Method and system for monitoring mechanical state of GIS circuit breaker spring operating mechanism

Abstract

The application discloses a GIS circuit breaker spring mechanism mechanical state monitoring method and system. The method comprises the following steps: collecting vibration signals in the process of opening and closing of the GIS circuit breaker; calculating the frequency spectrum of the vibration signals; obtaining a plurality of modal components of the vibration signals based on variational mode decomposition; identifying a multi-peak modal component in the plurality of modal components of the vibration signals and determining the frequency band segmentation boundary based on the local extremum method; designing a filter according to the frequency band segmentation result to obtain a corresponding single-peak modal component; calculating the center frequency of all single-peak modal components; calculating the correlation coefficient of the center frequency sequence and the center frequency sequence of the historical vibration signals of the GIS circuit breaker spring operating mechanism; and discriminating the mechanical state of the GIS circuit breaker spring operating mechanism according to the correlation coefficient. The application can efficiently and accurately discriminate the mechanical state of the GIS circuit breaker spring operating mechanism, so that effective operation and maintenance measures of the GIS circuit breaker can be taken in time, and the occurrence of a large fault can be avoided.

Description

Technical Field

[0001] This invention relates to the field of mechanical condition monitoring technology for GIS circuit breaker operating mechanisms, and in particular to a method, system, equipment, and medium for monitoring the mechanical condition of spring operating mechanisms during the opening and closing process of GIS circuit breakers. Background Technology

[0002] Gas-insulated switchgear (GIS) is an enclosed combined electrical appliance consisting of circuit breakers, disconnectors, grounding switches, instrument transformers, surge arresters, busbars, and other electrical equipment. It boasts advantages such as small footprint, high insulation level, low maintenance workload, and low failure rate, and is widely used in substations of various voltage levels. As the arc-extinguishing breaking element in GIS, the circuit breaker carries normal operating current and interrupts fault current, playing a dual role of protection and control, and is one of the core components ensuring the safe and stable operation of GIS.

[0003] The operating mechanism provides energy for circuit breaker operation, making its proper functioning a crucial prerequisite for successful circuit breaker opening and closing. Among these, the spring operating mechanism is one of the most widely used types of operating mechanisms in high-voltage circuit breakers due to its advantages such as simple structure, small size, low operating noise, no environmental pollution, maintenance-free operation, and high reliability. However, the complex mechanical structure of the spring operating mechanism leads to a variety of fault types, primarily manifesting as failure to operate, incomplete opening or closing, and incorrect opening and closing. Statistics show that mechanical failures are the main type of circuit breaker failure, with the operating mechanism accounting for the highest proportion of these failures, and this proportion is increasing year by year. Therefore, accurately identifying mechanical defects in the spring operating mechanism of GIS circuit breakers has always been a focus of attention.

[0004] Vibration signals, as an effective carrier of equipment mechanical condition information, are closely related to changes in the equipment's operating state. As a type of instantaneous switching equipment, GIS circuit breakers generate vibration signals during the opening and closing process due to the movement of mechanical components in the operating mechanism and the impact of contacts. In other words, the internal events during the opening and closing process are reflected in each transient waveform. Therefore, the multi-peak vibration signals during the opening and closing process of GIS circuit breakers carry the action information of various internal mechanical components and exhibit strong similarity. Thus, vibration analysis has become an important means of monitoring the mechanical condition of circuit breakers. However, corresponding to the movement process of mechanical components during the opening and closing process of GIS circuit breakers, the accompanying vibration signals exhibit characteristics of rapid rise, gradual decay, and multi-peak aliasing. The corresponding spectrum shows a wide-band continuous distribution, making it difficult to accurately obtain the equipment mechanical condition information contained in the vibration signals. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism. This method monitors the vibration signal of the GIS circuit breaker spring operating mechanism and calculates and analyzes the changes in the center frequencies of multiple modal components in the vibration signal, thereby achieving efficient and accurate assessment of the mechanical condition of the GIS circuit breaker spring operating mechanism.

