into two processes: starting meshing and disengaging meshing. When a gear has a defect, the meshing point generates a gradually increasing and then gradually attenuated impact pulse from entering to disengaging the defect position. The single impact attenuation signal of the gear impact fault can be represented by formula (1):

[0237]

[0238] Where T is the time range of the shock attenuation wavelet, ω d =2πf z , f zis the center frequency of the shock attenuation wavelet, and ξ is the attenuation index of the shock attenuation wavelet. From the convolution characteristics of the signal, it can be seen that the periodic shock pulse sequence can be formed by convolving a single shock attenuation signal with a periodic unit pulse signal. The expression of the periodic unit pulse signal is:

[0239]

[0240] In the formula, k i is the number of unit pulse signals, i=1,2,…,Z,P i is the unit pulse period interval. The convolution of the signals in equation (1) and equation (2) can be expressed as:

[0241] x i (t) = x a (t)*δ i (t) (3)

[0242] According to the periodic shock pulse sequence constructed by formula (1-3), the sampling frequency fs of the signal is set to 1000 Hz, and the shock attenuation wavelet x a (t) The time range T is set to 5s, and the center frequency f z Set to 300Hz, k1 of the periodic unit pulse signal δ1(t) is set to 17, and the period interval P1 is set to 283. Shock attenuation simulation signal x a (t), periodic unit pulse simulation signal δ1(t), periodic shock pulse sequence x1(t) Figure 1 shown.

[0243] 1. Specific application fields or related products of the present invention.

[0244] The embodiment of the present invention provides a gearbox fault diagnosis system based on cycle pulse feature decomposition based on the gearbox fault diagnosis method based on cycle pulse feature decomposition, and the system specifically includes:

[0245] A matrix construction module is used to construct a new dictionary matrix "cyclic pulse dictionary matrix" to accurately estimate the pulse cycle period contained in a signal formed by mixing multiple periodic pulse signals;

[0246] The frequency amplitude acquisition module is connected to the matrix construction module, and uses the Goertzel algorithm to obtain the corresponding frequency amplitude according to the energy value order of the pulse cycle period;

[0247] The separation and reconstruction module is connected to the frequency amplitude acquisition module, separates and reconstructs the CPCC corresponding to the pulse cycle period, and decomposes the signal into several CPCCs.

[0248] An embodiment of the present invention provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the gearbox fault diagnosis method based on cyclic pulse feature decomposition.

[0249] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the gearbox fault diagnosis method based on cyclic pulse feature decomposition.

[0250] An embodiment of the present invention provides an information data processing terminal, which is used to implement the gearbox fault diagnosis method system based on cyclic pulse feature decomposition.

[0251] 2. Relevant evidence of the technical effects obtained by the embodiments of the present invention.

[0252] 1. Simulation signal analysis

[0253] According to the vibration characteristics of gear impact fault, the simulation signal of gear fault periodic impact vibration is constructed by formula (1-3), the signal sampling frequency fs = 1000 Hz, the impact wavelet time range T = 5s, and the impact attenuation wavelet center frequency f z =300Hz, number of unit pulse signals k1=17, k2=28, k3=54, unit pulse signal x b1 (t) Period interval P1 = 283, P2 = 177, P3 = 91, impact attenuation simulation signal x of faulty gear a (t and periodic unit pulse simulation signals δ1(t), δ2(t), δ3(t) as Figure 4 shown.

