GIS basin-type insulator flange bolt pre-tightening force detection method and system

Abstract

The invention discloses a bolt pre-tightening force monitoring method and system based on vibration signals, and the method comprises the steps: installing a vibration sensor at the center of the surface of a flange bolt, and collecting the vibration signals of the bolt under different pre-tightening forces through the vibration sensor; according to the collected vibration signal of the flange bolt, carrying out modal decomposition on the vibration signal through a complete empirical mode decomposition method of adaptive noise; the correlation coefficients of all the obtained modal components are solved, high-frequency and low-frequency indexing is conducted on all the modal components, the correlation coefficients of the high-frequency components are taken for fusion processing, and characteristic indexes under the single bolt pre-tightening force are represented; according to the characteristic indexes under different pre-tightening forces, the characteristic indexes of the pre-tightening force change condition of the flange bolt are constructed, and the current bolt pre-tightening force state is detected. The method is based on vibration acoustics, manual operation is reduced, and self-adaptive bolt state detection of the flange bolt in the using process of the GIS basin-type insulator is achieved.

Description

Technical Field

[0001] The invention relates to the field of bolt fasteners, and in particular to a method and system for detecting the pre-tightening force of flange bolts of a GIS pot-type insulator. Background Art

[0002] Flange bolts are important metal accessories of pot-type insulators, which play the role of connecting and fixing the barrel and also bear great mechanical stress during vibration. Due to installation reasons, long-term operation and natural environmental factors, the flange bolts of GIS equipment may become loose or corrode and fail, which may cause uneven force connection between the pot-type insulator and the barrel, leading to gas leakage and cracking accidents, which seriously threaten the safe and reliable operation of GIS equipment. Although measures such as regular inspection, tightening, and anti-loosening glue have been adopted, the hidden dangers of bolt loosening have not been fundamentally eliminated. Therefore, in order to ensure the safe operation of pot-type insulators of GIS equipment, stress detection of flange bolts of GIS equipment should be carried out in time.

[0003] At present, the commonly used detection methods for bolt preload are simple torque wrench method, strain test method, ultrasonic detection method, and vibration acoustic method. Among them, the torque wrench method has low accuracy, and the signal acquisition equipment of the second and third methods is expensive and easily interfered by noise or high-frequency signals. The vibration acoustic method has a simple structure, low cost, and high accuracy, and has application prospects in bolt fault diagnosis.

[0004] The current research status shows that there are few reports on the research on the condition monitoring technology of GIS pot insulator bolts. Most of the existing detection methods are based on manual data reading and reanalysis, which cannot achieve adaptive signal analysis and processing and have low efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the defects of the prior art and provide a GIS pot insulator flange bolt preload detection method and system based on vibroacoustics.

[0006] In a first aspect, the present invention provides a method for detecting the preload force of a flange bolt of a GIS pot-type insulator, comprising the following process:

[0007] Step 1: Install the vibration sensor at the center of the flange bolt head surface, use a torque wrench to apply different degrees of pre-tightening force to the bolt, use a vibration hammer to hit the head surface, and use the vibration sensor to collect the vibration signal of the bolt under different pre-tightening forces;

[0008] Step 2: According to the vibration signal of the flange bolt collected, the vibration signal is modally decomposed by the complete empirical mode decomposition of adaptive noise (CEEMDAN) method. The algorithm flow is as follows:

[0009] Constructing multiple groups of random test signals, which are obtained by adding random noise of adaptive control strategy to the original vibration signal;

[0010] Perform EMD decomposition on each set of test signals to obtain a set of modal component (IMF) functions. Perform combined weighted processing on each set of IMF functions to generate a set of total IMF functions.

[0011] Perform EMD decomposition on the total IMF function again to obtain a new set of IMF functions. Check whether the newly generated IMF functions meet the convergence conditions. If not, adjust the adaptive noise and repeat the above steps.

[0012] After the convergence condition is met, the iteration continues until the number of IMF functions no longer increases, and the CEEMDAN algorithm decomposition ends;

[0013] The convergence conditions are: the residual signal is a monotonic signal;

[0014] Step 3: After the modal decomposition is completed, the time and frequency characteristics of different modal components of the bolt are obtained, the correlation coefficients of all the modal components are solved, and the high and low frequency indexes of each modal component are performed. The high frequency vibration correlation coefficient is taken for fusion processing to characterize the characteristic index under a single bolt preload;

[0015] Step 4: According to the characteristic indicators under different preload forces, characteristic indicators of the change of flange bolt preload force are constructed to detect the current bolt preload force state;

[0016] Furthermore, according to the vibration signal of the flange bolt collected, the vibration response signal of the sensor is modally decomposed by the CEEMDAN method, and the correlation coefficient of the flange bolt in different modes is obtained after decomposition, which adopts the following formula:

[0017]

[0018] Where: IMF_k i (t), i = 1, ..., n is the i modal components obtained by decomposition; x(t) is the original vibration response signal; Cov(IMF_k i (t), x(t)) is the covariance of each modal component and the original vibration signal.

