Online detection method and equipment for mechanical performance of glass fiber reinforced plastic connecting piece, medium and product

Abstract

The invention discloses a glass fiber reinforced plastic connecting piece mechanical performance online detection method and device, a medium and a product, and relates to the field of nondestructive detection.The method comprises the steps that a GFRP connecting piece sample is obtained; carrying out mechanical accelerated aging and acoustic emission loading tests with different time lengths on the GFRP connecting piece sample, and acquiring a characteristic acoustic signal by utilizing an acoustic emission sensor; determining the bending strength of each GFRP connecting piece sample after the test; constructing a mechanical strength model of the glass fiber reinforced plastic connecting piece according to the residual mechanical strength and the corresponding characteristic sound signal; acquiring a real-time characteristic acoustic signal of the GFRP connecting piece in a working state by using an acoustic emission sensor; and determining the real-time residual mechanical strength by adopting a glass fiber reinforced plastic connecting piece mechanical strength model according to the real-time characteristic sound signal and the corresponding borne load. The mechanical performance of the GFRP connecting piece can be detected in a lossless, time-saving and real-time manner in the equipment operation process, and the health state is evaluated.

Description

Technical Field

[0001] The present application relates to the field of nondestructive testing, and in particular to an online testing method, equipment, medium and product for the mechanical properties of a glass fiber reinforced plastic connector. Background Art

[0002] Glass fiber reinforced plastic (GFRP) connectors, as the support of anode saturated reactors, are one of the important components in high-voltage converter stations. Their mechanical properties are crucial to the safety of converter stations. However, since 2015, 53 cases of surface cracking of GFRP connectors have occurred in 6 converter stations, seriously threatening the safe and stable operation of the converter stations. However, at present, the mechanical properties of disassembled GFRP connectors can only be obtained by destructive testing of bending, tensile and shearing test methods specified by national standards, and their mechanical properties cannot be effectively detected online in real time to ensure their safe operation. In order to effectively avoid the occurrence of extremely serious failures of connectors and cause huge economic losses, it is necessary to detect their mechanical properties online.

[0003] Although non-destructive testing technologies have achieved certain results in detecting the mechanical properties of GFRP equipment, such as ultrasonic testing technology, infrared thermal imaging testing technology, and terahertz testing technology, most non-destructive testing technologies currently rely mainly on external excitation to obtain response signals under large-scale air gap defects to detect the mechanical properties of GFRP equipment. However, in actual field conditions, such large-scale air gap defects rarely appear inside the equipment, so current non-destructive testing technologies cannot achieve online non-destructive testing of the mechanical properties of power equipment.

[0004] Therefore, based on the above problems, there is an urgent need to provide a new online detection method or device for the mechanical properties of GFRP connectors. Summary of the invention

[0005] The purpose of this application is to provide a method, equipment, medium and product for online detection of the mechanical properties of glass fiber reinforced plastic connectors, which can detect the mechanical properties of GFRP connectors in a non-destructive, time-saving and real-time manner during the operation of the equipment and evaluate the health status.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for online detection of mechanical properties of a glass fiber reinforced plastic connector, the method comprising:

[0008] Obtain samples of glass fiber reinforced plastic connectors;

[0009] The glass fiber reinforced plastic connector samples were subjected to mechanical accelerated aging and acoustic emission loading tests of different durations, and the characteristic acoustic signals were obtained using acoustic emission sensors;

[0010] Determine the bending strength of each glass fiber reinforced plastic connector sample after the test; the bending strength is the residual mechanical strength;

[0011] According to the residual mechanical strength of each glass fiber reinforced plastic connector sample after the test and the corresponding characteristic acoustic signal, a mechanical strength model of the glass fiber reinforced plastic connector is constructed;

[0012] Acoustic emission sensors are used to obtain real-time characteristic acoustic signals of glass fiber reinforced plastic connectors in working conditions;

[0013] According to the real-time characteristic acoustic signal and the corresponding borne load, the real-time residual mechanical strength is determined by using the mechanical strength model of the glass fiber reinforced plastic connector.

