Electric fireplace electrical safety performance test system

Abstract

The invention belongs to the technical field of electrical safety detection, and relates to an electrical fireplace electrical safety performance test system. By constructing a multi-parameter acquisition unit, a data correction unit, a performance evolution analysis unit and a failure mode recognition unit, synchronous acquisition and fusion analysis of insulation resistance, leakage current and environment temperature and humidity of the electric fireplace are realized; processing the original data by using an insulation resistance correction model based on temperature and humidity, analyzing and identifying a high-risk coupling state in combination with sliding window correlation, accurately judging an insulation performance failure critical point, and further dividing a degradation stage; and establishing a differential variable correlation model at each stage, generating a performance trend vector, and identifying the current insulation failure mode through the characteristic change of the performance trend vector. The system effectively solves the problem that the traditional test method is difficult to dynamically evaluate the insulating property evolution process and early warning failure risk, and improves the accuracy, the intelligent level and the risk pre-judgment capability of the electrical safety performance test of the electric fireplace.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electrical safety detection, and relates to an electric fireplace electrical safety performance test system. BACKGROUND

[0002] As a common household heating device, electric fireplaces have been widely used in homes and commercial places in recent years. The insulation performance is a core indicator of the electrical safety of electric fireplaces, and is directly related to whether safety hazards such as electric leakage and short circuit will occur during use, thereby affecting the personal safety of users. Therefore, type testing and factory inspection of the insulation performance of electric fireplaces before they are shipped is a key link to ensure that they meet electrical safety standards and guarantee safe operation of the devices.

[0003] During insulation performance detection, changes in environmental temperature and humidity will affect the electrical conductivity characteristics of the surface and interior of the insulation material, resulting in nonlinear drift of the measured insulation resistance value. Especially in non-uniform environments such as high temperature and high humidity, the stability and repeatability of the measurement results are affected. However, existing detection methods mostly directly use the original insulation resistance measurement value or rely on static threshold criteria, without fully considering actual complex working conditions, lacking effective environmental compensation models, and being difficult to distinguish between intrinsic degradation of the material and transient fluctuations caused by the environment, which easily leads to false alarms in harsh conditions or missed early aging in good environments, seriously weakening the reliability of the state evaluation and the electrical safety risk early warning capability.

[0004] Further, the existing technology lacks early warning capability for gradual degradation of insulation performance. The alarm mechanism is essentially passive and lagging, which means that the system cannot identify high-risk precursors in which the absolute values of parameters have not yet exceeded the standard, but significant coordinated abnormalities have occurred between multiple parameters. This insensitivity to gradual changes and parameter coupling relationships causes the system to miss valuable early warning windows, making it difficult for maintenance personnel to implement effective preventive interventions before the critical point of accelerated degradation of insulation performance, and ultimately turning safety protection into after-the-fact remediation. SUMMARY

[0005] In view of this, to solve the problems raised in the background art, an electric fireplace electrical safety performance test system is proposed.

[0006] The purpose of the application can be achieved by the following technical solution: an electric fireplace electrical safety performance test system, comprising: a multi-parameter acquisition unit for real-time detection and acquisition of the insulation resistance value, leakage current signal, voltage signal and environmental temperature and humidity parameters of the electric fireplace to be tested.

[0007] A data correction unit is configured to preprocess the insulation resistance value, establish an insulation resistance correction model based on the environmental temperature and humidity parameters, and standardize and correct the preprocessed insulation resistance value to obtain a corrected insulation resistance value.

[0008] a performance evolution analysis unit configured to determine a high-risk coupling state according to the corrected insulation resistance value, the leakage current value, and the environmental humidity parameter by sliding window correlation analysis, determine an insulation performance failure critical point in the state, and divide insulation performance degradation stages.

[0009] a failure mode identification unit configured to establish a variable correlation model in each stage based on the divided degradation stages to generate a performance trend vector, and identify a current insulation failure mode by calculating a feature change of the performance trend vector.

[0010] Compared with the prior art, the present application has the following advantages: (1) The present application establishes an insulation resistance correction model through a data correction unit, and the system can correct the insulation resistance value according to the environmental temperature and humidity parameters in real time, effectively solving the problem that the test results deviate from the actual use state due to the neglect of environmental temperature and humidity changes in traditional test methods, making the insulation performance evaluation results accurately reflect the actual performance of the electric fireplace in the real use environment, and providing a more reliable basis for safety performance evaluation.

[0011] (2) The present application introduces a multi-parameter correlation calculation method in insulation performance analysis, which can simultaneously evaluate the dynamic coupling relationship between insulation resistance, leakage current, and environmental humidity, determine a high-risk coupling state through sliding window correlation, effectively distinguish between short-term occasional fluctuations and persistent changes caused by degradation trends, avoid misjudgment caused by single indicator fluctuations, and thus improve the reliability of the detection and evaluation results.