[0006] To achieve the above-mentioned objectives, the present invention provides a technical solution using the following method: a method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism, comprising:

[0007] Step 1: Collect vibration signal s(t) during the opening and closing process of the GIS circuit breaker;

[0008] Step 2: Calculate the spectrum of the vibration signal s(t) based on Fourier transform, whereby the spectrum of the vibration signal is represented as s(ω);

[0009] Step 3: Perform variational mode decomposition based on the spectrum of the vibration signal to obtain multiple modal components of the vibration signal;

[0010] Step 4: Identify multi-peak mode components and determine frequency band segmentation boundaries in multiple modal components of the vibration signal based on the local extremum method;

[0011] Step 5: Design a filter based on the frequency band segmentation results of the multi-peak mode components of the vibration signal, and obtain the corresponding single-peak mode components;

[0012] Step 6: Calculate the center frequencies of all single-peak modal components of the vibration signal of the decomposed GIS circuit breaker spring operating mechanism;

[0013] Step 7: Calculate the correlation coefficient between the current vibration signal center frequency sequence and the historical vibration signal center frequency sequence of the GIS circuit breaker spring operating mechanism. Based on the correlation coefficient, determine the mechanical state of the GIS circuit breaker spring operating mechanism: when the correlation coefficient is lower than 0.85, it is determined that the mechanical state of the GIS circuit breaker spring operating mechanism has changed. At this time, timely maintenance is required to avoid major failures.

[0014] Further, in step 1, the vibration signal is acquired by a vibration acceleration sensor placed on the outer shell of the GIS circuit breaker spring operating mechanism box, with a sampling frequency of f. s The sampling point length is N0.

[0015] Furthermore, the specific process of step 3 is as follows:

[0016] 3a. Construct a constrained variational model for the vibration signal of a GIS circuit breaker. The constrained variational model is expressed as follows:

[0017]

[0018]

[0019] In the formula: v k Let represent the k-th modal component in the vibration signal obtained based on variational mode decomposition; P represents the number of modal components; δ(t) represents the impulse function; ω k Represents the k-th modal component v k The center frequency of , and the two satisfy . ||·||2 represents the 2-norm; * represents convolution; s represents the vibration signal; ω represents the partial derivative of time t; ω represents the frequency.

[0020] 3b. The constrained variational model is transformed into an unconstrained optimization variational model by using an augmented Lagrangian function. The unconstrained optimization variational model is expressed as:

[0021]

[0022] In the formula: α is the quadratic penalty factor; λ is the Lagrange multiplier parameter;<x,y> Represents the inner product operation of x and y;

[0023] 3c. Set the penalty factor α, the number of modal components P, and the convergence tolerance σ, and let the iteration variable... λ 1 =0 and n=0, where n represents the number of iterations;

[0024] 3d. The optimal solution of the unconstrained variational optimization model is calculated using the alternating direction multiplier method.

[0025] Furthermore, the calculation steps for using the alternating direction multiplier method to calculate the optimal solution of the unconstrained optimization variational model are as follows:

[0026] 31) respectively for and λ n Perform a Fourier transform on (t), and denote the corresponding Fourier transform results as follows: and λ n (ω);

[0027] 32) According to Update modal components The modal component calculation formula is as follows:

[0028]

[0029] 33) According to Update center frequency The formula for calculating the center frequency is as follows:

[0030]

[0031] 34) According to Update Lagrange multiplier λ n+1 Here, ρ is the update step size;

[0032] 35) Determine whether the stopping condition is met. If this condition is not met, let n = n + 1, and repeat steps 31 to 35 until the optimal solution of the unconstrained variational model is obtained, which is P modal components IMF1, IMF2, ..., IMF1. P .