[0014] Optionally, the method further includes: performing mechanical accelerated aging and acoustic emission loading tests on the glass fiber reinforced plastic connector samples for different durations, and obtaining characteristic acoustic signals using an acoustic emission sensor.

[0015] A weak sound signal detection platform is built based on the acoustic emission detection principle; the weak sound signal detection platform includes: a universal testing machine, an acoustic emission sensor, an acoustic emission module and a host computer software; the acoustic emission module contains a preamplifier, an acoustic emission signal real-time acquisition unit and a waveform collector.

[0016] Optionally, the host computer software is AE win TM software.

[0017] Optionally, the determining of the bending strength of each glass fiber reinforced plastic connector sample after the test specifically includes:

[0018] The bending strength of each glass fiber reinforced plastic connector sample after the test is determined according to the standard bending test steps specified in the national standard GB / T 1449-2005.

[0019] Optionally, the mechanical strength model of the glass fiber reinforced plastic connector specifically includes the following formula:

[0020] σ f =-3.08e 0.00145f ×v+e (0.000112f+4.81) ;

[0021] Where v is the impact count rate, the impact count rate is the characteristic sound signal, e is the natural logarithm, f is the load of the glass fiber reinforced plastic connector under working conditions, σ f Residual mechanical strength.

[0022] Optionally, the determining of the real-time residual mechanical strength using a glass fiber reinforced plastic connector mechanical strength model according to the real-time characteristic acoustic signal and the corresponding borne load specifically includes:

[0023] The finite element simulation method is used to determine the load of glass fiber reinforced plastic connectors under working conditions.

[0024] In a second aspect, the present application provides an online detection device for mechanical properties of a glass fiber reinforced plastic connector, the online detection device for mechanical properties of a glass fiber reinforced plastic connector comprising:

[0025] A sample acquisition module, used for acquiring samples of glass fiber reinforced plastic connectors;

[0026] A test module is used to perform mechanical accelerated aging and acoustic emission loading tests of different lengths on glass fiber reinforced plastic connector samples, and obtain characteristic acoustic signals using an acoustic emission sensor; determine the bending strength of each glass fiber reinforced plastic connector sample after the test; the bending strength is the residual mechanical strength;

[0027] A model building module, used to build a mechanical strength model of the glass fiber reinforced plastic connector according to the residual mechanical strength of each glass fiber reinforced plastic connector sample after the test and the corresponding characteristic acoustic signal;

[0028] A real-time characteristic sound signal acquisition module is used to acquire the real-time characteristic sound signal of the glass fiber reinforced plastic connector in the working state by using an acoustic emission sensor;

[0029] The real-time residual mechanical strength determination module is used to determine the real-time residual mechanical strength according to the real-time characteristic acoustic signal and the corresponding borne load by adopting the mechanical strength model of the glass fiber reinforced plastic connector.

[0030] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for online detection of mechanical properties of glass fiber reinforced plastic connectors.

[0031] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for online detection of mechanical properties of glass fiber reinforced plastic connectors.

[0032] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for online detection of mechanical properties of glass fiber reinforced plastic connectors.

[0033] According to the specific embodiments provided in this application, this application has the following technical effects:

[0034] The present application provides a method, device, medium and product for online detection of mechanical properties of glass fiber reinforced plastic connectors. The acoustic emission sensor receives the tiny vibration signal generated when an external load acts on the GFRP connector, and obtains a characteristic acoustic signal, i.e., the impact count rate, based on the tiny vibration signal. The mathematical relationship between the impact count rate and the residual mechanical strength is constructed to achieve quantitative calculation of the mechanical properties of the GFRP connector. The present application proposes to use the acoustic emission method to detect the mechanical properties of GFRP connectors online, which can detect the mechanical properties of GFRP connectors in a non-destructive, time-saving and real-time manner during the commissioning of the equipment, and evaluate the health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a schematic diagram of an online detection process of mechanical properties of a glass fiber reinforced plastic connector in one embodiment of the present application;

[0037] Figure 2 Schematic diagram of the structure of a weak sound signal detection platform built based on the acoustic emission detection principle;

