Detection and monitoring system for fault early warning in use of insulating sling

Abstract

The invention discloses a detection and monitoring system for fault early warning in use of an insulating sling, relates to the technical field of electrical safety and intelligent monitoring, and is used for solving the problem that an internal progressive deterioration trend cannot be captured. A microwave resonance signal in the sling is obtained, the resonant frequency offset of each layer is extracted based on spectral decomposition, and a humidity distribution result is inversed and fed back in combination with a dielectric property database, and is used for setting a monitoring period and collecting operation data; a key area is screened based on a humidity diffusion coefficient, an evaluation area of which the risk level needs to be improved is identified by using a weighted average method, and the risk weight is calculated and the early warning time is set through a nonlinear model in combination with the physical attribute and the diffusion coefficient of the area, so that the early identification and dynamic early warning of the hidden fault of the sling are realized, and the safety and the monitoring precision are effectively improved. And the sudden failure risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical safety and intelligent monitoring, and more particularly to a detection and monitoring system for early warning of faults during use of an insulating sling. Background Art

[0002] In live working scenarios such as high-voltage transmission lines and substations, insulating slings are critical insulating protective tools, widely used to support, suspend, and isolate high-voltage equipment to ensure the electrical safety of workers and equipment. However, due to long-term use, erosion from humid environments, or aging of the microstructure, moisture can gradually accumulate inside the insulating slings, leading to a decrease in their dielectric properties and the risk of partial discharge or even breakdown failure.

[0003] Existing technologies typically assess the condition of slings through manual visual inspection or periodic insulation resistance testing. However, these methods suffer from long inspection cycles, lack of real-time monitoring, inability to quantify internal moisture diffusion behavior, and delayed warnings. For example, traditional methods struggle to identify the early stages of microcracks in the sling's internal structure or the formation of moisture gradients at material interfaces, potentially posing a safety hazard.

[0004] The existing technology has the following deficiencies:

[0005] Currently, there is a lack of a detection and monitoring system that can dynamically sense and assess the internal moisture distribution and diffusion risk of slings based on changes in the material's electromagnetic properties. Traditional methods based on surface detection or discontinuous measurement struggle to capture the internal progressive degradation trend and form an effective risk weighting model, making it difficult to achieve the goals of "intelligent, regional, and proactive" fault warning. Therefore, a detection and monitoring system for fault warning during use of insulating slings is proposed.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a detection and monitoring system for fault warning during use of insulating slings, which solves the problems raised in the above-mentioned background technology by applying microwave resonance spectrum analysis technology, humidity gradient inversion modeling, regional diffusion coefficient dynamic evaluation algorithm and risk weight-based warning mechanism.

[0008] To achieve the above object, the present invention provides the following technical solution: a detection and monitoring system for early warning of faults during use of insulating slings, comprising a data acquisition module, a humidity gradient analysis module, a state evaluation module, and an early warning processing module;

[0009] The data acquisition module is used to collect the microwave resonance signal inside the sling and transmit the collected signal to the humidity gradient analysis module. After receiving the humidity distribution result returned by the humidity gradient analysis module, the data acquisition module sets a monitoring cycle to collect the sling operation status data and transmits it to the status evaluation module;

[0010] The humidity gradient analysis module uses a spectrum decomposition algorithm to extract the resonant frequency offset of each material layer based on the incoming microwave resonance signal, and inverts the dielectric loss tangent value of each material layer based on the dielectric properties database. It generates humidity distribution results and returns them to the data acquisition module.

[0011] The status assessment module is used to receive the sling operation status data, screen out key areas based on the operation status data and calculate the humidity diffusion coefficient of the corresponding areas, detect abnormal behavior of the sling and perform secondary screening of the key areas to obtain assessment areas, use the weighted average method to determine whether to increase the humidity risk level of the assessment area, mark the assessment area where the humidity risk level is increased, and send the humidity diffusion coefficient of the marked area to the early warning processing module;

[0012] After receiving the marked area, the early warning processing module obtains the physical properties of the marked area, calculates the risk weight of the corresponding marked area based on the physical properties of the marked area and the humidity diffusion coefficient, and sets the early warning time for the marked area according to the risk weight.

[0013] In a preferred embodiment, the microwave resonance signal in the data acquisition module includes the resonance frequency variation amplitude of each material layer inside the sling and the resonance signal intensity attenuation rate;

[0014] When the ring microwave resonator inside the sling is working, the frequency difference between the microwave signal it transmits and the reflected signal is recorded, the material layer corresponding to the frequency difference is identified, and the amplitude of the resonant frequency change of each material layer is counted;

[0015] The intensity attenuation rate of the resonant signal is obtained by measuring the intensity attenuation of the microwave signal after passing through the material layer.