[0033] Furthermore, the specific process of step 4 is as follows:

[0034] 4a. Sequentially process each modal component IMF1, IMF2, ..., IMF P Perform a Fourier transform to obtain its spectrum, denoted as u. k k = 1, 2, ..., P;

[0035] 4b. Traverse the entire spectrum sequence, and add the sequence elements that are simultaneously greater than both the preceding and following elements as local maxima of the spectrum curve to the array {u k_localmax}middle;

[0036] 4c. For array {u k_localmax The elements in} are arranged in descending order, and the largest spectral sequence element u is extracted. k_max and the Lth element v k_L ;

[0037] 4d. Based on the Lth element v k_L Before sorting the array {u} in descending order k_localmax Make corrections, retaining values ​​greater than or equal to u. k_L The L local maxima are recorded and denoted sequentially as y. k_1 y k_2 ... y k_l ... y k_L ; Here, y k_l Indicates the modal component IMF k The magnitude of the l-th local maximum, l = 1, 2, ..., L;

[0038] 4e. Set the multi-peak judgment threshold coefficient μ k , will y k_l sequentially with μ k v k_max Compare; if there exists only one local maximum satisfying y k_l ≥μ k v k_max Then determine the modal component IMF. kThe spectrum exhibits a single peak, which is considered to indicate that the decomposition is complete, denoted as IMF. k.0 And the number of this modal component is set to n. k =1; otherwise, the modal component IMF k The spectrum is a multimodal function;

[0039] 4f. Denote the Modal Components (IMFs) k The middle satisfies y k_l ≥μ k u k_max The frequencies of the local maxima are denoted as ω. k_1 ω k_m 、…、ω k_M This serves as the basis for determining the frequency band segmentation boundary.

[0040] Furthermore, in step 5, the calculation process for the kth multi-peak mode component is as follows:

[0041] 5a. Determine the frequency band segmentation boundary, that is, take the midpoint of the frequency corresponding to two consecutive maxima as the boundary of the frequency spectrum segmentation. The corresponding calculation formula is:

[0042]

[0043] Where: Ω k_0 =0 and Ω k_M+1 =π represents its two sides;

[0044] Therefore, the entire frequency band is divided into M+1 sub-bands, and the range of different sub-bands is denoted as:

[0045] Here, and

[0046] 5b. with Define the width as the center. The transition section, here,

[0047] 5c. Introducing a scaling function Build The bandpass filter on the other frequency bands The bandpass filter on the curve is determined by the empirical wavelet function. Determine that, for n = 2, ..., M+1, the scaling function is... and empirical wavelet function The formula is:

[0048]

[0049]

[0050] In the formula: β(x) is any C k The equation [0,1] is taken as:

[0051] β(x)=x 4 (35-84x+70x 2 -20x 3 );

[0052] 5d. Scale function and empirical wavelet function respectively with the spectrum signal u k Bandwidth filtering based on inner product operations yields approximation coefficients. and detail coefficient It is represented as:

[0053]

[0054]

[0055] In the formula: and They are and Fourier transform; F represents the complex conjugate function of (·); -1 [·] denotes the inverse Fourier transform; τ denotes time; express Translation on the time axis; express Translation on the time axis;

[0056] 5e. Calculate and obtain the multimodal modal component (IMF) based on the approximation coefficients and detail coefficients. k single component According to n k The frequencies are arranged from low to high, and are denoted as IMF. k.1 IMF k.2 ..., IMF k.(M+1) The formula for calculating the single-component component is as follows:

[0057]

[0058] Furthermore, in step 6, the center frequencies are calculated using Fourier transform, and the number of them is... n k The number of frequencies is represented by , and P represents the number of modal components.

[0059] The above technical solution calculates the center frequency of each modal component of the vibration signal of the GIS circuit breaker spring operating mechanism, and determines the mechanical state of the GIS circuit breaker spring operating mechanism based on the correlation coefficient between the center frequency sequence and the center frequency sequence of the modal components of the historical vibration signal. This judgment method is efficient, accurate, and easy to implement, making it convenient for operators to detect abnormal mechanical states of the GIS circuit breaker spring operating mechanism in a timely manner.

[0060] The GIS circuit breaker spring operating mechanism mentioned in this invention, by adopting the above-mentioned technical solution, effectively improves the ability to enhance anti-noise interference and suppress mode mixing during the vibration signal decomposition process of the GIS circuit breaker spring operating mechanism. This enables accurate monitoring of the mechanical state of the GIS circuit breaker spring operating mechanism through vibration signals, thereby allowing for effective operation and maintenance measures and significantly reducing the failure rate of the GIS circuit breaker spring operating mechanism.