[0038] Figure 3 This is a schematic diagram showing the comparison of the average residual mechanical strength of samples after 0.5h mechanical accelerated aging before and after the acoustic emission test;

[0039] Figure 4 Schematic diagram of the relationship between impact counting rate and residual mechanical strength under different loading loads;

[0040] Figure 5 This is the relationship diagram between the loading load and the slope and intercept. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0043] In an exemplary embodiment, Figure 1 As shown, an online detection method for mechanical properties of a glass fiber reinforced plastic connector is provided, and the method includes the following S101 to S106. Among them:

[0044] S101, obtaining a sample of a glass fiber reinforced plastic connector;

[0045] A weak sound signal detection platform is built based on the acoustic emission detection principle; the weak sound signal detection platform includes: a universal testing machine, an acoustic emission sensor, an acoustic emission module and a host computer software; the acoustic emission module contains a preamplifier, an acoustic emission signal real-time acquisition unit and a waveform collector. The host computer software used is AE win TM software.

[0046] Acoustic emission detection principle When building a weak acoustic signal detection platform, first, use a USB cable to connect the host computer and the acoustic emission module; then, use a coupling line to connect the acoustic emission sensor to the acoustic emission module; finally, place the GFRP connector sample on the universal testing machine, and place the acoustic emission sensor on the right side of the GFRP connector sample, and fill the coupling agent between the sample and the sensor. The specific experimental connection diagram is shown in the figure. Figure 2 shown.

[0047] The model of the universal testing machine is AT-L-2, with a load application range of 0.2-5000N. Its main function is to apply a load in a specific direction to the test sample through electric loading, so that the GFRP connector sample gradually fails and generates a characteristic sound signal;

[0048] The model of the acoustic emission sensor is R15a, and its operating frequency range is 50kHz-400kHz. Its main function is to receive the acoustic signal during the fracture failure process of the GFRP connector sample;

[0049] The model of the acoustic emission module is 1283USB AE NODE, which contains a preamplifier, an acoustic emission signal real-time acquisition module, and a waveform collector. Its main functions are to amplify the acoustic signal received by the sensor, extract the characteristic parameters contained in the acoustic signal, and extract the waveform;

[0050] The host computer software is AE win TM Software, to achieve interaction with hardware devices, complete data storage and playback. The host computer converts the received data into TXT files for storage, and uses orgin, matlab and other software to reprocess the data to achieve in-depth analysis and research of acoustic signals.

[0051] S102, conducting mechanical accelerated aging and acoustic emission loading tests of different durations on glass fiber reinforced plastic connector samples, and obtaining characteristic acoustic signals using an acoustic emission sensor;

[0052] According to the national standard GB / T 1449-2005, standard block GFRP specimens were prepared, with a sample quantity of 65 and a sample size of 18cm×1.5cm×1cm. First, a standard bending test was performed on 5 GFRP connector samples according to the national standard GB / T 1449-2005, and the test results are shown in Table 1. The mechanical properties of the GFRP connector samples are dispersed, so the average value of the bending failure load of the 5 GFRP connector samples was taken as the final result of the sample, and the bending failure load of the GFRP connector sample was 815.6N. In order to obtain samples with different residual mechanical strengths, the standard samples were then subjected to mechanical accelerated aging for different lengths using a universal testing machine. The loading load was set to 65% of the bending failure load (530.14N), and the aging time was 0.5h, 6h, 12h, 18h, and 24h. The number of samples in each group was 12.

[0053] Table 1 Bending failure load test results of GFRP connector samples

[0054]

[0055]

[0056] After the mechanical accelerated aging, the samples were subjected to an acoustic emission loading test. Considering the fluctuation of stress on GFRP connectors under actual working conditions, the loading loads of the acoustic emission test were set to four types of loads, namely 29% (236.52N), 34% (277.30N), 39% (318.08N), and 44% (358.86N) of the bending failure load of the new connector, expressed as A-29%, A-34%, A-39%, and A-44%. In order to clarify the feasibility of online detection of the residual mechanical strength of GFRP connector samples by acoustic emission technology, three samples with mechanical accelerated aging for 0.5h under four experimental groups of A-29%, A-34%, A-39%, and A-44% were subjected to a 30-min mechanical loading test, and the acoustic emission sensor was used to receive the characteristic acoustic signals generated by local damage of the samples during the test.