[0019] Furthermore, each modal component is indexed for high and low frequencies, and the high-frequency vibration correlation coefficient is taken for fusion processing to construct characteristic indicators that characterize the change of bolt preload, including:

[0020] The decomposed IMF components are distinguished into high and low frequencies. The specific method is: IMF1 is used as index 1, IMF1+IMF2 is index 2, and so on. The sum of the first i IMFs is added as index i, and a t-test is performed to determine whether the mean is significantly different from the original vibration signal. According to the obtained p-value, the first i IMF components are determined to be high-frequency components;

[0021] The correlation coefficients of all high-frequency components are counted, the negative correlation coefficients are eliminated, and the positive correlation coefficients are superimposed. Their values ​​are used as the characteristic indicators of the single preload force of the flange bolts. The characteristic indicators of different preload forces are statistically analyzed to obtain the characteristic indicators that change with the preload force.

[0022] Furthermore, by constructing a characteristic indicator that characterizes the change in the preload force of the GIS pot-type insulator flange bolts, the current state of the flange bolt preload force is detected, including: judging the change in the preload force of the collected flange bolts by using the constructed characteristic indicator of the change in the flange bolt preload force, thereby realizing the state detection of the bolt preload force.

[0023] In a second aspect, the present invention provides a GIS pot-type insulator flange bolt preload detection system, comprising:

[0024] Module 1 is used to: install the vibration sensor at the center of the flange bolt head surface, use a torque wrench to apply different degrees of pre-tightening force to the bolt, use a vibration hammer to hit the head surface, and use the vibration sensor to collect the vibration signal of the bolt under different pre-tightening forces;

[0025] Module 2 is used to: perform modal decomposition of the vibration signal of the flange bolts collected by using the complete empirical mode decomposition of adaptive noise (CEEMDAN) method;

[0026] Module three is used to: after the modal decomposition is completed, the time and frequency characteristics of different modal components of the bolt are obtained, the correlation coefficients of all the modal components are solved, and the high and low frequency indexes of each modal component are performed, and the high-frequency vibration correlation coefficients are taken for fusion processing to characterize the characteristic indicators under a single bolt preload;

[0027] Module 4 is used to: construct characteristic indicators of flange bolt preload changes based on characteristic indicators under different preload forces, and detect the current bolt preload state;

[0028] Further, the module 2 is used to obtain the correlation coefficient of the flange bolt in different modes after decomposition, using the following formula:

[0029]

[0030] Where: IMF_k i(t), i = 1, ..., n is the i modal components obtained by decomposition; x(t) is the original vibration response signal; Cov(IMF_k i (t), x(t)) is the covariance of each modal component and the original vibration signal.

[0031] Further, the module three is used for:

[0032] The decomposed IMF components are distinguished into high and low frequencies. The specific method is: IMF1 is used as index 1, IMF1+IMF2 is index 2, and so on. The sum of the first i IMFs is added as index i, and a t-test is performed to determine whether the mean is significantly different from the original vibration signal. According to the obtained p-value, the first i IMF components are determined to be high-frequency components;

[0033] The correlation coefficients of all high-frequency components are counted, the negative correlation coefficients are eliminated, and the positive correlation coefficients are superimposed. Their values ​​are used as the characteristic indicators of the single preload force of the flange bolts. The characteristic indicators of different preload forces are statistically analyzed to obtain the characteristic indicators that change with the preload force.

[0034] Furthermore, the module four is used to: determine the change of the collected flange bolt preload force through the constructed characteristic index of the flange bolt preload force change, and realize the state detection of the bolt preload force.

[0035] In a third aspect, the present invention provides a product, comprising:

[0036] Memory, used to store programs;

[0037] A processor is used to implement the method as described in the first aspect by executing the program stored in the memory.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising a program, wherein the program can be executed by a processor to implement the method as described in the first aspect.

[0039] In the present invention, the current state of the flange bolt preload is detected by constructing a characteristic index that characterizes the change in the preload of the GIS pot-type insulator flange bolt. The flange bolt preload detection method based on vibration acoustics includes: collecting the vibration response signal of the flange bolt, and judging the change in the preload of the collected flange bolt through modal analysis and processing of the vibration response signal by using the constructed characteristic index of the change in the preload of the flange bolt, thereby realizing the state detection of the bolt preload.