[0012] (3) The present application further determines the insulation performance failure critical point in the high-risk coupling state and divides the degradation process into stages, realizing the whole process characterization from normal operation to critical failure. This method not only can more intuitively reflect the evolution law of insulation performance, but also makes the detection process forward-looking, which is helpful to find potential hidden dangers in the early stage, thereby providing a reliable basis for subsequent maintenance and risk management. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 It is a schematic diagram of the connection of the units of the system of the present application.

[0015] Figure 2 It is a flowchart of the establishment of the insulation resistance correction model in the present application.

[0016] Figure 3 Flow chart for determining the insulation performance failure critical point in the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0018] Please refer to Figure 1 As shown in the figure, the present application provides an electric fireplace electrical safety performance test system, comprising: a multi-parameter acquisition unit, a data correction unit, a performance evolution analysis unit, and a failure mode identification unit, and the connection relationship between the units is: the multi-parameter acquisition unit is connected with the data correction unit, the data correction unit is connected with the performance evolution analysis unit, and the performance evolution analysis unit is connected with the failure mode identification unit.

[0019] The multi-parameter acquisition unit is used for real-time detection and acquisition of the insulation resistance value, the leakage current signal, the voltage signal, and the environmental temperature and humidity parameters of the electric fireplace to be tested.

[0020] Further, the collection method of the insulation resistance value, the leakage current value, and the environmental temperature and humidity parameters is: applying a specified amplitude of DC or AC voltage to the electric fireplace to be tested.

[0021] In a specific embodiment, a test voltage of 500V DC or 1250V AC can be selected, which is determined according to electrical safety standards such as IEC60335-1 or GB4706.1, and the test voltage is applied between the electric fireplace power input terminal and the accessible metal shell to simulate the insulation failure path that may occur in actual use.

[0022] The insulation resistance sensor is used to measure the original insulation resistance value of the insulation part of the electric fireplace in real time, and a plurality of groups of sampling data are continuously collected at a preset sampling frequency, and the median value of a plurality of sampling values in a window is calculated as the insulation resistance value corresponding to the window by using a sliding window method.

[0023] In a specific embodiment, the system continuously collects the original insulation resistance value at a preset sampling frequency of 10Hz, and a sliding window with a length of 30 sampling points is used, and the median of all sampling values in the window is taken as the insulation resistance output value at this moment after sorting.

[0024] It should be noted that the insulation resistance value is collected in order to evaluate the insulation performance of the electric fireplace in real time under the applied test voltage, and to determine whether there is a risk of insulation degradation or failure, thereby ensuring electrical safety.

[0025] The preset sampling frequency is determined comprehensively based on the response characteristics of the dynamic changes in the insulation performance of the electric fireplace, the sensor response speed, and the subsequent data processing requirements. Specifically, the insulation resistance usually exhibits a certain polarization response process after the test voltage is applied, and its stabilization time is generally on the order of several seconds to tens of seconds. In order to accurately capture the entire process change of the insulation resistance from the initial transient state to the steady state, the preset sampling frequency is set to the range of 1Hz to 50Hz, preferably 10Hz.

[0026] It should be explained that the original insulation resistance value is susceptible to transient electromagnetic interference and measurement noise. If the instantaneous value is used directly in the analysis, it will introduce significant random errors. The sliding window method divides the continuous sampling data into several time windows and takes the median of multiple sampling values ​​in the window as the representative value, which can effectively suppress impulse noise and avoid the interference of abnormal high and low values ​​on the true insulation state.

[0027] The leakage current value of the electric fireplace power supply circuit is collected in real time using a leakage current sensor.

[0028] In one specific embodiment, the leakage current is transmitted through a high-precision Hall effect current sensor.

[0029] It should be explained that the leakage current value is one of the key parameters for evaluating the electrical safety performance of electric fireplaces, and directly reflects the overall condition of the insulation system.

[0030] By deploying multiple temperature and humidity sensor nodes in the electric fireplace test area, ambient temperature and humidity data are collected synchronously, and the ambient temperature and humidity parameters are obtained by fusing the readings of each node using the spatial averaging method.

[0031] In one specific embodiment, five temperature and humidity sensing nodes are evenly deployed in the test area around the electric fireplace, located at the four corners and the center of the test platform, respectively.

[0032] It should be explained that electric fireplaces may be in non-uniform temperature and humidity environments in actual use. In order to accurately characterize the impact of environmental conditions on insulation performance, multiple temperature and humidity sensing nodes are deployed and data fusion is performed using the spatial averaging method. Multi-point measurement can avoid data distortion caused by improper location of a single node. The environmental temperature and humidity parameters obtained by the fusion algorithm can better reflect the overall condition of the environment in which the electric fireplace is located, providing a reliable input for the temperature and humidity correction of the insulation resistance in the future.