[0016] In a preferred embodiment, the humidity gradient analysis module integrates the resonant frequency change amplitude and the resonant signal intensity attenuation rate, uses a spectrum decomposition algorithm to extract the resonant frequency offset of each material layer, and combines it with the dielectric properties database to invert the dielectric loss tangent value of each material layer. The specific steps are as follows:

[0017] The resonant frequency variation amplitude and resonant signal intensity attenuation rate of multiple sling samples are collected and normalized respectively. The resonant frequency variation amplitude is merged into a frequency data set, and the resonant signal intensity attenuation rate is merged into an intensity data set. The ratio of the data in the frequency data set or the intensity data set to the maximum value in the corresponding data set is used as the normalization result;

[0018] A data set is randomly selected from the frequency dataset and the intensity dataset as the benchmark data, the decomposition coefficient K is set, and the K data sets with the smallest difference from the benchmark data are selected from each dataset as the reference data. The mean of the reference data in the frequency dataset and the intensity dataset is calculated and summed to obtain the humidity distribution threshold.

[0019] The humidity distribution value is obtained by summing the normalized results of the resonance frequency change amplitude and the resonance signal intensity attenuation rate of each material layer inside the sling, and the humidity distribution value is compared with the humidity distribution threshold;

[0020] When the humidity distribution value exceeds the humidity distribution threshold, it is determined that there is a high humidity gradient area inside the sling; when the humidity distribution value is lower than the humidity distribution threshold, it is determined that the inside of the sling is a low humidity gradient area.

[0021] In a preferred embodiment, the humidity gradient analysis module determines the humidity distribution inside the sling and returns the humidity distribution result to the data acquisition module. When there is a high humidity gradient area inside the sling, the data acquisition module sets a monitoring cycle to collect the sling operation status data. The operation status data is the stress distribution of the sling in each area.

[0022] The data acquisition module presets a monitoring period, records and counts the stress changes in each area of ​​the sling during the monitoring period, obtains the stress distribution of the sling in each area during the monitoring period, and transmits the stress distribution of the sling in each area to the status assessment module.

[0023] In a preferred embodiment, the state assessment module receives the stress distribution of the sling in each region, selects the median of the stress distribution in each region as a screening criterion, screens out regions where the stress distribution exceeds the screening criterion as key regions, and uses the ratio of the stress distribution in the key region to the screening criterion as the moisture diffusion coefficient of the corresponding region;

[0024] The abnormal behavior of the sling refers to the local stress mutation phenomenon that occurs in the sling during the monitoring period. The area corresponding to the stress mutation phenomenon is identified and matched with the key area. The area with successful matching is retained as the evaluation area. The stress mutation ratio of the evaluation area is calculated. The number of the same evaluation areas corresponding to the stress mutation phenomenon is accumulated and divided by the total number of stress mutation phenomena to obtain the stress mutation ratio of the corresponding evaluation area.

[0025] In a preferred embodiment, the state assessment module comprehensively assesses the humidity diffusion coefficient and stress mutation ratio of the assessment area and uses a weighted average method to determine whether to increase the humidity risk level of the assessment area. The specific steps are as follows:

[0026] Data standardization: the humidity diffusion coefficient and stress mutation ratio of the assessment area are normalized to the same dimension;

[0027] Repeat the normalization and preset n normalization parameters to obtain the normalized results of multiple humidity diffusion coefficients or stress mutation ratios;

[0028] Data superposition: the humidity diffusion coefficient and stress mutation ratio after data normalization using the same normalization parameter are added together to obtain multiple superposition values;

[0029] Superposition processing: square multiple superposition values ​​and take the arithmetic mean as the superposition processing amount;

[0030] Square root processing: take the square root of the superimposed processing amount to obtain the risk assessment value; when the risk assessment value of the assessment area exceeds the preset risk threshold, the humidity risk level of the assessment area will not be increased;

[0031] When the risk assessment value of the assessment area is lower than the preset risk threshold, the humidity risk level of the assessment area will be increased.

[0032] In a preferred embodiment, the physical properties of the marking area are obtained, including the thickness and tensile strength of each material in the corresponding marking area.

[0033] In a preferred embodiment, when calculating the risk weight of the marked area, the early warning processing module randomly selects a preset number of materials in the marked area as target materials, collects the thickness and tensile strength of each target material, calculates the average thickness of all target materials as the regional thickness, and calculates the sum of the tensile strength of all target materials as the regional strength.

