Insulation performance detection and fault early warning method, system, equipment and medium for heat frame

Abstract

This invention discloses a method, system, equipment, and medium for detecting the insulation performance and providing fault early warning for heating racks. The method involves collecting leakage current signals during the heating rack's operation; acquiring ambient temperature and humidity data; compensating the leakage current signals to generate an equivalent insulation resistance value under standard conditions; performing spectral analysis on the leakage current signals to extract harmonic component characteristics and generate an insulation nonlinear degradation index; calculating a health index based on the equivalent insulation resistance value and the insulation nonlinear degradation index; predicting the remaining insulation life based on historical health index data; determining abnormal insulation performance states based on the health index and remaining insulation life; and generating fault early warning information based on the abnormality level and sending it to the user terminal. This application integrates multi-physics compensation, harmonic characteristic analysis, and fault prediction and health management technologies to achieve online monitoring, degradation trend assessment, and remaining life prediction of heating rack insulation performance.

Description

Technical Field

[0001] This invention belongs to the field of electrical safety testing technology, and specifically discloses a method, system, equipment and medium for testing the insulation performance and fault early warning of heating racks. Background Technology

[0002] With the increasing popularity of small household appliances such as high-speed hair dryers and the continuous improvement of power density, the reliability of the insulation performance of heating elements, as core heating components, is directly related to user safety. During long-term use, heating elements experience high-temperature aging, erosion from humid environments, and frequent temperature cycling, leading to gradual deterioration of the insulation material. This can eventually result in insulation breakdown, leakage, or even electric shock. Therefore, accurate testing and timely warning of the insulation performance of heating elements are of great importance.

[0003] Currently, the insulation performance of heating frames is mainly tested using the following methods:

[0004] The first method is offline withstand voltage testing during the production stage. High voltage is applied to the heating element before it leaves the factory to test for breakdown. While this method can screen out products with obvious insulation defects, it is a one-time test and cannot reflect the insulation degradation process during use. Because the heating element is subjected to continuous thermal stress and environmental corrosion in actual use, its good insulation condition at the time of manufacture cannot guarantee long-term safety.

[0005] The second method is overcurrent protection or leakage protection during operation. This involves installing a residual current device (RCD) in the circuit to cut off the power supply when the leakage current exceeds a preset threshold. While this method provides passive protection when insulation failure is severe, it has significant shortcomings: firstly, the protection threshold is usually set too high to detect early signs of insulation degradation; secondly, this method is reactive and cannot provide early warnings to users before a fault occurs. Users often only realize the insulation problem after the equipment trips, at which point the safety hazard has already been identified.

[0006] The third method is periodic manual inspection. Maintenance personnel use an insulation resistance tester to perform offline measurements on the heating element. This method is not only inefficient and difficult to implement continuous monitoring, but the test results are also easily affected by the ambient temperature and humidity during the test, leading to misjudgments or omissions. Especially in small household appliances such as high-speed hair dryers, users do not have the conditions for regular professional inspections, and the process of insulation degradation is often overlooked.

[0007] In summary, existing technologies for testing the insulation performance of heating racks have the following areas for improvement: First, existing solutions primarily focus on passive protection after a fault occurs, and no publicly available technical means for online monitoring and proactive early warning of insulation performance degradation trends have been found. Second, offline testing methods are insufficient to cover the real-time status assessment needs of heating racks under complex operating conditions such as high temperature and high humidity. Third, existing single-dimensional testing and single-threshold judgment methods do not address the compensation for the impact of ambient temperature and humidity on leakage current, nor do they disclose technical solutions for identifying early microscopic degradation of insulation through harmonic characteristics. Fourth, existing technologies lack quantitative assessment methods for the remaining life of insulation, making it difficult to provide predictive maintenance recommendations.

[0008] Therefore, it is necessary to invent methods, systems, equipment, and media for testing the insulation performance and providing early warning of faults in heating racks to solve the above problems. Summary of the Invention

[0009] To overcome the aforementioned deficiencies in the prior art, this invention provides a method, system, equipment, and medium for detecting and warning the insulation performance of heating racks. By integrating temperature and humidity compensation calibration of macroscopic insulation resistance with harmonic characteristic identification of microscopic insulation aging, and combining fault prediction and health management technologies, it achieves online monitoring of the entire life cycle of heating rack insulation performance, early degradation identification, remaining life prediction, and graded proactive early warning, effectively solving the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting the insulation performance and providing fault early warning of a heating frame, specifically including the following steps:

[0011] S1. In response to the detection start command of the target device, synchronously collect the leakage current signal of the heating rack in the target device during operation, as well as the ambient temperature data and ambient humidity data during the operation of the heating rack, and the time synchronization deviation of the multi-source data is ≤10ms.