[0040] The flange bolt preload detection method based on vibration acoustics proposed in the present invention does not require any manual data processing process, and the proposed data processing method can directly characterize the bolt preload state index. Compared with the traditional preload detection method, it minimizes human operation and realizes the adaptive bolt state detection of the flange bolts of the GIS pot insulator during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a method for monitoring the preload force of flange bolts of GIS pot-type insulators;

[0042] Figure 2 It is the time domain diagram of the original vibration signal of the flange bolt;

[0043] Figure 3 It is the spectrum diagram of the original vibration signal of the flange bolt;

[0044] Figure 4 This is a schematic diagram of CEEMDAN modal decomposition results;

[0045] Figure 5 Schematic diagram of the change of high-frequency modes under different preloads;

[0046] Figure 6 It is the change of characteristic index with preload. DETAILED DESCRIPTION

[0047] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the accompanying drawings and 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, that is, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0050] Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of more restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0051] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0052] Step 1: Install the vibration sensor at the center of the flange bolt head surface, use a torque wrench to apply different degrees of pre-tightening force to the bolt, use a vibration hammer to hit the head surface, and use the vibration sensor to collect the vibration signal of the bolt under different pre-tightening forces;

[0053] Step 2: According to the vibration signal of the flange bolt collected, the vibration signal is modally decomposed by the complete empirical mode decomposition of adaptive noise (CEEMDAN) method. The algorithm flow is as follows:

[0054] Constructing multiple groups of random test signals, which are obtained by adding random noise of adaptive control strategy to the original vibration signal;

[0055] Perform EMD decomposition on each set of test signals to obtain a set of modal component (IMF) functions. Perform combined weighted processing on each set of IMF functions to generate a set of total IMF functions.

[0056] Perform EMD decomposition on the total IMF function again to obtain a new set of IMF functions. Check whether the newly generated IMF functions meet the convergence conditions. If not, adjust the adaptive noise and repeat the above steps.

[0057] After the convergence condition is met, the iteration continues until the number of IMF functions no longer increases, and the CEEMDAN algorithm decomposition ends;

[0058] The convergence condition is that the residual signal is a monotonic signal.

[0059] Step 3: After the modal decomposition is completed, the time and frequency characteristics of different modal components of the bolt are obtained, the correlation coefficients of all the modal components are solved, and the high and low frequency indexes of each modal component are performed. The high frequency vibration correlation coefficient is taken for fusion processing to characterize the characteristic index under a single bolt preload;

[0060] Step 4: According to the characteristic indicators under different preload forces, characteristic indicators of the change of flange bolt preload force are constructed to detect the current bolt preload force state.

[0061] Furthermore, according to the vibration signal of the flange bolt collected, the vibration response signal of the sensor is modally decomposed by the CEEMDAN method, and the correlation coefficient of the flange bolt in different modes is obtained after decomposition, using the following formula:

[0062]

[0063] Where: IMF_k i (t), i = 1, ..., n is the i modal components obtained by decomposition; x(t) is the original vibration response signal; Cov(IMF_k i (t), x(t)) is the covariance of each modal component and the original vibration signal.

[0064] Furthermore, each modal component is indexed for high and low frequencies, and the high-frequency vibration correlation coefficient is taken for fusion processing to construct characteristic indicators that characterize the change of bolt preload, including:

[0065] The decomposed IMF components are distinguished into high and low frequencies. The specific method is: IMF1 is used as index 1, IMF1+IMF2 is index 2, and so on. The sum of the first i IMFs is added as index i, and a t-test is performed to determine whether the mean is significantly different from the original vibration signal. According to the obtained p-value, the first i IMF components are determined to be high-frequency components;

[0066] The correlation coefficients of all high-frequency components are counted, the negative correlation coefficients are eliminated, and the positive correlation coefficients are superimposed, and their values ​​are used as characteristic indicators of flange bolt preload.

[0067] The GIS pot insulator flange bolt preload detection method proposed by the present invention is as follows: Figure 1 shown.

[0068] Acquisition of vibration response signals

[0069] The vibration response signal is mainly collected by the vibration sensor fixed on the surface of the flange bolt to collect the original vibration signal of the bolt. The collected vibration signal is used to extract the modal characteristics and detect the bolt preload state. The time domain diagram of the original vibration response signal of the flange bolt is shown in the figure below. Figure 2 shown.