[0254] Gear fault vibration shock pulse sequence simulation signal and its envelope spectrum Figure 5 As shown, Figure 5 (a) x1(t) = x a (t)*δ1(t),x2(t)=0.7(x a (t)*δ2(t)), x3(t)=0.5(x a (t)*δ3(t)), x(t)=x1(t)+x2(t)+x3(t)+e(n), where e(n) is -8dB Gaussian white noise. The three constructed signals are periodic impact pulse signals with different fault degrees. Envelope spectrum analysis is performed on x1(t), x2(t), x3(t), and x(t), respectively. The envelope spectrum is as follows: Figure 5 (b) as shown. Figure 5In (b), it can be clearly seen that the first harmonic frequencies of x1(t), x2(t), and x3(t) are 3.6 Hz, 5.6 Hz, and 11 Hz, respectively. Due to the interference of Gaussian noise, the first harmonic frequencies of x1(t), x2(t), and x3(t) cannot be distinguished in the envelope spectrum of x(t). Therefore, it is impossible to extract the fault frequency of the constructed gear fault simulation signal by directly performing envelope analysis on the simulation signal.

[0255] In order to verify the superiority of the CICD method, WT, EEMD and VMD are compared with the proposed method CICD, and the simulation signal x(t) is decomposed respectively. The first four components of the decomposition results and their envelope spectra are shown in Figure 6-Figure 9 shown.

[0256] Figure 6 (a) is the time domain diagram decomposed by CICD method. Figure 6 (a) The decomposition results clearly show that the three gear fault vibration shock pulse sequences constructed are successfully separated from x(t). Figure 6 (b) The envelope spectrum of the cyclic pulse characteristic component is obtained by the CICD method. The first harmonic frequencies in the envelope spectra of CPCC1, CPCC2, and CPCC3 components are 3.6 Hz, 5.6 Hz, and 11 Hz, respectively, which are consistent with the first harmonic frequencies of the constructed simulation signals x1(t), x2(t), and x3(t), and the frequency doubling information of the first harmonic in each component is obvious. Therefore, the CICD method can effectively separate the gear fault simulation signal component from x(t).

[0257] Figure 7 (a) and Figure 8 (a) are the components decomposed by the WT method and the VMD method respectively. The WT decomposition wavelet uses the 8th-order Daubechies wavelet, and the decomposition level is 10. The decomposition results of the first 4 levels are taken to draw the envelope spectrum. The envelope spectrum Figure 7 As shown in (b), VMD takes the first four decomposition components for envelope analysis, and its envelope spectrum is as follows Figure 8 (b) shows that Figure 7 (b) Figure 8 The frequencies with more prominent amplitudes in (b) only include the first harmonic frequency of the simulation signal x3(t), and the first harmonic frequencies of x1(t) and x2(t) cannot be extracted. Therefore, the WT method and VMD method cannot completely separate the fault frequency contained in x(t). Fig. 9 is the component decomposed by EEMD method and its envelope spectrum. Fig. 9(b) only contains the first harmonic frequency of the simulation signal x2(t), while the first harmonic frequencies of x1(t) and x3(t) cannot be separated from the noise. Therefore, the EEMD method cannot completely separate the fault frequency contained in x(t).

[0258] Through the verification and analysis results of the above gear fault simulation signal decomposition using four methods, it can be seen that the CICD decomposition method can separate components with different fault degrees and effectively extract the fault impact component of the periodic component. Therefore, the CICD decomposition method proposed in this paper is suitable for gear fault diagnosis of different degrees.

[0259] 4. Experimental signal analysis

[0260] In order to further illustrate the advantages of CICD method in gear fault diagnosis, this section will use CICD method to diagnose the fault vibration data of steering system gear pump fault test bench. Fig. 9 As shown in the figure, the test bench consists of a pressure sensor, an electromagnetic reversing valve, an acceleration sensor, a gear pump, a test gear, a servo motor, a stop valve, a hydraulic cylinder, an oil tank, and a pressure gauge. The fault types include three fault states: tooth surface wear, partial tooth breakage, and missing teeth. The number of teeth on the driving wheel and the driven wheel of the gear pump are both 10. During the test, the acceleration sensor is installed at the oil inlet, oil outlet, and top of the gear pump. The specific installation direction is as follows: Fig.10 As shown in the mark, ①Pressure sensor ②Electromagneticreversing valve ③Acceleration sensor 1 ④Acceleration sensor 2 ⑤Gearpump ⑥Acceleration sensor 3 ⑦Servo motor ⑧Stop valve ⑨Hydraulic cylinder ⑩Oil tank Pressure gauge.