[0034] In a preferred embodiment, the regional thickness and regional intensity of the marked area are normalized and the weighted average value is taken as the risk adjustment, and the risk weight of the corresponding marked area is calculated by a nonlinear formula. The expression of the nonlinear formula is:

[0035] ;

[0036] in, is the humidity diffusion coefficient of the marked area, is the preset nonlinear parameter, is the risk adjustment for the marked area, is the risk weight of the marked area;

[0037] The product of the default warning time of the marked area and the risk weight is used as the warning time of the corresponding marked area.

[0038] In a preferred embodiment, the data acquisition module collects microwave resonance signals through an annular microwave resonator arranged inside the sling. The annular microwave resonator is embedded in different material layers of the sling at uniform intervals to record the frequency difference between the transmitted microwave signal and the reflected signal, and to count the resonance frequency change amplitude and resonance signal intensity attenuation rate of each material layer.

[0039] Technical effects and advantages of the present invention:

[0040] The present invention constructs a data acquisition module, a humidity gradient analysis module, a status assessment module and an early warning processing module. Based on the microwave resonance signal inside the sling, the resonance frequency offset of each material layer of the sling is extracted based on the spectrum decomposition algorithm, and the humidity distribution result is obtained by inversion of the dielectric property database for feedback. The module is used to set the monitoring period and collect the sling operation status data, screen the key areas based on the humidity diffusion coefficient, and use the weighted average method to identify the assessment areas where the risk level needs to be increased. In combination with the regional physical properties and the humidity diffusion coefficient, the regional risk weight is calculated based on the nonlinear model and the early warning time is set. The module realizes the early identification of hidden faults of the sling and dynamic graded early warning, effectively improves the use safety and monitoring accuracy of insulating tools, and reduces the risk of sudden failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention is a schematic diagram of a module framework of a detection and monitoring system for early warning of faults during use of an insulating sling.

[0042] Figure 2 The present invention is a flowchart of the working implementation of a detection and monitoring system for early warning of faults during use of an insulating sling. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] Example 1

[0045] The present invention provides a detection and monitoring system for early warning of faults during use of insulating slings, the structural block diagram of which is shown as follows: Figure 1 As shown, it includes a data acquisition module, a humidity gradient analysis module, a state assessment module and an early warning processing module.

[0046] These modules are connected in sequence through data transmission links to form a complete closed-loop monitoring and early warning system.

[0047] The data acquisition module is responsible for collecting the microwave resonance signal inside the sling and transmitting the signal to the humidity gradient analysis module for processing.

[0048] The humidity gradient analysis module generates humidity distribution results and returns them to the data acquisition module for setting the monitoring cycle and collecting operating status data.

[0049] The operating status data is then passed to the status assessment module, which completes the screening of key areas of the sling and the calculation of the humidity diffusion coefficient, and sends the humidity diffusion coefficient of the marked area to the early warning processing module.

[0050] The early warning processing module calculates the risk weight based on the physical properties and humidity diffusion coefficient of the marked area and sets the early warning time according to the risk weight.

[0051] The core function of the data acquisition module is to collect the microwave resonance signal inside the sling, which includes the resonance frequency change amplitude and the resonance signal intensity attenuation rate.

[0052] The logic for obtaining the resonant frequency variation amplitude is that when microwaves propagate in a medium, their frequency variation is related to the dielectric constant. The resonant frequency variation amplitude is obtained by subtracting the reflected signal frequency from the initial frequency.

[0053] The logic for obtaining the resonance signal intensity attenuation rate is to calculate the difference between the transmitted power and the reflected power and then calculate the ratio with the transmitted power to obtain the resonance signal intensity attenuation rate.

[0054] In practical applications, multiple ring-shaped microwave resonators are arranged inside the sling, and these resonators are embedded in different material layers of the sling at uniform intervals.

[0055] It should be noted that the operating frequency range of the ring microwave resonator is 2.4-5.8GHz, corresponding to the sensitive frequency band of the dielectric properties of the insulating material, and the transmission power is ≤10mW to avoid affecting the insulation performance; it is embedded in different material layers at uniform intervals of 5-10cm along the length of the sling. The interval value is adjusted according to the rated load-bearing capacity of the sling: 5cm when the load-bearing capacity is ≤5t, 8cm when the load-bearing capacity is 5-10t, and 10cm when the load-bearing capacity is greater than 10t. It is used to record the frequency difference between the transmitted microwave signal and the reflected signal, and to calculate the resonant frequency change amplitude and resonant signal intensity attenuation rate of each material layer.

[0056] When the ring microwave resonator is operating, the frequency difference between the transmitted microwave signal and the reflected signal is recorded, and the material layer corresponding to the frequency difference is identified.