[0057] S103, determining the bending strength of each glass fiber reinforced plastic connector sample after the test; the bending strength is the residual mechanical strength;

[0058] The bending strength of each glass fiber reinforced plastic connector sample after the test was determined according to the standard bending test procedure specified in the national standard GB / T 1449-2005, and compared with the residual mechanical strength of the sample without 30 minutes of mechanical loading test.

[0059] Comparison results of the average residual mechanical strength of samples after 0.5h accelerated aging before and after the acoustic emission test, such as Figure 3 As shown. Figure 3 It can be seen that in the sample group of the 0.5h accelerated aging test, the maximum difference between the average residual mechanical strength of the samples without acoustic emission experimental load and under different loading loads is only 1.74MPa, and the change in the residual mechanical strength before and after loading is only 1.41%. It can be considered that the residual mechanical strength of the samples before and after the acoustic emission loading test has no change. Therefore, the residual mechanical strength of the samples after the test can represent the residual mechanical strength of the samples before and during the test.

[0060] A 30-min acoustic emission loading test was performed on all remaining samples of the A-29%, A-34%, A-39% and A-44% experimental groups. After the test, the remaining mechanical strength of the samples was tested. The test results are shown in Tables 2 to 5.

[0061] Table 2 Corresponding results of impact counting rate and residual mechanical strength of A-29% sample group

[0062]

[0063] Table 3A-34% sample group impact count rate and residual mechanical strength corresponding results

[0064]

[0065]

[0066] Table 4 Corresponding results of impact counting rate and residual mechanical strength of A-39% sample group

[0067]

[0068] Table 5A-44% sample group impact count rate and residual mechanical strength corresponding results

[0069]

[0070]

[0071] S104, constructing a mechanical strength model of the glass fiber reinforced plastic connector according to the residual mechanical strength of each glass fiber reinforced plastic connector sample after the test and the corresponding characteristic acoustic signal;

[0072] By fitting the impact counting rate and residual mechanical strength under different loading loads, the fitting results are as follows: Figure 4 shown.

[0073] according to Figure 4 It can be seen that under the same loading conditions, the residual mechanical strength of the GFRP connector samples is approximately negatively linearly related to the acoustic emission impact count rate. The fitting results of the four groups of samples, A-29%, A-34%, A-39% and A-44%, are shown in formulas (1), (2), (3) and (4).

[0074] σ f-29％ =-5.37×v+126.18 (1)

[0075] σ f-34％ =-5.60×v+126.66 (2)

[0076] σ f-39％ =-6.09×v+127.40 (3)

[0077] σ f-44％ =-6.36×v+127.87 (4)

[0078] Among them, σ f is the residual mechanical strength, in MPa; v is the impact count rate.

[0079] By analyzing formula (1)-formula (4), it can be found that under different loading loads f, the residual mechanical strength σ f The relationship between the impact count rate v is shown in formula (5). Through the acoustic emission test, it is found that when the loading load f is known, the corresponding slope k and intercept c can be obtained. Therefore, by constructing the relationship between the slope k, intercept c and the loading load f, the residual mechanical strength of the GFRP material under any loading load can be calculated. In the acoustic emission test, the slope k and intercept c of the corresponding fitting line for different loads f are shown in Table 6.

[0080] Table 6 Corresponding results of loading load, slope and intercept

[0081]

[0082] The data were fitted according to the loading loads and the corresponding slope and intercept values ​​in Table 6. The fitting results are shown in Figure 5 shown.

[0083] Depend on Figure 5 It can be seen that the relationship equation between the loading load and the slope and the relationship equation between the loading load and the intercept are shown in formula (6) and formula (7).

[0084] k = 3.80 × e 0.00145f (6)

[0085] c=e (0.000112×f+4.81) (7)

[0086] Then, by substituting formula (6) and formula (7) into formula (5), we can obtain the general formula for the relationship between the residual mechanical strength of the GFRP sample and the impact counting rate under the condition of a certain loading load, as shown in formula (8).