[0261] The effectiveness of the CICD algorithm is verified by the vibration data of gear tooth breakage fault and tooth surface wear fault test. The gear pump driven gear tooth breakage fault and tooth surface wear fault are as follows: Fig.11 As shown. First, the envelope analysis of the original signal is performed to determine that the data is not directly diagnosable. Then, the CICD is compared with the WT, VMD, and EEMD decomposition methods. The vibration signals of the two faults are decomposed using the four decomposition methods, and the components decomposed by the four methods are respectively subjected to envelope analysis to verify whether the four methods can effectively separate the fault characteristic frequencies masked by noise in the vibration signal.

[0262] (1) Gear tooth breakage

[0263] In the test, a broken tooth fault was set on the driven wheel of the gear pump. The sampling frequency of the acquisition card was set to 4096Hz, the speed of the gear pump was set to 1200rmp, and the characteristic frequency of the broken tooth fault of the driven gear was 20Hz. Fig.12 (a) is the time domain diagram of the vibration signal of the broken tooth fault. Fig.12 (b) is the envelope spectrum, and the red dotted line in the envelope spectrum is the double frequency f of the fault characteristic frequency. g1 and the double frequency f g2 ,It can be seen from the envelope spectrum that the fault characteristic frequency of the first and second harmonics is covered by noise. Therefore, it is impossible to extract the fault characteristic frequency by directly analyzing the original vibration signal by envelope spectrum. The CICD algorithm is used to diagnose the gear tooth breakage fault, and WT, VMD, and EEMD are used for experimental comparison. The decomposition results and envelope spectra of the four decomposition methods are shown in Figure 13-Figure 16 shown.

[0264] Fig.13 The results of using CICD algorithm to decompose the vibration signal of broken tooth fault and the envelope spectrum of the first four components are shown in Figure 2. Fig.13 It can be seen from the envelope spectrum in (b) that the first harmonic frequency in the envelope spectrum of CPCC1 is close to the fault characteristic frequency f g1 The fault characteristic frequency is extracted accurately. The first harmonic frequency in the envelope spectrum of CPCC2 is consistent with f g2 The first harmonic frequency in the envelope spectrum of CPCC3 and CPCC4 is close to f g1 near.

[0265] Fig.14 The components obtained by using WT to decompose the vibration signal of broken tooth fault and the envelope spectrum of the first four components. Fig.13 The frequency at which the d1 and d4 components in (b) are more prominent is close to f g1 , the frequency at which the d4 component amplitude is more prominent is close to f g2 , but it cannot accurately extract the fault characteristic frequency and is easily affected by the surrounding interference information. Fig.15 and Fig.16 They are respectively the components obtained by decomposing the broken tooth fault diagnosis signal using VMD and EEMD and the envelope spectrum of the first four components. Fig.15 The frequency and f of the envelope spectrum with more prominent amplitude corresponding to the IMF2 component in (b) g1 near, Fig.16 The frequency and f of the envelope spectrum with more prominent amplitude corresponding to the IMF4 component in (b) g1 It is similar to the envelope spectrum analysis result of WT decomposition, but the fault characteristic frequency is not accurately extracted and it is easily affected by the surrounding interference information.

[0266] (2) Gear tooth surface wear failure

[0267] In the test, a tooth surface wear fault was set on the driven wheel of the gear pump. The sampling frequency of the acquisition card was set to 4096Hz, the speed of the gear pump was set to 800rmp, and the fault characteristic frequency of the driven gear tooth surface wear was 13Hz. Fig.17 (a) is the time domain diagram of the vibration signal of tooth surface wear fault. Fig.17 (b) is the envelope spectrum of the fault vibration signal. The red dashed lines in the envelope spectrum are the double frequency f of the fault characteristic frequency. g1 and the double frequency f g2 ,It can be seen from the envelope spectrum that the first and second frequencies of the fault ,characteristic frequencies are masked by noise. Therefore, directly performing envelope spectrum analysis on the ,original vibration signal cannot extract the fault characteristic frequency.

