A detection monitoring system for early warning of in-service failure of an insulating sling

Abstract

The application discloses a kind of detection monitoring systems for early warning of insulation sling failure in use, it is related to electrical safety and intelligent monitoring technical field, for solving the problem that internal progressive deterioration trend cannot be captured, by building including data acquisition, humidity gradient analysis, state evaluation and early warning processing module, obtain the microwave resonance signal inside sling, based on frequency spectrum decomposition extraction each layer resonance frequency offset, combined with dielectric characteristic database inversion humidity distribution result and feedback, for setting monitoring period and collection operation data;Based on humidity diffusion coefficient screening key area, using weighted average method to identify the evaluation area that needs to improve risk level, and combining regional physical properties and diffusion coefficient, by nonlinear model calculation risk weight and set early warning time, realize sling implicit fault early identification and dynamic early warning, effectively improve safety and monitoring precision, reduce sudden failure risk.

Description

Technical Field

[0001] This invention relates to the field of electrical safety and intelligent monitoring technology, and more specifically, to a detection and monitoring system for fault early warning during the use of insulated slings. Background Technology

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

[0003] In existing technologies, the condition of slings is typically assessed through manual visual inspection or periodic insulation resistance testing. However, these methods suffer from drawbacks such as long inspection cycles, inability to monitor in real time, inability to quantify internal humidity diffusion behavior, and delayed early warnings. For example, in the early stages when micro-cracks appear in the internal structure of the sling or when humidity gradients form at material layer interfaces, traditional methods are difficult to identify, which can easily lead to safety hazards.

[0004] The existing technology has the following shortcomings:

[0005] Currently, there is a lack of a detection and monitoring system capable of dynamically sensing and assessing the diffusion risk of humidity distribution inside the sling based on changes in the electromagnetic properties of materials. Traditional methods based on surface detection or discontinuous measurement are difficult to capture the gradual internal degradation trend and cannot form an effective risk weight model, making it difficult to achieve the goal of "intelligent, regional, and proactive" fault early warning. Therefore, this paper proposes a detection and monitoring system for fault early warning during the use of insulated slings.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a detection and monitoring system for fault early warning during the use of insulating slings. This system addresses the problems mentioned in the background art by employing microwave resonant spectrum analysis technology, humidity gradient inversion modeling, regional diffusion coefficient dynamic evaluation algorithm, and a risk weight-based early warning mechanism.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a detection and monitoring system for fault early warning during the use of an insulating sling, comprising a data acquisition module, a humidity gradient analysis module, a status assessment module, and an early warning processing module;

[0009] The data acquisition module is used to acquire microwave resonant signals inside the sling and transmit the acquired signals to the humidity gradient analysis module. After receiving the humidity distribution results returned by the humidity gradient analysis module, the data acquisition module sets the monitoring cycle to collect sling operating status data and transmits it to the status assessment 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 input microwave resonant signal, and combines the dielectric property database to invert the dielectric loss tangent of each material layer, generating humidity distribution results and returning them to the data acquisition module.

[0011] The status assessment module is used to receive the operating status data of the sling, filter out key areas based on the operating status data and calculate the humidity diffusion coefficient of the corresponding areas, detect abnormal behavior of the sling and perform secondary screening of key areas to obtain assessment areas, use the weighted average method to determine whether to upgrade the humidity risk level of the assessment area, mark the assessment areas that have been upgraded in humidity risk level, and send the humidity diffusion coefficient of the marked areas to the early warning processing module.

[0012] After receiving the marked area, the early warning processing module obtains the physical attributes of the marked area, calculates the risk weight of the corresponding marked area by combining the physical attributes of the marked area with 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 resonant signal in the data acquisition module includes the amplitude of the resonant frequency change of each material layer inside the sling and the attenuation rate of the resonant signal intensity.

[0014] When the ring microwave resonator inside the sling is working, the frequency difference between the emitted microwave signal 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 statistically analyzed.

[0015] The resonant signal intensity attenuation rate is obtained by measuring the intensity attenuation of the microwave signal after it passes 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 the dielectric property database to invert the dielectric loss tangent 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 were collected and normalized respectively. The resonant frequency variation amplitude was merged into a frequency dataset, and the resonant signal intensity attenuation rate was merged into an intensity dataset. The ratio of the data in the frequency dataset or intensity dataset to the maximum value in the corresponding dataset was used as the normalization result.