[0012] S2. Based on the ambient temperature data and the ambient humidity data, the leakage current signal is compensated. Through the pre-established temperature and humidity compensation model, the measured leakage current is converted into the equivalent insulation resistance value under the standard environment of 25℃ and 50% relative humidity.

[0013] S3. Perform spectrum analysis on the leakage current signal, extract the harmonic component characteristics of the fundamental amplitude, third harmonic amplitude, and fifth harmonic amplitude in the leakage current signal, and generate an insulation nonlinear degradation index based on the weighted summation of the ratio of the harmonic amplitude to the fundamental amplitude according to the harmonic component characteristics.

[0014] S4. Based on the equivalent insulation resistance value and the insulation nonlinear degradation index, calculate the current health index of the heating frame by weighted fusion of resistance retention rate and harmonic deviation. Combine the historical health index data of the heating frame and predict the remaining insulation life of the heating frame based on the insulation aging index degradation model.

[0015] S5. Based on the health index and the remaining insulation life, determine whether the heating frame is in an abnormal insulation performance state based on the preset two-level health threshold and life threshold; if the heating frame is in an abnormal insulation performance state, generate corresponding fault warning information according to the abnormality level, and send the fault warning information to the user terminal.

[0016] A heating frame insulation performance testing and fault early warning system, the system comprising: a data acquisition module, a compensation module, an analysis module, a calculation module, and an early warning module; wherein,

[0017] The acquisition module is used to respond to the detection start command of the target device, acquire the leakage current signal of the heating rack in the target device during operation, as well as the ambient temperature and humidity data during the operation of the heating rack, and transmit the synchronously acquired leakage current signal and temperature and humidity data to the compensation module and the analysis module respectively.

[0018] The compensation module is used to receive temperature and humidity data and leakage current fundamental amplitude value transmitted by the acquisition module, convert the measured leakage current into equivalent insulation resistance value under standard environment through the pre-stored temperature and humidity compensation model, and transmit the equivalent insulation resistance value to the calculation module.

[0019] The analysis module is used to receive the leakage current signal transmitted by the acquisition module, perform spectrum analysis on the leakage current signal, extract the harmonic component characteristics of the fundamental amplitude, third harmonic amplitude, and fifth harmonic amplitude in the leakage current signal, generate an insulation nonlinear degradation index based on the weighted summation of the ratio of the harmonic amplitude to the fundamental amplitude according to the harmonic component characteristics, and transmit the insulation nonlinear degradation index to the calculation module.

[0020] The calculation module is used to receive the equivalent insulation resistance value of the compensation module and the insulation nonlinear degradation index of the analysis module. It calculates the current health index of the heating frame by weighted fusion of resistance retention rate and harmonic deviation. Combining the historical health index data of the heating frame, it predicts the remaining insulation life of the heating frame based on the insulation aging index degradation model, and transmits the health index and remaining insulation life to the early warning module.

[0021] The early warning module is used to receive the health index and remaining insulation life from the calculation module, and determine whether the heating frame is in an abnormal insulation performance state based on the preset two-level health threshold and life threshold; if the heating frame is in an abnormal insulation performance state, the module generates corresponding fault early warning information according to the abnormality level and sends the fault early warning information to the user terminal.

[0022] An electronic device includes a processor, a memory, a user interface, a network interface, a signal acquisition interface, a peripheral control interface, and a power management unit. Each unit achieves bidirectional data interaction through an AHB system bus. The memory is used to store instructions, and the user interface and network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to enable the electronic device to execute a computer program such as any of the above-described methods for detecting the insulation performance of a heating frame and providing fault warning.

[0023] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as any of the above-described methods for detecting the insulation performance of a heating frame and providing early warning of faults.