[0061] The second objective of this invention is to provide a mechanical condition monitoring system for the spring operating mechanism of a GIS circuit breaker, which is used to implement the above-mentioned mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker.

[0062] A third objective of this invention is to provide an electronic device for performing one of the objectives of the invention, comprising a processor, a storage medium, and a computer program, wherein the computer program is stored in the storage medium, and when the computer program is executed by the processor, it implements the above-mentioned method for monitoring the mechanical condition of the spring operating mechanism of a GIS circuit breaker.

[0063] A fourth objective of this invention is to provide a computer-readable storage medium storing one of the objectives of the invention, wherein a computer program is stored thereon, and when the computer program is executed by a processor, it implements the above-described method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism.

[0064] The mechanical condition monitoring system, electronic terminal, and computer-readable storage medium of the present invention for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism are used to execute the mechanical condition monitoring method of the present invention for a GIS circuit breaker spring operating mechanism. Of course, they also have the above-mentioned beneficial effects, which will not be repeated here. Attached Figure Description

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Figure 1 This is a flowchart illustrating the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker according to the present invention.

[0067] Figure 2 This is a time-domain waveform diagram of the vibration signal collected during the operation of the spring operating mechanism of the GIS circuit breaker in an embodiment of the present invention;

[0068] Figure 3 This is a spectrum diagram of the vibration signal collected during the operation of the spring operating mechanism of the GIS circuit breaker in an embodiment of the present invention. Detailed Implementation

[0069] The present invention will now be described in more detail with reference to the accompanying drawings. It should be noted that the following description of the present invention with reference to the accompanying drawings is merely illustrative and not restrictive. Various embodiments can be combined with each other to form other embodiments not shown in the following description.

[0070] Reference Figure 1 Vibration signal tests were conducted on a 252kV GIS circuit breaker during its opening and closing process. The vibration signal waveform of the circuit breaker was decomposed according to the following steps:

[0071] (1) Collect vibration signals s(t) during the opening and closing process of the GIS circuit breaker, such as Figure 2 As shown, it is a nonlinear and strongly time-varying signal. The vibration signal is acquired by a vibration acceleration sensor placed on the outer shell of the GIS circuit breaker spring operating mechanism box, and its sampling frequency is f. s The sampling point length is N0; here, f s =50kHz, N0=25000.

[0072] (2) The spectrum of the vibration signal s(t) is calculated based on the Fourier transform. The spectrum of the vibration signal can be expressed as s(ω), such as... Figure 3 As shown;

[0073] (3) Variational mode decomposition is performed based on the spectrum of the vibration signal to obtain multiple modal components of the vibration signal. The specific calculation process is as follows:

[0074] 3a. Construct a constrained variational model for the vibration signal of a GIS circuit breaker. The constrained variational model is expressed as follows:

[0075]

[0076]

[0077] In the formula: v k Let represent the k-th modal component in the vibration signal obtained based on variational mode decomposition; P represents the number of modal components; δ(t) represents the impulse function; ω k Represents the k-th modal component v k The center frequency of , and the two satisfy . ·||2 represents the 2-norm; * represents convolution; ω represents the partial derivative of time t; ω represents the frequency.

[0078] 3b. The constrained variational model is transformed into an unconstrained optimization variational model by using an augmented Lagrangian function. The unconstrained optimization variational model can be expressed as:

[0079]

[0080] In the formula: α is the quadratic penalty factor; λ is the Lagrange multiplier parameter;<x,y> This represents the inner product operation between x and y.