[0018] Randomly select a data point from the frequency dataset and the intensity dataset as the baseline data, set the decomposition coefficient K, select the K data points with the smallest difference from the baseline data in each dataset as reference data, calculate the mean of the reference data in the frequency dataset and the intensity dataset, and sum them to obtain the humidity distribution threshold.

[0019] The humidity distribution value is obtained by summing the normalized results of the resonant frequency variation amplitude and resonant signal intensity attenuation rate of each material layer inside the sling, and then comparing the humidity distribution value with the humidity distribution threshold.

[0020] 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 there is a low humidity gradient area inside the sling.

[0021] In a preferred embodiment, after the humidity gradient analysis module determines the humidity distribution inside the sling, it 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's operating status data, which is the stress distribution of the sling in each area.

[0022] The data acquisition module has a preset monitoring period. During the monitoring period, it records and counts the stress changes in each area of ​​the sling, and obtains the stress distribution of the sling in each area during the monitoring period. The data acquisition module then transmits the stress distribution of the sling in each area to the condition assessment module.

[0023] In a preferred embodiment, the condition assessment module receives the stress distribution of the sling in each region, selects the median of the stress distribution in each region as the screening criterion, screens out regions with stress distribution exceeding the screening criterion as key regions, and uses the ratio of the stress distribution of the key region to the screening criterion as the humidity diffusion coefficient of the corresponding region.

[0024] 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 condition 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 upgrade the humidity risk level of the assessment area. The specific steps are as follows:

[0026] Data standardization involves standardizing the humidity diffusion coefficient and stress mutation ratio of the assessment area to the same dimension.

[0027] Repeated standardization is performed, and n standardization parameters are preset to obtain multiple standardized results of humidity diffusion coefficients or stress mutation ratios;

[0028] Data overlay involves adding the humidity diffusion coefficient (after data standardization using the same standardized parameters) to the stress mutation ratio to obtain multiple overlay values.

[0029] The superposition process involves squaring multiple superimposed values ​​and taking the arithmetic mean as the superposition value.

[0030] Square root processing is used to obtain the risk assessment value by taking the square root of the superimposed processing amount; 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 upgraded.

[0031] 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 upgraded.

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

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

[0034] In a preferred embodiment, the weighted average of the normalized region thickness and region strength of the marked region is taken as the risk adjustment parameter, and the risk weight of the corresponding marked region is calculated using a nonlinear formula. The nonlinear formula is expressed as follows:

[0035] ;

[0036] in, The humidity diffusivity of the marked area. These are preset nonlinear parameters. Risk adjustment for marked areas, Risk weights for the marked regions;

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

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

[0039] The technical effects and advantages of this invention are as follows:

[0040] This invention constructs a system comprising a data acquisition module, a humidity gradient analysis module, a status assessment module, and an early warning processing module. Based on the acquired microwave resonant signal inside the sling, it extracts the resonant frequency offset of each material layer of the sling using a spectrum decomposition algorithm. It then combines this with humidity distribution results obtained from a dielectric property database for feedback, allowing for the setting of monitoring cycles and the collection of sling operation status data. Key areas are screened based on the humidity diffusion coefficient, and a weighted average method is used to identify assessment areas requiring increased risk levels. Combining regional physical properties and the humidity diffusion coefficient, a nonlinear model is used to calculate regional risk weights and set early warning times. This achieves early identification and dynamic graded early warning of latent faults in slings, effectively improving the safety and monitoring accuracy of insulating tools while reducing the risk of sudden failures. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the module framework of a fault warning and monitoring system for an insulating sling in use according to the present invention.

[0042] Figure 2 This is a flowchart illustrating the operation of a fault warning and monitoring system for insulated slings according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1

[0045] This invention provides a detection and monitoring system for fault early warning during the use of insulating slings, the structural block diagram of which is shown below. Figure 1 As shown, it includes a data acquisition module, a humidity gradient analysis module, a status assessment module, and an early warning processing module.

[0046] These modules are connected sequentially via a data transmission link to form a complete closed-loop monitoring and early warning system.

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

[0048] After generating humidity distribution results, the humidity gradient analysis module returns them to the data acquisition module, which is used to set the monitoring cycle and collect operational status data.

[0049] The operational status data is then transmitted 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 by combining the physical properties of the marked area and the humidity diffusion coefficient, and sets the early warning time according to the risk weight.