[0033] It should be noted that the leakage current value, insulation resistance value, and temperature and humidity data share the same system clock, achieving microsecond-level time synchronization.

[0034] The data correction unit is used to preprocess the insulation resistance value, establish an insulation resistance correction model based on the ambient temperature and humidity parameters, and standardize the preprocessed insulation resistance value to obtain the corrected insulation resistance value.

[0035] In one specific embodiment, the preprocessing of the insulation resistance value specifically involves: performing outlier detection on the insulation resistance value, using a judgment method based on sliding window statistics or a threshold method based on the 3σ principle to identify and eliminate outliers caused by instantaneous sensor interference, poor contact, or electromagnetic noise.

[0036] Missing values ​​are detected in the time series after outlier removal. If there are data gaps due to communication interruption or sampling loss, they are filled by linear interpolation, neighboring values, or spline interpolation based on the time series trend, depending on the length of the gap. The filled insulation resistance values ​​are then smoothed and filtered to further suppress high-frequency noise and generate continuous and stable pre-processed insulation resistance values, which are used as input for the subsequent temperature and humidity correction model.

[0037] For further details, please refer to Figure 2 As shown, the method for obtaining the insulation resistance correction model is as follows: through calibration experiments, under various temperature and humidity combinations covering typical and extreme environmental conditions of electric fireplaces, a specified test voltage is applied to the same type of electric fireplace to obtain the corresponding insulation resistance measurement value under stable conditions.

[0038] It should be explained that the resistivity of insulating materials is significantly affected by ambient temperature and humidity, and its changes are usually nonlinear. In order to establish a calibration model with broad applicability, the calibration experiment must cover the typical operating conditions and extreme environmental conditions of electric fireplaces, such as high temperature and high humidity, low temperature and low humidity. The purpose is to fully capture the coupling effect of temperature and humidity on insulation resistance and ensure the predictive effectiveness of the model in various real-world scenarios.

[0039] A mapping relationship is constructed with ambient temperature and humidity as inputs and the measured value of insulation resistance under steady state as output.

[0040] It should be noted that, under each temperature and humidity combination, insulation resistance measurements should only be collected after the electric fireplace insulation system has reached thermal and humidity equilibrium. This operation eliminates interference from transient processes and obtains steady-state data that truly reflects the insulation characteristics under those environmental conditions.

[0041] Based on the aforementioned mapping relationship, a multivariate regression method is used to establish an insulation resistance correction model to investigate the influence of ambient temperature and humidity on insulation resistance.

[0042] In one specific embodiment, a calibration experiment was conducted on a certain model of electric fireplace under 25 temperature and humidity combinations, such as temperatures: –5°C, 25°C, 45°C; humidity: 30%, 60%, 90%RH, etc. After obtaining a stable insulation resistance value at 500V DC, a calibration model was established using a bivariate quadratic polynomial regression model, and the R-value was fitted. 2 =0.97.

[0043] Furthermore, the method for obtaining the corrected insulation resistance value is as follows: the synchronously collected ambient temperature and humidity parameters and the corresponding original insulation resistance values ​​are input into the insulation resistance correction model, and the results output by the model are standardized to obtain the corrected insulation resistance value.

[0044] It should be explained that the insulation resistance correction model effectively eliminates the interference of environmental fluctuations on the test results by normalizing the insulation resistance values ​​measured under different environmental conditions to comparable values ​​under standard reference environmental conditions. This allows subsequent performance evolution analysis, critical point judgment, and failure mode identification to be based on state eigenvalues.

[0045] This invention establishes an insulation resistance correction model through a data correction unit. The system can perform standardized correction of the insulation resistance value in real time based on environmental temperature and humidity parameters. This effectively solves the problem that traditional testing methods may lead to deviations between test results and actual usage conditions due to neglecting changes in environmental temperature and humidity. This ensures that the insulation performance evaluation results accurately reflect the actual performance of the electric fireplace in the real usage environment, providing a more reliable basis for safety performance evaluation.

[0046] The performance evolution analysis unit is used to determine the high-risk coupling state based on the corrected insulation resistance value, leakage current value and environmental humidity parameters through sliding window correlation analysis. Under this state, the critical point of insulation performance failure is determined and the insulation performance degradation stage is divided.

[0047] Furthermore, the method for determining the high-risk coupling state is as follows: the corrected insulation resistance value, leakage current value, and environmental humidity parameter are aligned according to a unified timestamp to form a triplet time series.

[0048] Based on the triplet time series, within a sliding window of preset sampling points, the Pearson correlation coefficient between insulation resistance and leakage current is calculated as the first correlation coefficient, the Pearson correlation coefficient between insulation resistance and ambient humidity is calculated as the second correlation coefficient, and the Pearson correlation coefficient between leakage current and ambient humidity is calculated as the third correlation coefficient.