[0087] σ f =-3.08e 0.00145f ×v+e (0.000112f+4.81) (8)

[0088] Where, e is the natural logarithm, and f is the load of the glass fiber reinforced plastic connector under working conditions.

[0089] Three standard samples were prepared from some operational GFRP connectors. Acoustic emission loading tests were carried out under a loading condition of 50% of the failure load (145 N). The impact counting rate v generated during the test was obtained. The estimated value of the residual mechanical strength of the sample was obtained according to formula (8) and compared with the actual test value to verify the accuracy of the model.

[0090] The test results are shown in Table 7. It can be seen from Table 7 that the maximum error between the model evaluation value and the actual measurement value is about 3%, so it can be considered that the mathematical model can be used to detect the residual mechanical strength of GFRP connectors.

[0091] Table 7 Acoustic emission test results of cracked GFRP connectors

[0092]

[0093] S105, using an acoustic emission sensor to obtain a real-time characteristic acoustic signal of the glass fiber reinforced plastic connector in a working state;

[0094] S106, determining the real-time residual mechanical strength using a glass fiber reinforced plastic connector mechanical strength model according to the real-time characteristic acoustic signal and the corresponding borne load.

[0095] S106 specifically includes:

[0096] A single acoustic emission sensor can receive characteristic acoustic signals within a maximum distance of 80-100 cm from the sound source. Therefore, under actual working conditions, the acoustic emission sensors can be placed at the plane positions at both ends of the sample to receive the internal characteristic acoustic signals of the GFRP connector, namely the impact count rate v. In addition, the load f borne by the GFRP connector under working conditions can be obtained by means of finite element simulation methods and other means. Substituting the rate v and load f obtained under actual working conditions into formula (8) can obtain the real-time residual mechanical strength of the GFRP connector, thereby realizing non-destructive testing of the mechanical properties of the GFRP connector in the converter valve field under non-destructive conditions.

[0097] Based on the same inventive concept, the embodiment of the present application also provides an online detection device for the mechanical properties of glass fiber reinforced plastic connectors for realizing the online detection method for the mechanical properties of glass fiber reinforced plastic connectors involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the online detection device for the mechanical properties of glass fiber reinforced plastic connectors provided below can refer to the limitations of the online detection method for the mechanical properties of glass fiber reinforced plastic connectors above, and will not be repeated here.

[0098] In an exemplary embodiment, an online detection device for mechanical properties of a glass fiber reinforced plastic connector is provided, comprising:

[0099] A sample acquisition module, used for acquiring samples of glass fiber reinforced plastic connectors;

[0100] A test module is used to perform mechanical accelerated aging and acoustic emission loading tests of different lengths on glass fiber reinforced plastic connector samples, and obtain characteristic acoustic signals using an acoustic emission sensor; determine the bending strength of each glass fiber reinforced plastic connector sample after the test; the bending strength is the residual mechanical strength;

[0101] A model building module, used to build a mechanical strength model of the glass fiber reinforced plastic connector according to the residual mechanical strength of each glass fiber reinforced plastic connector sample after the test and the corresponding characteristic acoustic signal;

[0102] A real-time characteristic sound signal acquisition module is used to acquire the real-time characteristic sound signal of the glass fiber reinforced plastic connector in the working state by using an acoustic emission sensor;

[0103] The real-time residual mechanical strength determination module is used to determine the real-time residual mechanical strength according to the real-time characteristic acoustic signal and the corresponding borne load by adopting the mechanical strength model of the glass fiber reinforced plastic connector.

[0104] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an online detection method for mechanical properties of a glass fiber reinforced plastic connector is implemented.