[0268] Figure 18-Figure 21 The CICD method is used to decompose the vibration signal of tooth surface wear fault to obtain the components and the envelope spectrum of the first four components, the WT method is used to decompose the components and the envelope spectrum of the first four components, the VMD method is used to decompose the components and the envelope spectrum of the first four components, and the EEMD method is used to decompose the components and the envelope spectrum of the first four components.

[0269] from Fig.18 As can be seen from (b), in the envelope spectra of the first four CPCC components decomposed by the CICD method, the first harmonic frequency of CPCC1 is 13 Hz, which is consistent with the fault characteristic frequency f g1 The first harmonic frequency of CPCC2 is 12Hz, which is consistent with the fault characteristic frequency f g1 The first harmonic frequency of CPCC3 is 23Hz, which is close to the fault characteristic frequency f g2 The first harmonic frequency of CPCC4 is 26Hz, which is close to the fault characteristic frequency f g2 Therefore, the CICD method can be used to decompose and then perform envelope analysis to accurately extract the fault characteristic frequency of the tooth surface wear fault vibration signal.

[0270] The envelope spectra of the first four components of the tooth wear fault vibration signal are obtained by WT decomposition. Fig.19 As shown in (b), among the envelope spectra of the first four components, only the envelope spectrum of the d4 component has a prominent amplitude close to f. g1 The fault characteristic frequency of the tooth surface wear fault in the vibration signal is not accurately extracted. The VMD is used to decompose the tooth surface wear fault vibration signal to obtain the envelope spectrum of the first four components as shown in Fig. 20 As shown in (b), the frequencies with more prominent amplitudes in the envelope spectra of IMF1 and IMF3 components in the envelope spectra of the first four components are related to f g1The frequency with the most prominent amplitude in the envelope spectrum of the IMF2 component is close to f g2 However, the fault characteristic frequency f is not accurately extracted. g2 EEMD is used to decompose the vibration signal of tooth surface wear fault to obtain the envelope spectrum of the first four components as follows: Fig.21 As shown in (b), among the envelope spectra of the first four components, only the envelope spectrum corresponding to IMF3 has a prominent amplitude close to f g1 The fault characteristic frequency of the tooth surface wear fault is not accurately extracted from the vibration signal, just like the WT transformation.

[0271] The comparison results of the above two experiments show that the proposed CICD decomposition method can accurately separate the characteristic frequency of gear faults from the interference information, and the fault diagnosis effect is better than the other three methods. The separated component envelope spectrum is very concise and is not affected by other interference information. It is an effective gear fault diagnosis method.

[0272] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0273] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A gearbox fault diagnosis method based on cyclic pulse feature decomposition, characterized in that: The method includes: S1: Construct a new dictionary matrix "cyclic pulse dictionary matrix" to accurately estimate the pulse cycle period contained in the signal composed of multiple periodic pulse signals; S2: According to the energy value of the pulse cycle, the Goertzel algorithm is used to obtain the corresponding frequency amplitude; S3: Separate and reconstruct the CPCC corresponding to the pulse cycle period, and decompose the signal into several CPCCs.