[0049] It should be noted that the preset sampling points refer to the time series data points obtained by the system synchronously collecting insulation resistance, leakage current and ambient humidity according to a preset sampling frequency during the test. The sliding window slides on this time series according to a preset length and step size. The data points in each window are the preset sampling points used to calculate the three sets of Pearson correlation coefficients. For example, if the sampling frequency is 1Hz, then one sampling point is generated per second.

[0050] If, within a preset series of sliding windows, the first correlation coefficient is greater than the first correlation threshold, the second correlation coefficient is greater than the second correlation threshold, and the third correlation coefficient is greater than the third correlation threshold, then it is determined to be a high-risk coupling state.

[0051] It should be explained that the high-risk coupling state refers to a dangerous system state in which the three parameters of ambient humidity, insulation resistance and leakage current change from being relatively independent to being strongly correlated and mutually driving.

[0052] The method utilizes the Pearson correlation coefficient within a sliding window to dynamically monitor the strength of the linear correlation between three parameters: insulation resistance, leakage current, and ambient humidity. The aim is to identify a key precursor state: increased humidity leads to a significant decrease in insulation resistance, which in turn directly causes a sharp increase in leakage current. When these three factors form a cycle of accelerated deterioration and exhibit strong and consistent synergistic changes, it means that environmental factors are effectively driving the accelerated degradation of electrical performance, and the system has entered a high-risk coupling state.

[0053] Furthermore, the method for obtaining the first correlation threshold, the second correlation threshold, and the third correlation threshold is as follows: based on the acquisition of three sets of synchronous monitoring data during the fault-free historical operation period, namely insulation resistance-humidity, leakage current-humidity, and insulation resistance-leakage current.

[0054] It should be noted that the aforementioned fault-free historical operation period refers to the period during which the equipment has not experienced insulation failure, alarm, or abnormal shutdown during factory testing, laboratory aging tests, or normal user use.

[0055] Through sliding window correlation analysis, three sets of correlation coefficient numerical sequences were obtained.

[0056] In one specific embodiment, for each set of variable sequences, the Pearson correlation coefficient is calculated using the sliding window method, with the sliding window length set to M sampling points. For example, M=60, corresponding to 1 minute, and the sliding step size is 1 sampling point. Within each window, the time series of three sets of correlation coefficients are obtained by calculating the Pearson correlation coefficient between the two variables.

[0057] For each set of modeled correlation coefficient numerical sequences, a preset quantile is used as the baseline value through a quantile calculation function.

[0058] It should be noted that the preset quantile is calculated by applying a quantile function, such as the 90th quantile or the 95th quantile, to each set of correlation coefficient sequences to determine its typical upper limit under fault-free conditions. This quantile can be obtained by using an empirical distribution function or statistical software. The quantile is selected to exclude occasional low correlation fluctuations and to preserve the statistical boundary of the strong coupling relationship between variables under normal operating conditions.

[0059] Each baseline value is multiplied by a predefined safety factor k to obtain its respective correlation threshold value, where k is a real number and k>1.

[0060] It should be noted that the safety margin is introduced to prevent misjudgment of high-risk states due to data noise or short-term disturbances. The above quantile benchmark value is multiplied by a safety factor k greater than 1 to obtain the final correlation threshold value. The typical value range of k is 1.1 to 1.5. The specific value can be determined by experimental calibration based on the equipment safety level requirements or historical false alarm rate. The larger the value of k, the stricter the conditions for the system to judge high-risk coupling states, the lower the warning sensitivity but the higher the reliability.

[0061] This invention introduces a multi-parameter correlation calculation method into insulation performance analysis, which can simultaneously evaluate the dynamic coupling relationship between insulation resistance, leakage current and ambient humidity. By using a sliding window correlation to determine high-risk coupling states, it can effectively distinguish between short-term occasional fluctuations and continuous changes caused by degradation trends, avoiding misjudgments caused by fluctuations in a single indicator, thereby improving the reliability of detection and evaluation results.

[0062] For further details, please refer to Figure 3 As shown, the method for determining the critical point of insulation performance failure is as follows: within the time interval corresponding to the high-risk coupling state, it is determined whether the following critical conditions are met simultaneously: (a) the corrected insulation resistance value is lower than the insulation safety limit set according to the electrical safety specification within N consecutive sampling periods, where N≥3.

[0063] It should be explained that this condition is used to eliminate accidental low resistance values ​​caused by transient interference, ensuring that insulation degradation is continuous.

[0064] (b) The average value of the leakage current in the sliding window exceeds the upper limit of the fluctuation determined based on the statistical process control limits of historical normal operating data, and its growth rate relative to the average value of the previous time window exceeds the preset growth rate threshold.