[0057] By counting the resonance frequency change amplitude and resonance signal intensity attenuation rate of each material layer, the real-time status information of each material layer inside the sling can be obtained.

[0058] The data acquisition module transmits the acquired resonance frequency variation amplitude and resonance signal intensity attenuation rate to the humidity gradient analysis module.

[0059] The workflow of the humidity gradient analysis module is as follows: Figure 2 As shown, first, the received resonance frequency variation amplitude and resonance signal intensity attenuation rate are normalized.

[0060] The specific method of normalization processing is to merge the resonance frequency change amplitude into a frequency data set, merge the resonance signal intensity attenuation rate into an intensity data set, and take the ratio of the data in the frequency data set or the intensity data set to the maximum value in the corresponding data set as the normalization result.

[0061] It should be noted that there are not only one normalization method mentioned above, but also multiple methods such as maximum and minimum value normalization method, Z-score standard deviation normalization method, decimal calibration normalization method, etc. The specific normalization method is selected by the experimenters based on the distribution characteristics of the target data and the performance evaluation results of the processed model, which will not be elaborated here.

[0062] A data set is randomly selected from the frequency data set and the intensity data set as the benchmark data, and the decomposition coefficient K is set. The K data sets with the smallest difference from the benchmark data are selected from each data set as the reference data.

[0063] The mean of the reference data in the frequency dataset and the intensity dataset is calculated and summed to obtain the humidity distribution threshold.

[0064] Specifically, the formula for calculating the humidity distribution threshold is as follows:

[0065] ;

[0066] Where, is the humidity distribution threshold, For the The normalized resonant frequency variation of each sample point is For the The normalized resonance signal intensity attenuation rate of the sample points, The total number of samples in the current statistical period.

[0067] The humidity distribution value is obtained by summing the normalized results of the resonance frequency change amplitude and the resonance signal intensity attenuation rate of each material layer inside the sling, and then comparing the humidity distribution value with the humidity distribution threshold.

[0068] The calculation formula for the humidity distribution value is as follows:

[0069] ;

[0070] Where, is the humidity distribution value, For the The normalized resonant frequency variation of each sample point is For the The normalized resonance signal intensity decay rate of each sample point.

[0071] If the humidity distribution value exceeds the humidity distribution threshold, it is determined that there is a high humidity gradient area inside the sling; if the humidity distribution value is lower than the humidity distribution threshold, it is determined that the inside of the sling is a low humidity gradient area.

[0072] The humidity gradient analysis module returns the humidity distribution result to the data acquisition module, and the data acquisition module sets a monitoring cycle according to the humidity distribution result to collect the sling operation status data.

[0073] It should be noted that when there is a high humidity gradient area inside the sling, the data acquisition module can preset the monitoring period to 1 hour; if the humidity distribution value of the high humidity gradient area is greater than 1.5×the humidity distribution threshold, the monitoring period can be shortened to 30 minutes. Verified by the humidity diffusion simulation model, this setting can ensure the capture of humidity mutations. The specific monitoring period is set by professionals.

[0074] The operating status data mainly reflects the stress distribution of the sling in each area. The data acquisition module selects a period of time as the monitoring period, records the stress changes in each area of ​​the sling during the monitoring period and counts the number of times, thereby obtaining the stress distribution data of the sling in each area during the monitoring period.

[0075] The stress distribution data is then passed to the condition assessment module.

[0076] The condition assessment module receives the stress distribution data of the sling in each area, selects the median of the stress distribution in each area as a screening criterion, and selects the area where the stress distribution exceeds the screening criterion as the key area.

[0077] The ratio of the stress distribution in the critical area to the screening criterion is defined as the moisture diffusion coefficient of the corresponding area.

[0078] In order to further detect the abnormal behavior of the sling, the state assessment module identifies the local stress mutation phenomenon that occurs in the sling during the monitoring period, matches the area corresponding to the stress mutation phenomenon with the key area, and retains the area with successful matching as the assessment area.

[0079] The stress mutation ratio of the assessment area is calculated by the following steps: the number of the same assessment areas corresponding to the stress mutation phenomenon is accumulated and divided by the total number of stress mutation phenomena to obtain the stress mutation ratio of the corresponding assessment area.

[0080] The status assessment module comprehensively assesses the humidity diffusion coefficient and stress mutation ratio of the area, and uses the weighted average method to determine whether to increase the humidity risk level of the assessment area.

[0081] It's important to note that the mechanism linking stress mutation and humidity risk is that moisture penetration reduces the bond strength between material layers. When the sling is under load, microslips occur between the layers, manifesting as localized stress mutations. Therefore, a higher stress mutation ratio indicates more severe structural degradation due to humidity in that area, and humidity risk assessment should be prioritized.