2. The gearbox fault diagnosis method based on cyclic pulse feature decomposition according to claim 1 is characterized in that: The S1 specifically includes: (1) DFT matrix Since the DFT matrix satisfies the Vandermonde matrix condition and has different rows, the DFT matrix is ​​a full-rank matrix; in addition, given a vector Where W P =e -j2π / P , k is an integer and 1≤k≤P, rewrite it as Where di is a divisor of P, denoted by d i |P; as a vector in n, The vector has period d i ; Let φ(n) denote the Euler totient function evaluated at n. Since k i There is φ(d i ) such values, so φ(d i ) in the form of The signal is exactly equal to d i If you consider For all values ​​of k in 1≤k≤P, each divisor d of P i The generated form is For each d i , obviously φ(d i ) such sequences, because k i There is φ(d i ) values; the total number of such sequences is: In summary, the P columns of the DFT matrix with dimension P×P can be divided into K categories, each of which is a factor d i |P; in class i there is φ(d i ) columns, can be aggregated into P×φ(d i )matrix Their form is The period is d i ; No column of this DFT matrix has d i | Cycles other than P; For example, for the 8×8 DFT matrix in equation (5), the first column is a vector with a period of 1, the second column is a vector with a period of 2, the third and fourth columns are vectors with a period of 4, and the rest are vectors with a period of 8; (2) Construction of Circular Pulse Dictionary Matrix According to the periodic characteristics of the above DFT matrix, the P×P DFT matrix D is expressed as: D=(c1,c2,…,c P ) (6) Among them, c P is the vector with period value P in the Pth column of matrix D, c P The dimension is P×1; let m=1,2,…,P-1, and the number of values ​​in m that are prime to the period P is count(■) and is denoted by L P : L P =count(gcd(m,P)=1)(7) Where gcd(m,P) means obtaining the greatest common factor of m and P, and reconstructing the DFT matrix into C P : in It is c P The cyclic shift form, for example, C8 in equation (9) is: Assume that the input data is x(n), the data length is N, and the matrix C is periodically P Copy and expand to form N×L P The matrix B P , truncate the last C if necessary Pq Matrix C P The first h rows, where q = ceil(N / P), satisfying N = P × (q-1) + h; Building from 1 to P max B P Matrix, forming the circulant impulse dictionary matrix A: Among them, A is an N×L matrix; (3) Cyclic pulse period estimation If the given signal has a period less than P max If it is a periodic signal, then it must be a linear combination of dictionary columns; this is because, if it is a period with period P, then a column with period P as a divisor must be able to span it, so the system of equations in equation (12) must have a solution y, where x = (x(0), x(1), …, x(N-1)) T , the equations are: x=Ay (12) To solve the equation system (12), the orthogonal matching pursuit algorithm (OMP) is used, and the residual γ0 = x and the atomic support set is Support matrix A0 = 0, sparsity I = round (P max ×4), the number of iterations t = 1; first find the maximum column inner product index λt: In the above formula, a j is the jth column of A, and then updates the atomic support set Λ t =Λ t-1 ∪λ t , update the support matrix A t =A t-1 ∪a λ , then find x=A t y t The least squares solution of : Finally, update the residual When t>I, the iteration ends, otherwise t=t+1, and the search for the maximum column inner product index λ continues t ; Let G(P) be the periodic energy value of x(n) in the current period P, and G(P) is defined as follows: The periodic energy value of the signal x(n) ranges from 1 to P max The change is recorded as S(U), where U = 1, 2, ..., P max , S(U) is defined as follows: S(U)=(G(1),G(2),…,G(P max )) (16)。 3. The gearbox fault diagnosis method based on cyclic pulse feature decomposition as claimed in claim 1, characterized in that: The S2 specifically includes: The Goertzel filtering algorithm is used to calculate the kth DFT component of a signal X(n) of length N. The specific expression is shown in formula (17): By multiplying both sides of the equation After sorting, we get: The right side of the above equation can be understood as X(n) and h k (n) discrete linear convolution, where u(l) is a unit step function, and l is an integer. The result of the convolution is expressed in the form of convolution as Y(n), where m is an integer, and we get: Comparing X(n) with Y(n), we can see that X(k) is the Nth sampling of the convolution result, as shown in formula (20): X(k)=Y k (N) (20) Where k is any non-negative integer, and the transformation equation is obtained by performing a Z transform on the output response: The above formula can be regarded as First, is a geometric series of common ratios. For |q|<1, that is, |z|>1, this sequence tends to converge. Integrating the summation results gives: The corresponding difference equation is: Where yk(-1) = 0; introduce conjugate factors into both the numerator and denominator of the fraction (22): Construct a filter with second-order conjugate poles and obtain the transfer function of the second-order Goertzel algorithm: Goertzel filtering can obtain the amplitude information corresponding to a specific frequency. Therefore, the signal pulse cycle period can be estimated by combining the cyclic pulse dictionary matrix, and the frequency amplitude of the corresponding period can be extracted by Goertzel filtering.