[0065] It should be noted that the fluctuation upper limit value determined in the process of statistical process control limits based on historical normal operating condition data adopts the fluctuation upper limit value determined by the 3σ principle.

[0066] The preset growth rate threshold refers to the upper limit of the growth rate of the average value of the leakage current sliding window between two adjacent time windows. It is used to determine whether the leakage current has an abnormally accelerated rise. It is calibrated according to the type of electric fireplace, rated voltage and historical normal operating data, and the typical value range is 0.2mA / s to 1.0mA / s.

[0067] It should be explained that this condition is used to capture the abnormally accelerated upward trend of leakage current, reflecting the rapid expansion of internal defects in the insulating medium.

[0068] (c) The ambient humidity remains above the preset high humidity threshold for a period of time that exceeds the preset minimum duration requirement.

[0069] It should be noted that the preset high humidity threshold is a humidity value threshold used to determine whether the ambient humidity is under high humidity stress. It is set according to the typical use environment of electric fireplaces and the moisture resistance performance of insulation materials, with a typical value of 80% to 90%RH. The preset minimum duration requirement is used to eliminate instantaneous humidity interference, with a typical value of 30 minutes to 2 hours, determined by calibration through historical fault data or accelerated aging tests.

[0070] It should be explained that this condition is used to confirm the long-term stress effect of high humidity environment on insulation performance, excluding the influence of short-term humidity fluctuations.

[0071] If conditions (a), (b), and (c) above are met simultaneously for the first time, then the corresponding moment will be determined as the critical point of insulation performance failure.

[0072] It should be noted that the critical point of insulation performance failure is not only the endpoint of the performance evolution analysis unit's comprehensive judgment on the entire process of insulation performance from normal stability and slow degradation to accelerated deterioration, but also the starting point for the failure mode identification unit to start phased modeling and failure mode identification, providing key time nodes for subsequent generation of performance trend vectors and identification of insulation failure types.

[0073] The critical point is only detected after a high-risk coupling state is identified because a high-risk coupling state indicates a continuous and significant abnormal correlation between the corrected insulation resistance, leakage current, and ambient humidity, reflecting a qualitative change in the overall stability of the insulation system. Detecting the critical point at this time can effectively avoid misjudgments under normal operating conditions or random fluctuations, improve the accuracy of early warning, and optimize the allocation of computing resources.

[0074] It should be explained that the method for determining the critical point of insulation performance failure is based on a multi-parameter coupling degradation criterion: within the time interval of the high-risk coupling state identified by the system, it comprehensively judges whether the corrected insulation resistance continues to be lower than the safety limit, whether the leakage current rises abnormally, and whether the environmental humidity is under high humidity stress for a long time. When the above three conditions are met simultaneously for the first time, it is determined that the moment is the critical point at which the insulation performance is about to fail, thereby achieving accurate early warning of the risk of insulation failure.

[0075] Furthermore, the process of dividing the insulation performance degradation stage involves: constructing a synchronous time-series curve of the corrected insulation resistance and leakage current; and based on the curve and the critical point of insulation performance failure, taking the moment when the rate of change of insulation resistance or leakage current is first detected to exceed the preset normal fluctuation range as the first dividing point.

[0076] It should be noted that the preset normal fluctuation range is based on the insulation resistance and leakage current data collected during the fault-free operation of the electric fireplace. The normal fluctuation range is determined by calculating its rate of change, such as the first-order difference or the slope of the sliding window, and using the 3σ principle or the quantile method. It is used to characterize the parameter stability under healthy conditions.

[0077] The second dividing point is the time point at which the insulation resistance first shows a monotonically decreasing trend, the leakage current shows a monotonically increasing trend, and the absolute value of their rate of change reaches the accelerated degradation trigger threshold; the third dividing point is the time point at which the insulation performance fails critically.

[0078] It should be noted that the accelerated degradation trigger threshold is used to determine whether the insulation performance has entered the accelerated deterioration stage. The absolute value of the rate of change is significantly greater than the boundary of the normal fluctuation range. Typical values ​​can be calibrated through accelerated aging tests or historical failure data analysis. For example, the insulation resistance change rate threshold is set to 0.05 MΩ / s, and the leakage current change rate threshold is set to 0.1 mA / s.

[0079] It should be explained that the first dividing point is based on the first time the parameter change rate breaks through the normal fluctuation range, identifying the starting moment when the insulation system deviates from the steady state; the second dividing point is marked by detecting the monotonically decreasing insulation resistance and the monotonically increasing leakage current with the change rate exceeding the accelerated degradation change rate threshold, indicating that irreversible degradation has begun to accelerate; the third dividing point is the critical moment of insulation failure that meets multiple critical conditions under high-risk coupling conditions.

[0080] The three elements sequentially depict the critical turning points of insulation degradation, from the initial appearance of anomalies to accelerated deterioration and then to near-failure, providing precise time-series anchors for phased assessments.