[0070] Modal Analysis and Processing of Vibration Response Signals

[0071] The method adopted by the present invention is to obtain the modal component of the vibration response signal by superimposing the random noise of the adaptive control strategy on the original vibration signal on the basis of empirical mode decomposition (EMD), and specifically comprises the following steps:

[0072] Step 201. Given an original signal x(t) and a predefined number of iterations N;

[0073] Initialize the first IMF to IMF1=x(t), and initialize the residual signal r(t) to r(t)=x(t).

[0074] Step 202. For each i, generate M Gaussian white noise signals n with standard normal distribution i (t), where M is the preset number of noise realizations;

[0075] For each noise realization j (from 1 to M), add the noise to the residual signal: ij (t) = r(t) + β i *n ij (t), where β i is the noise coefficient that increases with the number of iterations i.

[0076] Step 203. For each noisy signal r ij (t) Perform EMD decomposition to obtain a series of IMF components;

[0077] Calculate the mean of the first IMF component in all noise realizations: IMF_mean i (t) = mean[IMF1 ij (t)].

[0078] Step 204. Set the first IMF component to IMF1 = IMF_mean1(t).

[0079] Update the residual signal: r(t)=r(t)-IMF1.

[0080] For k starting from 2 (corresponding to the second and subsequent IMF components): initialize IMF_k(t)=0.

[0081] For each i from 1 to N: Generate M Gaussian white noise signals n with standard normal distribution i (t).

[0082] Calculate the residual signal with specific noise: r i (t) = r(t) + β i *EMD[r(t)](k-1), where EMD[r(t)](k-1) is the (k-1)th IMF component obtained after EMD decomposition of the residual signal r(t).

[0083] Step 205. For each noisy signal r i (t) Perform EMD decomposition to obtain a series of IMF components.

[0084] Calculate the average value of the kth IMF component in all noise realizations and add it to IMF_k(t): IMF_k(t) = IMF_k(t) + mean[IMFk i (t)].

[0085] The obtained IMF_k(t) is taken as the kth IMF component.

[0086] Update the residual signal: r(t)=r(t)-IMF_k(t).

[0087] The above steps are repeated until the stopping criterion is met (the residual signal becomes monotonic or the number of IMF components reaches a preset value).

[0088] Output all calculated IMF components and the final residual signal.

[0089] Figure 3 The spectrum of the original vibration response signal of the flange bolt is shown in Figure 2. The IMF and spectrum of the original vibration signal after CEEMDAN decomposition are shown in Figure 2. Figure 4 By performing high and low frequency indexing on each IMF after decomposition, the correlation coefficient of high frequency mode under single preload is obtained, as shown in Figure 5 As shown. Figure 5 It can be seen that there are negatively correlated modal components in the high-frequency mode, indicating that this component has no correlation with the original vibration signal and can be identified as a noise mode that needs to be eliminated.

[0090] Feature index construction

[0091] In this embodiment, according to the above process, the vibration response signals under different preload forces are decomposed by CEEMDAN one by one, and the positive correlation coefficient after superposition is obtained as the characteristic index, and the change of the characteristic index with the preload force is constructed, such as Figure 6 As shown, from Figure 6 It can be seen that the characteristic index is positively correlated with the preload force.

[0092] Loose bolt identification

[0093] In this embodiment, through the processing of the above process, an indicator characterizing the change trend of the bolt preload can be constructed. As the preload increases, the characteristic indicator shows an obvious monotonic change trend. Therefore, the characteristic indicator constructed by this method can be used to identify bolt loosening. In actual application, by monitoring the vibration signal of the bolt connector, the change of the preload of the GIS pot insulator flange bolt can be determined after the vibration signal is processed by the method proposed by the present invention.

[0094] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0095] The above description is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed in the present invention, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described in the present invention through the above teachings or the technology or knowledge in the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for detecting the preload force of flange bolts of GIS pot-type insulators, characterized in that: include: Step 1: Install the vibration sensor at the center of the flange bolt head surface, use a torque wrench to apply different degrees of pre-tightening force to the bolt, use a vibration hammer to hit the head surface, and use the vibration sensor to collect the vibration signal of the bolt under different pre-tightening forces; Step 2: According to the vibration signal of the flange bolt collected, the vibration signal is modally decomposed by using the complete empirical mode decomposition of adaptive noise (CEEMDAN) method; Step 3: After the modal decomposition is completed, the time and frequency characteristics of different modal components of the bolt are obtained, the correlation coefficients of all the modal components are solved, and the high and low frequency indexes of each modal component are performed. The high frequency vibration correlation coefficient is taken for fusion processing to characterize the characteristic index under a single bolt preload; Step 4: According to the characteristic indicators under different preload forces, characteristic indicators of the change of flange bolt preload force are constructed to detect the current bolt preload force state.