4. The gearbox fault diagnosis method based on cyclic pulse feature decomposition as claimed in claim 1, characterized in that: The S3 specifically includes: According to formula (1), it can be deduced that if x(n) is a mixture of three non-sinusoidal periodic signals, n = 1, 2, ..., N, n∈Z, and their periods are P = {P1, P2, P3}, their exponential sum model is: The specific decomposition steps are as follows: (1) Let r i =x(n), i=1, according to the length of signal ri and the maximum estimated period P max (P max >P), construct from 1 to P max The cyclic pulse dictionary matrix A; (2) Using the OMP algorithm to solve the signal r i The solution y of the equation formed by the cyclic pulse dictionary matrix A is: r i =Yes (27) (3) Calculate the number of y from 1 to P max The periodic energy value sequence S(U) is obtained, and the period PM where the maximum energy value of S(U) is located is obtained; S(U)=(G(1),G(2),…,G(P max )) (29) PM=max(S(U)) (30) (4) Let S(PM) = 0. According to the period PM, the signal r is extracted by the Goertzel filtering algorithm shown in equation (20). i The frequency amplitude sequence X corresponding to the PM in the middle period PM : X PM =(Y1(PM),Y2(PM),…,Y PM-1 (PM)) (31) Among them, X PM The corresponding frequency is (5) For non-frequency F PM Amplitude within index X PM Perform zero-filling interpolation. After zero-filling interpolation, X PM1 The data length is And X PM1 Mirror flip constitutes bilateral spectrum X PM2 , where X PM2 The length is N, and for X PM2 Perform IFFT transformation to restore the cyclic pulse characteristic component CPCC corresponding to the periodic PM i , to achieve the reconstruction of the characteristic components of the cyclic pulse; CPCC i =IFFT(X PM2 ,N) (32) r i+1 =r i -CPCC i (33) (6) If i>count(S(U)>0) or i>P max Then the iteration is terminated, otherwise i=i+1, and the process returns to step (3) and repeats the iteration process.

5. A gearbox fault diagnosis based on cyclic pulse feature decomposition as claimed in claims 1-4 The gearbox fault diagnosis system based on cyclic pulse feature decomposition is characterized by: The system specifically includes: The matrix construction module is used to construct a new dictionary matrix "cyclic pulse dictionary matrix" to accurately estimate the pulse cycle period contained in the signal formed by mixing multiple periodic pulse signals; The frequency amplitude acquisition module is connected to the matrix construction module, and uses the Goertzel algorithm to obtain the corresponding frequency amplitude according to the energy value order of the pulse cycle period; The separation and reconstruction module is connected to the frequency amplitude acquisition module, separates and reconstructs the CPCC corresponding to the pulse cycle period, and decomposes the signal into several CPCCs.

6. A computer device, characterized in that: The computer device comprises a memory and a processor, The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the gearbox fault diagnosis method based on cyclic pulse feature decomposition as claimed in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, wherein the computer program is processed When executed by the processor, the processor executes the steps of the gearbox fault diagnosis method based on cyclic pulse feature decomposition as described in any one of claims 1 to 4.

8. An information data processing terminal, characterized in that: The information data processing terminal is used to implement A gearbox fault diagnosis method system based on cyclic pulse feature decomposition as described in claim 5.

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