[0081] The four stages of the insulation performance degradation process are defined in chronological order as follows: the time period before the first dividing point is defined as the normal stable stage.

[0082] The time period between the first and second dividing points is defined as the slow degradation phase.

[0083] The time period between the second and third dividing points is defined as the accelerated degradation phase.

[0084] The time period following the third dividing point is defined as the critical failure stage.

[0085] It should be explained that the entire insulation performance degradation process is divided into four consecutive stages, among which the normal and stable stage: from the start of monitoring to before the first dividing point, the insulation parameters fluctuate within the normal range and there are no significant signs of deterioration.

[0086] Slow degradation stage: Between the first and second dividing points, the parameters begin to deviate from the steady state, but the changes are gradual, which is a reversible change caused by early aging or environmental disturbances.

[0087] Accelerated deterioration stage: Between the second and third dividing points, the insulation resistance drops rapidly and the leakage current rises sharply, indicating that internal defects are expanding and the deterioration is irreversible.

[0088] Critical failure stage: After the third dividing point, the system has been determined to be in a high-risk critical state, and the insulation performance is on the verge of failure, requiring immediate warning or shutdown.

[0089] This invention further determines the critical point of insulation performance failure under high-risk coupling conditions and divides the degradation process into stages, realizing the full-process characterization from normal operation to critical failure. This method can not only reflect the evolution law of insulation performance more intuitively, but also make the detection process forward-looking, which helps to discover potential hidden dangers in the early stage, thereby providing a reliable basis for subsequent maintenance and risk management.

[0090] The failure mode identification unit is used to establish a variable correlation model within each stage based on the divided degradation stages to generate a performance trend vector, and to identify the current insulation failure mode by calculating the feature changes of the performance trend vector.

[0091] Furthermore, the method for obtaining the performance trend vector is as follows: for each degradation stage, a corresponding variable association model is established.

[0092] It should be explained that the variable association model refers to the model used to describe the mathematical or statistical relationship between the corrected insulation resistance, leakage current and ambient humidity. At different degradation stages, due to different physical mechanisms, linear models such as multiple regression or nonlinear dynamic models such as LSTM are used for adaptive modeling to accurately capture the coupling characteristics between variables.

[0093] During the normal stable phase and the slow degradation phase, a linear multiple regression model is used to describe the coupling relationship between insulation resistance, leakage current and ambient humidity.

[0094] In the accelerated degradation stage and the critical failure stage, a nonlinear dynamic model is used to describe the coupling relationship between insulation resistance and leakage current.

[0095] Based on the variable association model corresponding to each current degradation stage, the model feature parameters are extracted.

[0096] It should be noted that the model feature parameters refer to the parameters obtained from training or fitting the variable association model at each stage, such as regression coefficients, network weights, and nonlinear function parameters, as the model feature parameters for that stage.

[0097] Based on the corrected insulation resistance, leakage current and ambient humidity three synchronous time-series curves, the rate of change sequence of each curve is obtained by calculating the numerical difference between consecutive sampling points.

[0098] Based on the first, second, and third correlation coefficients obtained from the sampling points, the absolute time difference between each sampling point and the critical point of insulation performance failure is calculated as a temporal location feature.

[0099] The rate of change sequence, three sets of correlation coefficient values, and time series location feature values ​​are fused into a multidimensional feature vector by using a vector concatenation method.

[0100] The extracted model feature parameters and multidimensional feature vectors are normalized and weighted and fused to generate a performance trend vector.

[0101] It should be explained that the performance trend vector is a high-dimensional numerical vector that integrates information such as the model structure characteristics of the current degradation stage, the dynamic rate of parameter change, multivariate correlation, and the time position from the failure critical point. It is used to quantitatively characterize the current evolution state and failure risk trend of the insulation system.

[0102] In one specific embodiment, the extracted model feature parameters and multidimensional feature vectors are normalized, such as by Min-Max normalization or Z-score standardization, and different weights are assigned according to the contribution of each feature to failure discrimination. The weights can be set by learning from historical data or by expert experience. The final performance trend vector is generated by weighted fusion. This vector represents the evolution trend and coupling characteristics of the current insulation state under a specific degradation stage, providing highly discriminative input features for subsequent failure mode identification.

[0103] Furthermore, the step of identifying the current insulation failure mode by calculating the feature changes of the performance trend vector is as follows: extracting the performance trend vectors of multiple consecutive monitoring times, calculating the feature changes between adjacent performance trend vectors, wherein the feature changes include the rate of change of the vector magnitude, the angle change of the vector direction, and the difference sequence of each dimension component.

[0104] In one specific embodiment, the Euclidean norm of the performance trend vector at the current moment and the performance trend vector at the previous moment are calculated respectively, and the difference between the two is divided by the sampling time interval to obtain the rate of change of the vector magnitude.