[0081] 3c. Set the penalty factor α, the number of modal components P, and the convergence tolerance σ, and let the iteration variable... λ 1 =0 and n=0, where n represents the number of iterations;

[0082] 3d. The optimal solution of the unconstrained variational optimization model is calculated using the alternating direction multiplier method. The calculation steps are as follows:

[0083] 1) respectively for and λ n Perform a Fourier transform on (t), and denote the corresponding Fourier transform results as follows: and λ n (ω);

[0084] 2) According to Update modal components The modal component calculation formula is as follows:

[0085]

[0086] 3) According to Update center frequency The formula for calculating the center frequency is as follows:

[0087]

[0088] 4) According to Update Lagrange multiplier λ n+1 Here, ρ is the update step size;

[0089] 5) Determine whether the stopping condition is met. If this condition is not met, let n = n + 1, and repeat steps 1 to 5 until the optimal solution of the unconstrained variational model is obtained, which is P modal components IMF1, IMF2, ..., IMF1. P .

[0090] (4) Based on the local extremum method, the multi-peak mode components and their frequency band segmentation boundaries in multiple modal components of the vibration signal are identified. The specific calculation process is as follows:

[0091] 4a. Sequentially process each modal component IMF1, IMF2, ..., IMF P Perform a Fourier transform to obtain its spectrum, denoted as u. k (k = 1, 2, ..., P);

[0092] 4b. Traverse the entire spectrum sequence, and add the sequence elements that are simultaneously greater than both the preceding and following elements as local maxima of the spectrum curve to the array {u k_localmax}middle;

[0093] 4c. For array {u k_localmax The elements in} are arranged in descending order, and the largest spectral sequence element is extracted and denoted as u. k_max and the Lth element u k_L ;

[0094] 4d. Based on the Lth local maximum u k_L The original array {u} before sorting in descending order k_localmax Make corrections, retaining values ​​greater than or equal to u. k_L The L local maxima are recorded and denoted sequentially as y. k_1 y k_2 ... y k_l ... y k_L ; Here, y k_l (l=1,2,…,L) represents the modal component IMF. k The magnitude of the l-th local maximum;

[0095] 4e. Set the multi-peak judgment threshold coefficient μ k , will y k_l sequentially with μ k u k_max Comparison. If there exists only one local maximum satisfying y k_l ≥μ k u k_max Then determine the modal component IMF. k The spectrum exhibits a single peak, which is considered to indicate that the decomposition is complete, denoted as IMF. k.0 And the number of this modal component is set to n. k =1; otherwise, the modal component IMF k The spectrum is a multimodal function;

[0096] 4f. Modal Components (IMF) k The middle satisfies y k_l ≥μ k u k_max The frequencies of the local maxima are denoted as ω. k_1 ω k_m 、…、ω k_M This serves as the basis for determining the frequency band segmentation boundary;

[0097] (5) Design a filter based on the frequency band segmentation results of the multi-peak mode components of the vibration signal, and obtain the corresponding single-peak mode components. The calculation process of the kth multi-peak mode component is as follows:

[0098] 5a. Determine the frequency band segmentation boundary, that is, take the midpoint of the frequency corresponding to two consecutive maxima as the boundary of the frequency spectrum segmentation. The corresponding calculation formula is:

[0099]

[0100] Where: Ω k_0 =0 and Ω k_M+1 =π represents its two sides;

[0101] Therefore, the entire frequency band is divided into M+1 sub-bands, and the range of different frequency bands is denoted as M+1. Here, and

[0102] 5b. with Define the width as the center. The transition section, here,

[0103] 5c. Introducing a scaling function Build The bandpass filter on the other frequency bands The bandpass filter on the curve is determined by the empirical wavelet function. Determine that, for n = 2, ..., M+1, the scaling function is... and empirical wavelet function The formula is:

[0104]

[0105]

[0106] In the formula: β(x) can be any C k The equation for [0,1] is usually taken as:

[0107] β(x)=x 4 (35-84x+70x 2 -20x 3 );

[0108] 5d. Scale function and empirical wavelet function respectively with the spectrum signal u k Bandwidth filtering based on inner product operations yields approximation coefficients. and detail coefficient It is represented as:

[0109]

[0110]

[0111] In the formula: and They are and Fourier transform; F represents the complex conjugate function of (·); -1 [·] denotes the inverse Fourier transform; τ is time; express Translation on the time axis; express Translation on the time axis;

[0112] 5e. Calculate and obtain the multimodal modal component (IMF) based on the approximation coefficients and detail coefficients. k single component According to n k The frequencies are arranged from low to high, and are denoted as IMF. k.1 IMF k.2 ..., IMF k.(M+1) The formula for calculating the single-component component is as follows:

[0113]

[0114] (6) Calculate the center frequencies of all single-peak modal components of the vibration signal of the GIS circuit breaker spring operating mechanism obtained from the decomposition. The center frequencies are calculated by Fourier transform, and the number of components is...