[0082] The specific steps are as follows:

[0083] First, the humidity diffusion coefficient and stress mutation ratio of the assessment area are normalized to have the same dimension.

[0084] Then, n standardized parameters are preset to obtain standardized results of multiple humidity diffusion coefficients or stress mutation ratios.

[0085] It should be explained that the preset n normalization parameters refer to the calculation basis of multiple preset normalization parameters to adapt to different data distribution characteristics when performing data normalization processing on the humidity diffusion coefficient and stress mutation ratio of the assessment area. The deviation of a single method is reduced through multi-dimensional normalization. For example, if n is 3, maximum and minimum value normalization, Z-score normalization, and decimal scaling normalization are performed respectively. Those skilled in the art can increase or decrease the number of n according to the actual data distribution characteristics;

[0086] The humidity diffusion coefficient and the stress mutation ratio after data normalization using the same normalization parameter are added together to obtain multiple superimposed values.

[0087] After squaring multiple superposition values, the arithmetic mean is taken as the superposition processing amount, and the square root of the superposition processing amount is taken to obtain the risk assessment value.

[0088] If the risk assessment value of the assessment area exceeds the preset risk threshold, the humidity risk level of the assessment area will not be increased;

[0089] If the risk assessment value of the assessment area is lower than the preset risk threshold, the humidity risk level of the assessment area will be increased.

[0090] The assessment area after the humidity risk level is upgraded is marked as a marked area, and the humidity diffusion coefficient of the marked area is then sent to the early warning processing module.

[0091] After receiving the marked area, the early warning processing module obtains the physical properties of the marked area, including the material thickness and tensile strength in the corresponding area.

[0092] Tensile strength is a mechanical property indicator of sling materials. Sling materials are classified and evaluated by setting different tensile strength ranges.

[0093] When calculating the risk weight of the marked area, the early warning processing module randomly selects multiple materials from the materials in the marked area as target materials and collects the thickness and tensile strength of each target material.

[0094] The mean thickness of all target materials is calculated as the regional thickness, and the sum of the tensile strength of all target materials is calculated as the regional strength.

[0095] The regional thickness and regional intensity of the marked area are normalized and the weighted average is taken as the risk adjustment m. The risk weight of the corresponding marked area is calculated using a nonlinear formula.

[0096] The nonlinear formula can be expressed as ;

[0097] in, is the humidity diffusion coefficient of the marked area, is the preset nonlinear parameter, is the risk adjustment for the marked area, is the risk weight of the marked area.

[0098] It should be noted that the value of t is set within the range preset by professionals. The value range can be set based on the moisture sensitivity coefficient of the sling material, the humidity level of the working environment, and the risk correlation in historical failure data. For example, when the ambient humidity is ≤60% RH, t=1.2; when 60% RH<ambient humidity≤80% RH, t=1.5; when the ambient humidity is >80% RH, t=1.8.

[0099] Finally, the product of the default warning time of the marked area and the risk weight is used as the warning time of the corresponding marked area.

[0100] Through the above steps, the early warning processing module can comprehensively evaluate the operation risk of the sling based on the physical properties and humidity diffusion coefficient of the marked area, and dynamically adjust the early warning time, thereby achieving accurate early warning of sling failure.

[0101] The specific implementation process of this system is as follows: In actual operation, the annular microwave resonator arranged inside the sling continuously emits microwave signals, and the data acquisition module monitors the status of each material layer inside the sling in real time by receiving the amplitude of the resonant frequency change and the attenuation rate of the resonant signal intensity.

[0102] The humidity gradient analysis module generates humidity distribution results based on the received data and returns the results to the data acquisition module.

[0103] The data acquisition module sets the monitoring cycle according to the humidity distribution results, collects the stress distribution data of the sling in each area, and transmits the data to the status assessment module.

[0104] The status assessment module further detects abnormal behavior of the sling by screening key areas and calculating the humidity diffusion coefficient, and increases the humidity risk level of the assessment area.

[0105] Finally, the early warning processing module calculates the risk weight based on the physical properties and humidity diffusion coefficient of the marked area, and sets the early warning time according to the risk weight to complete the dynamic monitoring and early warning of the sling failure.

[0106] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is supplemented below with reference to a specific application scenario.

[0107] In practical applications, insulating slings are widely used in high-altitude operations and heavy object lifting scenarios.

[0108] To ensure the safety of the sling, its internal humidity gradient and stress distribution need to be monitored in real time.

[0109] During an industrial equipment lifting task, the slings were in a high-humidity environment and were subjected to alternating loads for a long time.