[0105] The change in the angle between the current performance trend vector and the previous performance trend vector is obtained by taking the dot product of their Euclidean norms, dividing by the product of their Euclidean norms, and then taking the inverse cosine.

[0106] Subtract each dimension of the performance trend vector at the current moment from the corresponding dimension of the previous moment to obtain the difference sequence of each dimension.

[0107] The feature change is input into a pre-trained insulation failure classification model, which is trained using feature change samples corresponding to different failure modes in historical data.

[0108] It should be noted that the pre-trained insulation failure classification model uses SVM, random forest or neural network, and the model is trained using labeled feature change data from historical failure cases.

[0109] Based on the output of the classification model, the current insulation failure mode is identified.

[0110] It should be added that the identification of the current insulation failure mode is as follows: when the characteristic change amount increases monotonically and the vector direction remains unchanged, it is determined to be a uniform degradation mode.

[0111] When the cumulative increase in the magnitude of the performance trend vector of the characteristic change exceeds 80% to 150% within a local time period, and the value of its humidity dimension component continuously exceeds the upper limit of the historical normal fluctuation range, it is determined to be a wet-induced local breakdown mode.

[0112] When more than 70% of the dimensional components of the characteristic change exceed the danger threshold simultaneously, or when the vector direction angle changes by more than 45° to 60° in adjacent cycles, or when the modulus change rate is consistently higher than the danger threshold of 50% / minute, it is determined to be an overall insulation failure mode.

[0113] It should be explained that this invention dynamically identifies high-risk coupling states that characterize accelerated insulation degradation by collecting and correcting insulation resistance, leakage current, and ambient temperature and humidity data. This allows for the determination of critical points for insulation performance failure and the division of performance degradation stages. Furthermore, by constructing performance trend vectors and analyzing their evolution characteristics, the invention achieves intelligent identification of insulation failure modes. This system realizes intelligent safety assessment throughout the entire process, from condition monitoring and trend warning to mode diagnosis.

[0114] 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, in the form of a computer program product.

[0115] Those skilled in the art will recognize that the algorithmic 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 implementations should not be considered beyond the scope of this application.

[0116] 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.

[0117] 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.

[0118] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A system for testing the electrical safety performance of an electric fireplace, characterized in that, include: The multi-parameter acquisition unit is used to detect and acquire the insulation resistance value, leakage current value and ambient temperature and humidity parameters of the electric fireplace under test in real time under the application of constant voltage. The data correction unit is used to preprocess the insulation resistance value, establish an insulation resistance correction model based on the ambient temperature and humidity parameters, and standardize the preprocessed insulation resistance value to obtain the corrected insulation resistance value. The performance evolution analysis unit is used to determine the high-risk coupling state based on the corrected insulation resistance value, leakage current value and environmental humidity parameters through sliding window correlation analysis. Under this state, the critical point of insulation performance failure is determined and the insulation performance degradation stage is divided. The failure mode identification unit is used to establish a variable correlation model within each stage based on the divided degradation stages to generate a performance trend vector, and to identify the current insulation failure mode by calculating the feature changes of the performance trend vector.

2. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The methods for collecting the insulation resistance value, leakage current value, and ambient temperature and humidity parameters are as follows: Apply a DC or AC voltage of a specified amplitude to the electric fireplace under test; The original insulation resistance value of the insulation part of the electric fireplace is measured in real time by an insulation resistance sensor, and multiple sets of sampling data are continuously collected at a preset sampling frequency. The median value of multiple sampling values ​​in the window is calculated as the insulation resistance value corresponding to the window using the sliding window method. The leakage current value of the electric fireplace power supply circuit is collected in real time by a leakage current sensor; By deploying multiple temperature and humidity sensor nodes in the electric fireplace test area, ambient temperature and humidity data are collected synchronously, and the ambient temperature and humidity parameters are obtained by fusing the readings of each node using the spatial averaging method.

3. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The method for obtaining the insulation resistance correction model is as follows: Through calibration experiments, under various temperature and humidity combinations covering typical and extreme environmental conditions of electric fireplaces, a specified test voltage was applied to the same type of electric fireplace to obtain the corresponding insulation resistance measurement value under stable conditions. Construct a mapping relationship with ambient temperature and humidity as inputs and insulation resistance measurement values ​​under steady-state conditions as outputs; Based on the aforementioned mapping relationship, a multivariate regression method is used to establish an insulation resistance correction model to investigate the influence of ambient temperature and humidity on insulation resistance.

4. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The method for obtaining the corrected insulation resistance value is as follows: The synchronously collected ambient temperature and humidity parameters and the corresponding original insulation resistance values ​​are input into the insulation resistance correction model. The results output by the model are standardized to obtain the corrected insulation resistance value.

5. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The method for determining the high-risk coupling state is as follows: The corrected insulation resistance value, leakage current value and environmental humidity parameter are aligned according to a unified timestamp to form a triplet time series. Based on the triplet time series, within the sliding window of the preset sampling points, the Pearson correlation coefficient between insulation resistance and leakage current is calculated as the first correlation coefficient, the Pearson correlation coefficient between insulation resistance and ambient humidity is calculated as the second correlation coefficient, and the Pearson correlation coefficient between leakage current and ambient humidity is calculated as the third correlation coefficient. If, within a preset series of sliding windows, the first correlation coefficient is greater than the first correlation threshold, the second correlation coefficient is greater than the second correlation threshold, and the third correlation coefficient is greater than the third correlation threshold, then it is determined to be a high-risk coupling state.

6. The electric fireplace electrical safety performance testing system as described in claim 5, characterized in that, The first correlation threshold, the second correlation threshold, and the third correlation threshold include: Based on three sets of synchronous monitoring data obtained during the fault-free historical operation period: insulation resistance-humidity, leakage current-humidity, and insulation resistance-leakage current; Through sliding window correlation analysis, three sets of correlation coefficient numerical sequences were obtained. For each set of modeling correlation coefficient numerical sequences, a preset quantile is taken as the baseline value using the quantile calculation function; Each baseline value is multiplied by a predefined safety factor k to obtain its respective correlation threshold value, where k is a real number and k>1.

7. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The method for determining the critical point of insulation performance failure is as follows: Within the time interval corresponding to the high-risk coupling state, determine whether the following critical conditions are met simultaneously: (a) The corrected insulation resistance value is lower than the insulation safety limit set according to the electrical safety code for N consecutive sampling periods, where N≥3; (b) The average value of the leakage current in the sliding window exceeds the upper limit of the fluctuation determined based on the statistical process control limits of historical normal operating data, and its growth rate relative to the average value of the previous time window exceeds the preset growth rate threshold. (c) The ambient humidity remains above the preset high humidity threshold for a period of time that exceeds the preset minimum duration requirement; If conditions (a), (b), and (c) above are met simultaneously for the first time, then the corresponding moment will be determined as the critical point of insulation performance failure.

8. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The content of dividing the insulation performance degradation stages is as follows: Construct synchronous time-series curves of corrected insulation resistance and leakage current. Based on the curves and the critical point of insulation performance failure, the moment when the rate of change of insulation resistance or leakage current is first detected to exceed the preset normal fluctuation range is taken as the first dividing point. The second dividing point is the time point at which the insulation resistance first shows a monotonically decreasing trend and the leakage current shows a monotonically increasing trend, and the absolute value of their rate of change reaches the accelerated degradation trigger threshold; the third dividing point is the time point at which the insulation performance fails critically. The four stages of insulation degradation are defined in chronological order as follows: The time period before the first dividing point is defined as the normal stable phase; The time period between the first and second dividing points is defined as the slow degradation phase; The time period between the second and third dividing points is defined as the accelerated degradation phase. The time period following the third dividing point is defined as the critical failure stage.

9. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The method for obtaining the performance trend vector is as follows: For each stage of degradation, a corresponding variable association model is established: During the normal stable phase and the slow degradation phase, a linear multiple regression model is used to describe the coupling relationship between insulation resistance, leakage current and ambient humidity. In the accelerated degradation stage and the critical failure stage, a nonlinear dynamic model is used to describe the coupling relationship between insulation resistance and leakage current. Based on the variable association model corresponding to each current degradation stage, extract the model feature parameters; Based on the corrected insulation resistance, leakage current and ambient humidity three synchronous time-series curves, the rate of change sequence of each curve is obtained by calculating the numerical difference between consecutive sampling points; Based on the first correlation coefficient, second correlation coefficient and third correlation coefficient obtained from the sampling points, the absolute time difference between each sampling point and the critical point of insulation performance failure is calculated as the temporal location feature; The rate of change sequence, three sets of correlation coefficient values, and time series location feature values ​​are fused into a multi-dimensional feature vector by vector concatenation method. The extracted model feature parameters and multidimensional feature vectors are normalized and weighted and fused to generate a performance trend vector.

10. The electric fireplace electrical safety performance testing system as described in claim 1, characterized in that, The content of identifying the current insulation failure mode by calculating the feature changes of the performance trend vector is as follows: Extract performance trend vectors from multiple consecutive monitoring times, and calculate the feature changes between performance trend vectors at adjacent times. The feature changes include the rate of change of vector magnitude, the angle change of vector direction, and the difference sequence of each dimension component. The feature change is input into a pre-trained insulation failure classification model, which is trained using feature change samples corresponding to different failure modes in historical data. Based on the output of the classification model, the current insulation failure mode is identified.

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