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

[0054] In practical applications, multiple ring 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 frequency band sensitive to the dielectric properties of the insulating material. The transmission power is ≤10mW to avoid affecting the insulation performance. Different material layers are embedded at uniform intervals of 5-10cm along the length of the sling. The interval value is adjusted according to the rated load of the sling: 5cm when the load is ≤5t, 8cm when the load is 5-10t, and 10cm when the load is >10t. This is used to record the frequency difference between the transmitted microwave signal and the reflected signal, and to statistically analyze the amplitude of the resonant frequency change and the attenuation rate of the resonant signal intensity of each material layer.

[0056] When the ring microwave resonator is working, it records the frequency difference between the transmitted microwave signal and the reflected signal, and identifies the material layer corresponding to the frequency difference.

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

[0058] The data acquisition module transmits the collected resonant frequency variation amplitude and resonant 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, the received resonant frequency variation amplitude and resonant signal intensity attenuation rate are first normalized.

[0060] The specific method of normalization is to merge the amplitude of resonant frequency variation into a frequency dataset, merge the resonant signal intensity attenuation rate into an intensity dataset, and use the ratio of the data in the frequency dataset or intensity dataset to the maximum value in the corresponding dataset as the normalization result.

[0061] It should be noted that there are other normalization methods besides the one mentioned above. These include maximum-minimum normalization, Z-score standard deviation normalization, decimal scaling normalization, and others. The specific normalization method was chosen by the researchers based on the distribution characteristics of the target data and the performance evaluation results of the processed model, and will not be elaborated here.

[0062] Randomly select a data point from the frequency dataset and the intensity dataset as the baseline data, and set the decomposition coefficient K. Select the K data points from each dataset that have the smallest difference from the baseline data as reference data.

[0063] The mean of the reference data in the frequency dataset and 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] In the formula, Humidity distribution threshold, For the first The normalized resonant frequency variation amplitude of each sample point For the first Normalized resonant signal intensity attenuation rate at each sample point This represents the total number of samples within the current statistical period.

[0067] The humidity distribution value is obtained by summing the normalized results of the resonant frequency variation amplitude and resonant 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 formula for calculating the humidity distribution value is as follows:

[0069] ;

[0070] In the formula, This represents the humidity distribution value. For the first The normalized resonant frequency variation amplitude of each sample point For the first Normalized resonant signal intensity attenuation rate at each sample point.

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

[0072] The humidity gradient analysis module returns the humidity distribution results to the data acquisition module, which then sets the monitoring cycle based on the humidity distribution results to collect data on the operating status of the sling.

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

[0074] The operational status data mainly reflects the stress distribution of the sling in various areas. The data acquisition module selects a period of time as the monitoring cycle, records and counts the stress changes in various areas of the sling within the monitoring cycle, thereby obtaining the stress distribution data of the sling in various areas within the monitoring cycle.

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

[0076] The condition assessment module receives stress distribution data of the sling in each region, selects the median stress distribution in each region as the screening criterion, and filters out the regions with stress distribution exceeding the screening criterion as key regions.

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

[0078] To further detect abnormal behavior of the sling, the condition assessment module identifies local stress mutations that occur in the sling during the monitoring period, matches the areas corresponding to the stress mutations with key areas, and retains the successfully matched areas as assessment areas.

[0079] The stress mutation ratio of the assessment area is calculated by the following steps: sum up the number of the same assessment areas corresponding to stress mutation phenomena and divide 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 assessment area, and uses a weighted average method to determine whether to upgrade the humidity risk level of the assessment area.

[0081] It should be noted that the correlation mechanism between stress abrupt changes and humidity risk is as follows: moisture penetration leads to a decrease in interlayer bond strength. When the sling is under load, micro-slippage is likely to occur between layers, manifesting as localized stress abrupt changes. Therefore, a higher proportion of stress abrupt changes indicates more severe structural degradation due to humidity in that area, and the humidity risk level should be assessed first.

[0082] The specific steps are as follows:

[0083] First, the humidity diffusion coefficient and stress mutation ratio of the assessment area are standardized to ensure they have the same dimensions.