[0115] (7) Calculate the correlation coefficient between the current vibration signal center frequency sequence and the historical vibration signal center frequency sequence of the GIS circuit breaker spring operating mechanism. Based on the correlation coefficient, determine the mechanical state of the GIS circuit breaker spring operating mechanism: when the correlation coefficient is less than 0.85, it is determined that the mechanical state of the GIS circuit breaker spring operating mechanism has changed. At this time, timely maintenance is required to avoid major failures.

[0116] Here, the correlation coefficient between the current vibration signal center frequency sequence and the historical vibration signal center frequency sequence of the GIS circuit breaker spring operating mechanism is 0.89, indicating that the mechanical state of the GIS circuit breaker spring operating mechanism is normal.

[0117] Corresponding to the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker of the present invention, the present invention also provides a mechanical condition monitoring system for the spring operating mechanism of a GIS circuit breaker, for implementing the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker.

[0118] Corresponding to the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker of the present invention, the present invention also provides an electronic terminal, which includes a processor, a storage medium and a computer program, wherein the computer program is stored in the storage medium, and when the computer program is executed by the processor, it implements the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker.

[0119] Corresponding to the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker of the present invention, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker as described above.

[0120] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

Claims

1. A method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism, characterized in that, include: Step 1: Collect vibration signals during the opening and closing process of the GIS circuit breaker. ; Step 2: Calculate the vibration signal based on Fourier transform The spectrum; Step 3: Perform variational mode decomposition based on the spectrum of the vibration signal to obtain multiple modal components of the vibration signal; Step 4: Identify multi-peak mode components and determine frequency band segmentation boundaries in multiple modal components of the vibration signal based on the local extremum method; Step 5: Design a filter based on the frequency band segmentation results of the multi-peak mode components of the vibration signal, and obtain the corresponding single-peak mode components; Step 6: Calculate the center frequencies of all single-peak modal components of the vibration signal of the decomposed GIS circuit breaker spring operating mechanism; Step 7: Calculate the correlation coefficient between the current vibration signal center frequency sequence and the historical vibration signal center frequency sequence of the GIS circuit breaker spring operating mechanism, and determine the mechanical state of the GIS circuit breaker spring operating mechanism based on the correlation coefficient.

2. The method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism according to claim 1, characterized in that, In step 1, the vibration signal is acquired by a vibration acceleration sensor placed on the outer shell of the GIS circuit breaker spring operating mechanism box, and its sampling frequency is [missing information]. The sampling point length is .

3. The method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism according to claim 1, characterized in that, The specific process of step 3 is as follows: 3a. Construct a constrained variational model for the vibration signal of a GIS circuit breaker. The constrained variational model is expressed as follows: In the formula: The first element in the vibration signal obtained based on variational mode decomposition represents the... k One modal component; Indicates the number of modal components; Represents the impulse function; Indicates the first k Modal components The center frequency of , and the two satisfy . ; Represents the 2-norm; express t The partial derivative, Indicates frequency; 3b. The constrained variational model is transformed into an unconstrained optimization variational model by using an augmented Lagrangian function. The unconstrained optimization variational model is expressed as: In the formula: It is a secondary penalty factor; These are the Lagrange multiplier parameters; represent x and y dot product operation; 3c. Set a secondary penalty factor Number of modal components and convergence tolerance Let the iteration variable , , and Here, n Indicates the number of iterations; 3d. The optimal solution of the unconstrained variational optimization model is calculated using the alternating direction multiplier method.