[0110] At this point, the operating principle of this system and its specific implementation steps are as follows:

[0111] First, multiple ring-shaped microwave resonators are embedded in the sling at even intervals. These resonators are respectively arranged between different material layers of the sling.

[0112] When the sling starts working, the data acquisition module records the frequency difference between the transmitted microwave signal and the reflected signal through the ring microwave resonator, and identifies the material layer corresponding to the frequency difference.

[0113] At the same time, the intensity attenuation of the microwave signal after passing through each material layer is measured to obtain the resonance frequency change amplitude and the resonance signal intensity attenuation rate.

[0114] For example, if the resonant frequency variation of a material layer is 200 kHz and the signal intensity attenuation rate is 30%, then this data will serve as the basic input for subsequent analysis.

[0115] Next, the humidity gradient analysis module receives the data and normalizes it. Specifically, the resonant frequency variation amplitude is combined into a frequency dataset, and the resonant signal intensity decay rate is combined into an intensity dataset. The module then calculates the ratio of each data set to the maximum value in the corresponding data set to achieve normalization.

[0116] Subsequently, a benchmark data is randomly selected and the decomposition coefficient K is set to 5. The five reference data with the smallest difference from the benchmark data are selected in the frequency dataset and the intensity dataset respectively. Their means are calculated and summed to obtain the humidity distribution threshold.

[0117] If the humidity distribution value of a certain area exceeds the humidity distribution threshold, it is determined that the area has a high humidity gradient; otherwise, it is a low humidity gradient.

[0118] This process utilizes a spectral decomposition algorithm to extract the resonant frequency offset of each material layer, and combines it with a dielectric properties database to invert the dielectric loss tangent value of each material layer to generate the humidity distribution result.

[0119] Next, the data acquisition module sets the monitoring period according to the humidity distribution results.

[0120] It should be noted that the dielectric properties database is a system used to store, manage and provide data related to the dielectric properties of various materials, including material type, dielectric parameter range, and measurement conditions; the specific steps of the spectrum decomposition algorithm are to perform Fourier transform on the microwave resonance signal to obtain the frequency amplitude spectrum, use wavelet threshold denoising, divide the frequency interval into 50MHz intervals, extract the resonance peaks in each interval, and calculate the difference between the center frequency of each effective resonance peak and the initial frequency, which is the resonance frequency offset of the corresponding material layer; the decomposition coefficient is used to screen reference data to calculate the humidity distribution threshold. The value is positively correlated with the number of layers of the sling material. The default range is 3-8. For example, if the sling is 3-5 layers of material, the decomposition coefficient can range from 3 to 5.

[0121] In this example, the monitoring period is set to 1 hour. During the monitoring period, the data acquisition module records the stress changes in each area of ​​the sling and calculates the stress distribution data.

[0122] For example, the median stress distribution in a certain area is 50 MPa. If the stress distribution in a certain area exceeds this median, the area will be screened as a critical area.

[0123] The ratio of the stress distribution in the critical area to the screening standard is defined as the moisture diffusion coefficient. In addition, the condition assessment module also detects whether the sling has any local stress mutation during the monitoring period.

[0124] It should be noted that the moisture diffusion coefficient is defined as the ratio of the stress distribution in the key area to the screening standard. The basis is: in the stress concentration area of ​​the sling, the microscopic porosity of the material with stress greater than the median is higher. Through scanning electron microscopy observation, the porosity of the stress concentration area is 1.5-2 times that of the low stress area. The pores provide channels for moisture diffusion, so the stress ratio can indirectly reflect the humidity diffusion capacity.

[0125] If a stress mutation occurs in a key area, the stress mutation ratio of the area is further calculated.

[0126] For example, if the number of stress mutations in a certain assessment area accounts for 40% of the total number of mutations, the stress mutation ratio of the area is 0.4.

[0127] Then, the status assessment module comprehensively evaluates the humidity diffusion coefficient and stress mutation ratio of the area, and uses the weighted average method to determine whether the humidity risk level of the area should be increased.

[0128] Specifically, the humidity diffusion coefficient and stress mutation ratio are first normalized to have the same dimension.

[0129] Subsequently, n=3 normalization parameters were set to obtain multiple normalization results, and the humidity diffusion coefficient and stress mutation ratio processed using the same normalization parameter were added together to obtain multiple superposition values.

[0130] The arithmetic mean of these superimposed values ​​is taken as the superimposed treatment amount, and the square root is further taken to obtain the risk assessment value.

[0131] If the risk assessment value of a certain assessment area is lower than the preset risk threshold, the humidity risk level of the area will be increased and marked as a marked area.