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

[0085] It should be explained that the preset n standardized parameters refer to the calculation basis of multiple standardized parameters when performing data standardization processing on the humidity diffusion coefficient and stress mutation ratio of the evaluation area, in order to adapt to different data distribution characteristics. Multi-dimensional standardization reduces the bias of a single method. For example, if n is 3, then maximum and minimum value normalization, Z-score standardization 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, after being standardized using the same standardized parameters, is added to the stress mutation ratio to obtain multiple superimposed values.

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

[0088] If the risk assessment value of the assessed area exceeds the preset risk threshold, the humidity risk level of the assessed area will not be upgraded.

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

[0090] The assessed areas that have undergone humidity risk level upgrade processing are marked as marked areas, and the humidity diffusion coefficient of the marked areas 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 within the corresponding area.

[0092] Tensile strength is a mechanical property index 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 within the marked area as target materials and collects the thickness and tensile strength of each target material.

[0094] Calculate the average thickness of all target materials as the region thickness, and calculate the sum of the tensile strengths of all target materials as the region strength.

[0095] The weighted average of the normalized regional thickness and regional intensity of the marked region is taken as the risk adjustment quantity m, and the risk weight of the corresponding marked region is calculated by nonlinear formula.

[0096] The nonlinear formula is expressed as follows: ;

[0097] in, The humidity diffusivity of the marked area. These are preset nonlinear parameters. Risk adjustment for marked areas, Risk weights for the marked regions.

[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 humidity 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 for the marked area and the risk weight is used as the warning time for the corresponding marked area.

[0100] Through the above steps, the early warning processing module can comprehensively assess the operational 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 ring microwave resonator arranged inside the sling continuously emits microwave signals. The data acquisition module monitors the state 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 based on the humidity distribution results, collects stress distribution data of the sling in each area, and transmits the data to the condition 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 then upgrades the humidity risk level of the assessed area.

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

[0106] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

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

[0108] To ensure the safety of the sling, it is necessary to monitor its internal humidity gradient and stress distribution in real time.

[0109] During a certain industrial equipment hoisting operation, the slings were in a high humidity environment and were subjected to alternating loads for an extended period of time.

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

[0111] First, multiple ring microwave resonators are embedded at uniform intervals inside the sling, and these resonators are 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 a ring microwave resonator, and identifies the material layer corresponding to the frequency difference.

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

[0114] For example, if the resonant frequency of a certain material layer changes by 200kHz and the signal strength attenuation rate is 30%, this data will serve as the basis for subsequent analysis.

[0115] Secondly, after receiving the above data, the humidity gradient analysis module performs normalization processing on it. Specifically, it merges the amplitude of resonant frequency changes into a frequency dataset, merges the resonant signal intensity attenuation rate into an intensity dataset, and calculates the ratio of the data in each dataset to the maximum value in the corresponding dataset to achieve normalization.

[0116] Subsequently, a baseline data point is randomly selected and the decomposition coefficient K=5 is set. Five reference data points with the smallest difference from the baseline data are selected from the frequency dataset and intensity dataset respectively. Their mean values ​​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 there is a high humidity gradient in that area; otherwise, it is a low humidity gradient.

[0118] This process utilizes a spectrum decomposition algorithm to extract the resonant frequency offset of each material layer, and combines it with a dielectric property database to invert the dielectric loss tangent of each material layer, thereby generating humidity distribution results.

[0119] Next, the data acquisition module sets the monitoring cycle based on the humidity distribution results.

[0120] It should be noted that the dielectric property 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 as follows: perform Fourier transform on the microwave resonant signal to obtain the frequency amplitude spectrum, use wavelet thresholding for noise reduction, divide the frequency interval into 50MHz intervals, extract the resonant peaks in each interval, and calculate the difference between the center frequency and the initial frequency of each effective resonant peak, which is the resonant frequency offset of the corresponding material layer. The decomposition coefficient is used to filter reference data to calculate the humidity distribution threshold. The value is positively correlated with the number of material layers in the sling, and the default range is 3-8. For example, if the sling has 3-5 material layers, the value of the decomposition coefficient can be between 3 and 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 statistically analyzes the stress distribution data.

[0122] For example, if the median stress distribution in a certain region is 50 MPa, and the stress distribution in a certain region exceeds this median, then that region is selected as a critical region.

[0123] The ratio of stress distribution in the critical area to the screening criteria is defined as the humidity diffusion coefficient. Furthermore, the condition assessment module also detects whether localized stress abrupt changes occur in the sling during the monitoring period.