2. A method for detecting the preload force of flange bolts of GIS pot-type insulators according to claim 1, characterized in that: In the step 2, based on the vibration signal of the flange bolt collected, the vibration response signal of the sensor is modally decomposed by the CEEMDAN method, and the correlation coefficient of the flange bolt in different modes is obtained after decomposition, using the following formula: Where: IMF_k i (t), i = 1, ..., n is the i modal components obtained by decomposition; x(t) is the original vibration response signal; Cov(IMF_k i (t), x(t)) is the covariance of each modal component and the original vibration signal.

3. A method for detecting the preload force of flange bolts of GIS pot-type insulators according to claim 2, characterized in that: In step 3, each modal component is indexed for high and low frequencies, and the high frequency vibration correlation coefficient is taken for fusion processing to construct characteristic indicators that characterize the change of bolt preload, including: The decomposed IMF components are distinguished into high and low frequencies. The specific method is: IMF1 is used as index 1, IMF1+IMF2 is index 2, and so on. The sum of the first i IMFs is added as index i, and a t-test is performed to determine whether the mean is significantly different from the original vibration signal. According to the obtained p-value, the first i IMF components are determined to be high-frequency components; The correlation coefficients of all high-frequency components are counted, the negative correlation coefficients are eliminated, and the positive correlation coefficients are superimposed. Their values ​​are used as the characteristic indicators of the single preload force of the flange bolts. The characteristic indicators of different preload forces are statistically analyzed to obtain the characteristic indicators that change with the preload force.

4. A method for detecting the preload force of flange bolts of GIS pot-type insulators according to claim 3, characterized in that: In the step 4, the current state of the flange bolt preload is detected by constructing a characteristic indicator that characterizes the change in the preload of the GIS pot-type insulator flange bolt, including: judging the change in the preload of the collected flange bolts by using the constructed characteristic indicator of the change in the preload of the flange bolt, thereby realizing the state detection of the bolt preload.

5. A GIS pot-type insulator flange bolt preload detection system, characterized in that: include: Module 1 is used to: install the vibration sensor at the center of the flange bolt head surface, use a torque wrench to apply different degrees of pre-tightening force to the bolt, use a vibration hammer to hit the head surface, and use the vibration sensor to collect the vibration signal of the bolt under different pre-tightening forces; Module 2 is used to: perform modal decomposition of the vibration signal of the flange bolts collected by using the complete empirical mode decomposition of adaptive noise (CEEMDAN) method; Module three is used to: after the modal decomposition is completed, the time and frequency characteristics of different modal components of the bolt are obtained, the correlation coefficients of all the modal components are solved, and the high and low frequency indexes of each modal component are performed, and the high-frequency vibration correlation coefficients are taken for fusion processing to characterize the characteristic indicators under a single bolt preload; Module 4 is used to: construct characteristic indicators of flange bolt preload changes based on characteristic indicators under different preload forces, and detect the current bolt preload state.

6. A method for detecting the preload force of flange bolts of GIS pot-type insulators according to claim 5, characterized in that: The module 2 is used to obtain the correlation coefficient of the flange bolts in different modes after decomposition, using the following formula: Where: IMF_k i (t), i = 1, ..., n is the i modal components obtained by decomposition; x(t) is the original vibration response signal; Cov(IMF_k i (t), x(t)) is the covariance of each modal component and the original vibration signal.

7. A method for detecting the preload force of flange bolts of GIS pot-type insulators according to claim 6, characterized in that: The module three is used for: The decomposed IMF components are distinguished into high and low frequencies. The specific method is: IMF1 is used as index 1, IMF1+IMF2 is index 2, and so on. The sum of the first i IMFs is added as index i, and a t-test is performed to determine whether the mean is significantly different from the original vibration signal. According to the obtained p-value, the first i IMF components are determined to be high-frequency components; The correlation coefficients of all high-frequency components are counted, the negative correlation coefficients are eliminated, and the positive correlation coefficients are superimposed. Their values ​​are used as the characteristic indicators of the single preload force of the flange bolts. The characteristic indicators of different preload forces are statistically analyzed to obtain the characteristic indicators that change with the preload force.

8. A method for detecting the preload force of flange bolts of GIS pot-type insulators according to claim 7, characterized in that: The module four is used to collect the vibration response signal of the flange bolt, and after modal analysis and processing of the vibration response signal, determine the change of the collected flange bolt preload through the constructed characteristic index of the flange bolt preload change, so as to realize the state detection of the bolt preload.

9. A product characterized by include: Memory, used to store programs; A processor, configured to implement the method according to any one of claims 1 to 4 by executing the program stored in the memory.

10. A computer-readable storage medium, characterized in that: The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 4.

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