[0105] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0106] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0107] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0108] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0109] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0110] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0111] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for online detection of mechanical properties of glass fiber reinforced plastic connectors, characterized in that: The method for online detection of mechanical properties of glass fiber reinforced plastic connectors comprises: Obtain samples of glass fiber reinforced plastic connectors; The glass fiber reinforced plastic connector samples were subjected to mechanical accelerated aging and acoustic emission loading tests of different durations, and the characteristic acoustic signals were obtained using acoustic emission sensors; Determine the bending strength of each glass fiber reinforced plastic connector sample after the test; the bending strength is the residual mechanical strength; According to the residual mechanical strength of each glass fiber reinforced plastic connector sample after the test and the corresponding characteristic acoustic signal, a mechanical strength model of the glass fiber reinforced plastic connector is constructed; Acoustic emission sensors are used to obtain real-time characteristic acoustic signals of glass fiber reinforced plastic connectors in working conditions; According to the real-time characteristic acoustic signal and the corresponding borne load, the real-time residual mechanical strength is determined by using the mechanical strength model of the glass fiber reinforced plastic connector.

2. The method for online detection of mechanical properties of glass fiber reinforced plastic connectors according to claim 1, characterized in that: The method of subjecting the glass fiber reinforced plastic connector samples to accelerated mechanical aging and acoustic emission loading tests of different durations, and obtaining characteristic acoustic signals using an acoustic emission sensor, also includes: A weak sound signal detection platform is built based on the acoustic emission detection principle; the weak sound signal detection platform includes: a universal testing machine, an acoustic emission sensor, an acoustic emission module and a host computer software; the acoustic emission module contains a preamplifier, an acoustic emission signal real-time acquisition unit and a waveform collector.

3. The method for online detection of mechanical properties of glass fiber reinforced plastic connectors according to claim 2, characterized in that: The host computer software used is AE win TM software.

4. The method for online detection of mechanical properties of glass fiber reinforced plastic connectors according to claim 1, characterized in that: The determination of the bending strength of each glass fiber reinforced plastic connector sample after the test specifically includes: The bending strength of each glass fiber reinforced plastic connector sample after the test is determined according to the standard bending test steps specified in the national standard GB / T 1449-2005.

5. The method for online detection of mechanical properties of glass fiber reinforced plastic connectors according to claim 1, characterized in that: The mechanical strength model of glass fiber reinforced plastic connectors specifically includes the following formulas: s f =-3.08e 0.00145f ×v+e (0 .000112f+4 .81) ; Where v is the impact count rate, the impact count rate is the characteristic sound signal, e is the natural logarithm, f is the load of the glass fiber reinforced plastic connector under working conditions, σ f Residual mechanical strength.

6. The method for online detection of mechanical properties of glass fiber reinforced plastic connectors according to claim 1, characterized in that: The method of determining the real-time residual mechanical strength of a glass fiber reinforced plastic connector by using a mechanical strength model of the glass fiber reinforced plastic connector according to the real-time characteristic acoustic signal and the corresponding load is specifically as follows: The finite element simulation method is used to determine the load of glass fiber reinforced plastic connectors under working conditions.

7. An online detection device for mechanical properties of glass fiber reinforced plastic connectors, characterized in that: The on-line detection equipment for mechanical properties of glass fiber reinforced plastic connectors comprises: A sample acquisition module, used for acquiring samples of glass fiber reinforced plastic connectors; A test module is used to perform mechanical accelerated aging and acoustic emission loading tests of different lengths on glass fiber reinforced plastic connector samples, and obtain characteristic acoustic signals using an acoustic emission sensor; determine the bending strength of each glass fiber reinforced plastic connector sample after the test; the bending strength is the residual mechanical strength; A model building module, used to build a mechanical strength model of a glass fiber reinforced plastic connector according to the residual mechanical strength of each glass fiber reinforced plastic connector sample after the test and the corresponding characteristic acoustic signal; A real-time characteristic sound signal acquisition module is used to acquire the real-time characteristic sound signal of the glass fiber reinforced plastic connector in the working state by using an acoustic emission sensor; The real-time residual mechanical strength determination module is used to determine the real-time residual mechanical strength according to the real-time characteristic acoustic signal and the corresponding load carried by adopting the mechanical strength model of the glass fiber reinforced plastic connector.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for online detection of mechanical properties of a glass fiber reinforced plastic connector according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for online detection of mechanical properties of a glass fiber reinforced plastic connector according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for online detection of mechanical properties of a glass fiber reinforced plastic connector according to any one of claims 1 to 6 is implemented.

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