[0124] It should be noted that the humidity diffusion coefficient is defined as the ratio of the stress distribution in the critical area to the screening standard. The basis for this is that in the stress concentration area of ​​the sling, the material with stress greater than the median has a higher microscopic porosity. Observation by scanning electron microscopy shows that the porosity in the stress concentration area is 1.5-2 times that of the low-stress area. 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 critical area, the stress mutation rate in that 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, then the stress mutation ratio of that area is 0.4.

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

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

[0129] Subsequently, n=3 standardization parameters were set to obtain multiple standardization results. The humidity diffusion coefficient processed using the same standardization parameters was then added to the stress mutation ratio to obtain multiple superimposed values.

[0130] The arithmetic mean of the squared values ​​is taken as the superposition processing value, and then the square root is 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 that area will be upgraded and it will be marked as a marked area.

[0132] It should be noted that the preset risk threshold is determined by professionals based on historical fault data. For example, at least 50 sets of risk assessment values ​​for sling failure cases are collected. The risk assessment values ​​can be calculated by the algorithm of this system, and the distribution of assessment values ​​at the time of failure is recorded. 80% of the minimum risk assessment value in the failure cases is taken as the initial threshold. The preset risk threshold is then fine-tuned in combination with the need to balance the false alarm rate and the false alarm rate in actual applications.

[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, with thicknesses of 1 mm, 1.2 mm, 1.1 mm, 1.3 mm and 1.4 mm, and tensile strengths of 100 MPa, 120 MPa, 110 MPa, 130 MPa and 140 MPa, respectively.

[0135] The average thickness of all target materials is calculated as the region thickness (1.2 mm), and the sum of tensile strengths is calculated as the region strength (600 MPa).

[0136] The weighted average of the normalized regional thickness and regional intensity is taken as the risk adjustment parameter m, and then a nonlinear formula is used to calculate the risk adjustment parameter m. Calculate the risk weights.

[0137] Assuming the humidity diffusivity of the marked area The preset nonlinear parameter is 0.8. The risk adjustment is 1.5. If the value is 0.9, then the risk weight is... .

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

[0139] For example, if the default warning time is 10 minutes, then the warning time for this marked area is approximately 10 × 1.08 ≈ 11 minutes.

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

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

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

[0143] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0144] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. 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. 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 a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0145] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply 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 this application.

[0146] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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 understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing 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, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0151] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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

Claims

1. A detection and monitoring system for fault early warning during the use of an insulating sling, characterized in that: It includes a data acquisition module, a humidity gradient analysis module, a status assessment module, and an early warning processing module; The data acquisition module is used to acquire microwave resonant signals inside the sling and transmit the acquired signals to the humidity gradient analysis module. After receiving the humidity distribution results returned by the humidity gradient analysis module, the data acquisition module sets the monitoring cycle to collect sling operating status data and transmits it to the status assessment module. The humidity gradient analysis module collects the resonant frequency variation amplitude and resonant signal intensity attenuation rate of multiple sling samples, performs normalization processing, merges the resonant frequency variation amplitude into a frequency dataset, merges the resonant signal intensity attenuation rate into an intensity dataset, and uses the ratio of the data in the frequency dataset or intensity dataset to the maximum value in the corresponding dataset as the normalization result. Randomly select a data point from the frequency dataset and the intensity dataset as the baseline data, set the decomposition coefficient K, select the K data points with the smallest difference from the baseline data in each dataset as reference data, calculate the mean of the reference data in the frequency dataset and the intensity dataset, and sum them to obtain the humidity distribution threshold. The humidity distribution value is obtained by summing the normalized results of the resonant frequency variation amplitude and resonant signal intensity attenuation rate of each material layer inside the sling, and then comparing the humidity distribution value with the humidity distribution threshold. If the humidity distribution value exceeds the humidity distribution threshold, it is determined that there is a high humidity gradient region inside the sling; if the humidity distribution value is lower than the humidity distribution threshold, it is determined that there is a low humidity gradient region inside the sling, and the humidity distribution result is generated and returned to the data acquisition module. The status assessment module is used to receive the operating status data of the sling, filter out key areas based on the operating status data and calculate the humidity diffusion coefficient of the corresponding areas, detect abnormal behavior of the sling and perform secondary screening of key areas to obtain assessment areas, use the weighted average method to determine whether to upgrade the humidity risk level of the assessment area, mark the assessment areas that have been upgraded in humidity risk level, and send the humidity diffusion coefficient of the marked areas to the early warning processing module. The condition assessment module receives the stress distribution of the sling in each area, selects the median stress distribution in each area as the screening criterion, and screens out areas where the stress distribution exceeds the screening criterion as key areas. The ratio of the stress distribution in the key area to the screening criterion is used as the humidity diffusion coefficient of the corresponding area. After receiving the marked area, the early warning processing module obtains the physical attributes of the marked area, calculates the risk weight of the corresponding marked area by combining the physical attributes of the marked area with 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 fault early warning during use of an insulating sling according to claim 1, characterized in that: The microwave resonant signal in the data acquisition module includes the amplitude of the resonant frequency variation of each material layer inside the sling and the attenuation rate of the resonant signal intensity; When the ring microwave resonator inside the sling is working, the frequency difference between the emitted microwave signal 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 statistically analyzed. The resonant signal intensity attenuation rate is obtained by measuring the intensity attenuation of the microwave signal after it passes through the material layer.