[0132] It should be noted that the preset risk threshold is determined by professionals based on historical failure data statistics. For example, the risk evaluation values ​​of at least 50 groups of sling failure cases are collected and calculated through the algorithm of this system. The distribution of evaluation values ​​when the failure occurs is recorded, and 80% of the minimum risk evaluation value in the failure case is taken as the initial threshold. Combined with the balance requirements of false alarm rate and missed alarm rate in actual application, fine-tuning is performed to determine the preset risk threshold.

[0133] Finally, after receiving the marked area, the early warning processing module obtains its physical properties, including the area thickness and tensile strength.

[0134] For example, five target materials are randomly selected from the marked area, whose thicknesses are 1 mm, 1.2 mm, 1.1 mm, 1.3 mm, and 1.4 mm, and whose tensile strengths are 100 MPa, 120 MPa, 110 MPa, 130 MPa, and 140 MPa, respectively.

[0135] The mean thickness of all target materials was calculated as the regional thickness (1.2 mm), and the sum of the tensile strengths was statistically calculated as the regional strength (600 MPa).

[0136] After normalizing the regional thickness and regional intensity, the weighted average is taken as the risk adjustment m, and the nonlinear formula is used to calculate the risk adjustment m. Calculate risk weights.

[0137] Assume that the humidity diffusion coefficient of the marked area is is 0.8, the preset nonlinear parameter is 1.5, risk adjustment is 0.9, then the risk weight .

[0138] Finally, the product of the default warning time of the marked area and the risk weight is used as the warning time of the corresponding marked area.

[0139] For example, the default warning time is 10 minutes, so the warning time of the marked area is 10×1.08≈11 minutes.

[0140] Through the above steps, the system can dynamically adjust the warning time to achieve accurate warning of sling failure.

[0141] For example, when the moisture diffusion coefficient of a marked area is high and the stress distribution is abnormal, the system will issue an early warning signal to remind the operator to take measures to avoid insulation performance degradation or structural failure due to moisture penetration.

[0142] This process not only improves the accuracy and reliability of sling operation status monitoring, but also significantly reduces safety hazards caused by changes in humidity gradients.

[0143] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0144] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0145] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0146] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0149] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0151] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0152] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A detection and monitoring system for early warning of faults during use of an insulating sling, characterized by: It includes data acquisition module, humidity gradient analysis module, status assessment module and early warning processing module; The data acquisition module is used to collect the microwave resonance signal inside the sling and transmit the collected signal to the humidity gradient analysis module. After receiving the humidity distribution result returned by the humidity gradient analysis module, the data acquisition module sets a monitoring cycle to collect the sling operation status data and transmits it to the status evaluation module; The humidity gradient analysis module uses a spectrum decomposition algorithm to extract the resonant frequency offset of each material layer based on the incoming microwave resonance signal, and inverts the dielectric loss tangent value of each material layer based on the dielectric properties database. It generates humidity distribution results and returns them to the data acquisition module. The status assessment module is used to receive the sling operation status data, screen out key areas based on the operation status data and calculate the humidity diffusion coefficient of the corresponding areas, detect abnormal behavior of the sling and perform secondary screening of the key areas to obtain assessment areas, use the weighted average method to determine whether to increase the humidity risk level of the assessment area, mark the assessment area where the humidity risk level is increased, and send the humidity diffusion coefficient of the marked area to the early warning processing module; After receiving the marked area, the early warning processing module obtains the physical properties of the marked area, calculates the risk weight of the corresponding marked area based on the physical properties of the marked area and the humidity diffusion coefficient, and sets the early warning time for the marked area according to the risk weight.

2. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 1, characterized in that: The microwave resonance signal in the data acquisition module includes the resonance frequency variation amplitude of each material layer inside the sling and the resonance signal intensity attenuation rate; When the ring microwave resonator inside the sling is working, the frequency difference between the microwave signal it transmits and the reflected signal is recorded, the material layer corresponding to the frequency difference is identified, and the amplitude of the resonant frequency change of each material layer is counted; The intensity attenuation rate of the resonant signal is obtained by measuring the intensity attenuation of the microwave signal after passing through the material layer.

3. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 2, characterized in that: The humidity gradient analysis module integrates the resonant frequency change amplitude and the resonant signal intensity attenuation rate, uses the spectrum decomposition algorithm to extract the resonant frequency offset of each material layer, and combines it with the dielectric properties database to invert the dielectric loss tangent value of each material layer. The specific steps are as follows: The resonant frequency variation amplitude and resonant signal intensity attenuation rate of multiple sling samples are collected and normalized respectively. The resonant frequency variation amplitude is merged into a frequency data set, and the resonant signal intensity attenuation rate is merged into an intensity data set. The ratio of the data in the frequency data set or the intensity data set to the maximum value in the corresponding data set is used as the normalization result; A data set is randomly selected from the frequency dataset and the intensity dataset as the benchmark data, the decomposition coefficient K is set, and the K data sets with the smallest difference from the benchmark data are selected from each dataset as the reference data. The mean of the reference data in the frequency dataset and the intensity dataset is calculated and summed to obtain the humidity distribution threshold. The humidity distribution value is obtained by summing the normalized results of the resonance frequency change amplitude and the resonance signal intensity attenuation rate of each material layer inside the sling, and the humidity distribution value is compared with the humidity distribution threshold; When the humidity distribution value exceeds the humidity distribution threshold, it is determined that there is a high humidity gradient area inside the sling; when the humidity distribution value is lower than the humidity distribution threshold, it is determined that the inside of the sling is a low humidity gradient area.

4. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 3, characterized in that: The humidity gradient analysis module determines the humidity distribution inside the sling and returns the humidity distribution results to the data acquisition module. When there is a high humidity gradient area inside the sling, the data acquisition module sets a monitoring cycle to collect the sling operation status data. The operation status data is the stress distribution of the sling in each area. The data acquisition module presets a monitoring period, records and counts the stress changes in each area of ​​the sling during the monitoring period, obtains the stress distribution of the sling in each area during the monitoring period, and transmits the stress distribution of the sling in each area to the status assessment module.

5. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 4, characterized in that: The condition assessment module receives the stress distribution of the sling in each area, selects the median of the stress distribution in each area as a screening criterion, selects the area where the stress distribution exceeds the screening criterion as the key area, and uses the ratio of the stress distribution in the key area to the screening criterion as the humidity diffusion coefficient of the corresponding area; The abnormal behavior of the sling refers to the local stress mutation phenomenon that occurs in the sling during the monitoring period. The area corresponding to the stress mutation phenomenon is identified and matched with the key area. The area with successful matching is retained as the evaluation area. The stress mutation ratio of the evaluation area is calculated. The number of the same evaluation areas corresponding to the stress mutation phenomenon is accumulated and divided by the total number of stress mutation phenomena to obtain the stress mutation ratio of the corresponding evaluation area.

6. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 5, characterized in that: The status assessment module comprehensively assesses the humidity diffusion coefficient and stress mutation ratio of the assessment area, and uses the weighted average method to determine whether to increase the humidity risk level of the assessment area. The specific steps are as follows: Data standardization: the humidity diffusion coefficient and stress mutation ratio of the assessment area are normalized to the same dimension; Repeat the normalization and preset n normalization parameters to obtain the normalized results of multiple humidity diffusion coefficients or stress mutation ratios; Data superposition: the humidity diffusion coefficient and stress mutation ratio after data normalization using the same normalization parameter are added together to obtain multiple superposition values; Superposition processing: square multiple superposition values ​​and take the arithmetic mean as the superposition processing amount; Square root processing: take the square root of the superimposed processing amount to obtain the risk assessment value; when the risk assessment value of the assessment area exceeds the preset risk threshold, the humidity risk level of the assessment area will not be increased; When the risk assessment value of the assessment area is lower than the preset risk threshold, the humidity risk level of the assessment area will be increased.

7. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 1, characterized in that: Obtain the physical properties of the marked area, including the thickness and tensile strength of each material in the corresponding marked area.

8. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 7, characterized in that: When calculating the risk weight of the marked area, the early warning processing module randomly selects a preset number of materials in the marked area as target materials, collects the thickness and tensile strength of each target material, calculates the average thickness of all target materials as the regional thickness, and calculates the sum of the tensile strength of all target materials as the regional strength.

9. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 8, characterized in that: The thickness and intensity of the marked area are normalized and the weighted average is taken as the risk adjustment. The risk weight of the corresponding marked area is calculated using a nonlinear formula. The expression of the nonlinear formula is: ； in, is the humidity diffusion coefficient of the marked area, is the preset nonlinear parameter, is the risk adjustment for the marked area, is the risk weight of the marked area; The product of the default warning time of the marked area and the risk weight is used as the warning time of the corresponding marked area.

10. The detection and monitoring system for early warning of faults during use of an insulating sling according to claim 9, characterized in that: The data acquisition module collects microwave resonance signals through an annular microwave resonator arranged inside the sling. The annular microwave resonator is embedded in the different material layers of the sling at uniform intervals. It is used to record the frequency difference between the transmitted microwave signal and the reflected signal, and to calculate the resonance frequency change amplitude and resonance signal intensity attenuation rate of each material layer.

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