4. The method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism according to claim 3, characterized in that, The method of alternating direction multipliers is used to calculate the optimal solution of the unconstrained variational optimization model. The calculation steps are as follows: 31) respectively for and Perform a Fourier transform, and the corresponding Fourier transform results are denoted as follows: and ; 32) According to Update modal components The modal component calculation formula is as follows: 33) According to Update center frequency The formula for calculating the center frequency is as follows: 34) According to Update Lagrange multipliers Here, To update the step size; 35) Determine whether the stopping condition is met. If not satisfied, then let Repeat steps 31 to 35 until the optimal solution of the unconstrained variational optimization model is obtained, which is... Modal components IMF1, IMF2 IMF P .

5. The method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism according to claim 1, characterized in that, The specific process of step 4 is as follows: 4a. Sequentially process each modal component IMF1, IMF2, IMF P Perform a Fourier transform to obtain its spectrum, denoted as . ; 4b. Traverse the entire spectrum sequence, adding the sequence elements that are simultaneously greater than both the preceding and following elements as local maxima of the spectrum curve to the array { }middle; 4c. For array { The elements in the array are arranged in descending order, and the largest spectral sequence element is extracted. and sorted as number L one element ; 4d. According to the first L element Before sorting the array in descending order { Make corrections, retaining values ​​greater than or equal to of L Each local maxima is recorded and denoted sequentially as follows: , , , , , ; Here, Indicates the modal component IMF k No. l The magnitude of each local maximum. ; 4e. Set the threshold coefficient for multi-peak judgment ,Will sequentially with Compare; If there exists only one local maximum satisfying Then determine the modal component IMF. k The spectrum exhibits a single peak, which is considered to indicate that the decomposition is complete, denoted as IMF. k.0 And the number of this modal component is set to ; Otherwise, the modal component IMF k The spectrum is a multimodal function; 4f. Denote the Modal Components (IMFs) k China satisfies The frequencies of the local maxima are denoted as follows: , , , This is the frequency band segmentation boundary.

6. The method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism according to claim 4, characterized in that, In step 5, the first The calculation process for the multi-peak mode components is as follows: 5a. Determine the frequency band segmentation boundary, that is, take the midpoint of the frequency corresponding to two consecutive maxima as the boundary of the frequency spectrum segmentation. The corresponding calculation formula is: In the formula: and These are its two sides; Therefore, the entire frequency band is divided into M +1 sub-band, the range of different sub-bands is denoted as: Here, and ; 5b. with Define the width as the center. The transition section, here, ; 5c. Introducing a scaling function Build The bandpass filter on the other frequency bands The bandpass filter on the curve is determined by the empirical wavelet function. Sure, The scaling function and empirical wavelet function The formula is: In the formula: For any The equation is taken as: ； 5d. Scale function and empirical wavelet function respectively with the spectrum signal Bandwidth filtering based on inner product operations yields approximation coefficients. and detail coefficient It is represented as: In the formula: and They are and Fourier transform; Indicates taking The complex conjugate function; Indicates the inverse Fourier transform; For time; express Translation on the time axis; express Translation on the time axis; 5e. Calculate and obtain the multimodal modal component (IMF) based on the approximation coefficients and detail coefficients. k single component According to The frequencies are arranged from low to high, and are denoted as IMF. k.1 IMF k.2 , IMF k.(M+1) The formula for calculating the single-component component is as follows: 。 7. The method for monitoring the mechanical condition of a GIS circuit breaker spring operating mechanism according to claim 1, characterized in that, In step 6, the center frequency is calculated by Fourier transform, and the number of frequencies is... , Indicates the number of frequencies. P This indicates the number of modal components.

8. A mechanical condition monitoring system for the spring operating mechanism of a GIS circuit breaker, characterized in that, It is used to implement the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker as described in any one of claims 1-7.

9. An electronic device comprising a processor, a storage medium, and a computer program, wherein the computer program is stored in the storage medium, characterized in that, When the computer program is executed by the processor, it implements the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the mechanical condition monitoring method for the spring operating mechanism of a GIS circuit breaker as described in any one of claims 1 to 7.

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