3. The detection and monitoring system for fault early warning during use of an insulating sling according to claim 2, characterized in that: After the humidity gradient analysis module determines the humidity distribution inside the sling, it 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 the monitoring cycle to collect the sling's operating status data. The operating status data is the stress distribution of the sling in each area. The data acquisition module has a preset monitoring period. During the monitoring period, it records and counts the stress changes in each area of ​​the sling, and obtains the stress distribution of the sling in each area during the monitoring period. The data acquisition module then transmits the stress distribution of the sling in each area to the condition assessment module.

4. The detection and monitoring system for fault early warning during use of an insulating sling according to claim 3, characterized in that: The condition assessment module receives the stress distribution of the sling in each area, selects the median stress distribution in each area as the screening criterion, and screens out areas where the stress distribution exceeds the screening criterion as key areas. The ratio of the stress distribution in the key area to the screening criterion is used as the humidity diffusion coefficient of the corresponding area. 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.

5. The detection and monitoring system for fault early warning during use of an insulating sling according to claim 4, characterized in that: The condition assessment module comprehensively evaluates the humidity diffusion coefficient and stress abrupt change ratio of the assessment area, and uses a weighted average method to determine whether to upgrade the humidity risk level of the assessment area. The specific steps are as follows: Data standardization involves standardizing the humidity diffusion coefficient and stress mutation ratio of the assessment area to the same dimension. Repeated standardization is performed, and n standardization parameters are preset to obtain multiple standardized results of humidity diffusion coefficients or stress mutation ratios; Data overlay involves adding the humidity diffusion coefficient (after data standardization using the same standardized parameters) to the stress mutation ratio to obtain multiple overlay values. The superposition process involves squaring multiple superimposed values ​​and taking the arithmetic mean as the superposition value. Square root processing is used to obtain the risk assessment value by taking the square root of the superimposed processing amount; 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 upgraded. 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 upgraded.

6. The detection and monitoring system for fault early warning 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 within the marked area.

7. The detection and monitoring system for fault early warning during use of an insulating sling according to claim 6, characterized in that: When calculating the risk weight of the marked area, the early warning processing module randomly selects a number of materials within a preset range as target materials from the materials within the marked area, collects the thickness and tensile strength of each target material, calculates the average thickness of all target materials as the area thickness, and calculates the sum of the tensile strengths of all target materials as the area strength.

8. The detection and monitoring system for fault early warning during use of an insulating sling according to claim 7, characterized in that: The weighted average of the normalized region thickness and region intensity of the marked region is taken as the risk adjustment parameter. The risk weight of the corresponding marked region is then calculated using a nonlinear formula, which is expressed as follows: ； in, The humidity diffusivity of the marked area. These are preset nonlinear parameters. Risk adjustment for marked areas Risk weights for the marked regions; The product of the default warning time for the marked area and the risk weight is used as the warning time for the corresponding marked area.

9. A detection and monitoring system for fault early warning during use of an insulating sling according to claim 8, characterized in that: The data acquisition module acquires microwave resonance signals through a ring microwave resonator arranged inside the sling. The ring microwave resonator is embedded in different material layers of the sling at uniform intervals to record the frequency difference between the transmitted and reflected microwave signals, and to statistically analyze the amplitude of the resonant frequency change and the attenuation rate of the resonant signal intensity of each material layer.

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