[0024] The technical effects and advantages of this invention are as follows:

[0025] 1. Upgraded from passive protection to active early warning throughout the entire life cycle: This changes the existing protection mode of passively cutting off power only after insulation breakdown. By collecting leakage current signals online and combining temperature and humidity compensation with harmonic characteristic analysis, the insulation performance is evaluated in real time and the remaining life is predicted. It can issue graded early warnings when insulation deteriorates in the early stage, so that users can take maintenance measures in time before the fault occurs, effectively avoiding electric shock accidents and significantly improving the safety of use.

[0026] 2. A dual-dimensional integrated assessment addresses the core deficiency of existing technologies that rely on a single detection dimension: traditional detection methods focus only on the total effective value of leakage current or a single insulation resistance, making them susceptible to environmental temperature and humidity interference and unable to identify early micro-deterioration. This invention establishes a temperature and humidity compensation model, converting the measured leakage current into an equivalent insulation resistance value under standard conditions, thus eliminating the influence of environmental factors. Furthermore, it extracts fundamental and harmonic characteristics through spectral analysis to generate an insulation nonlinear degradation index, effectively reflecting the micro-discharge state and nonlinear degradation process of the insulation material. This integrated application ensures both the environmental robustness of the detection results and the accurate identification of early insulation degradation, significantly improving the accuracy and reliability of the assessment results.

[0027] 3. For the first time, fault prediction and health management (PHM) technology has been fully applied to the field of heating rack insulation testing: By constructing a health index degradation time series, fitting the exponential degradation curve, and calculating the instantaneous degradation rate, quantitative prediction of the remaining insulation life has been achieved. This technical solution provides users with a clear maintenance time window, facilitating reasonable scheduling of maintenance plans and avoiding safety risks and equipment downtime losses caused by sudden failures;

[0028] 4. Tiered Early Warning and Differentiated Response Mechanism, Balancing Safety and User Experience: Based on a comprehensive assessment of health index and remaining lifespan, a two-tiered abnormal state judgment mechanism is implemented. A "Attention" level warning is issued for mild degradation, prompting the user to perform drying or maintenance without affecting normal equipment use. A "Danger" level warning is issued for severe degradation, automatically cutting off power to prevent accidents. This tiered early warning system avoids the false alarms and missed alarms associated with single-threshold judgments and allows for differentiated responses based on the severity of the abnormality, ensuring both safety and equipment efficiency. Attached Figure Description

[0029] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0030] Figure 1 This is a flowchart of the overall method for testing the insulation performance of heating frame and for fault early warning.

[0031] Figure 2 This is a flowchart illustrating the hierarchical early warning and differentiated response decision-making process of this invention.

[0032] Figure 3 This is a module architecture diagram of a heating frame insulation performance testing and fault early warning system. Detailed Implementation

[0033] 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.

[0034] This invention provides, for example Figure 1 The method for testing the insulation performance and providing early warning of faults of the heating frame is shown.

[0035] Furthermore, in the above technical solution, the method for detecting the insulation performance of the heating frame and providing fault early warning includes:

[0036] S1. In response to the detection start command of the target device, synchronously collect the leakage current signal of the heating rack in the target device during operation, as well as the ambient temperature data and ambient humidity data during the operation of the heating rack, and the time synchronization deviation of the multi-source data is ≤10ms.

[0037] Furthermore, in the above technical solution, the step of compensating the leakage current signal based on the ambient temperature data and the ambient humidity data to generate an equivalent insulation resistance value under standard conditions includes:

[0038] A pre-established temperature and humidity compensation model is obtained, which includes the standard leakage current reference value of a qualified healthy heating rack under different temperature and humidity combinations. The qualified healthy heating rack is a sample of heating racks from the same batch whose insulation resistance value is within ±5% of the factory nominal value and whose insulation nonlinear degradation index is confirmed to be lower than the preset reference threshold of 0.05 by spectrum analysis. The standard leakage current reference value is the average value of the fundamental amplitude of the leakage current of no less than 10 qualified healthy heating racks under the corresponding temperature and humidity conditions.

[0039] The collected ambient temperature and humidity data are input into the temperature and humidity compensation model to match the corresponding standard leakage current reference value; if the collected temperature and humidity conditions exceed the calibration range, the corresponding standard leakage current reference value is calculated through a pre-fitted multivariate nonlinear interpolation formula.

[0040] Based on the standard leakage current reference value and the fundamental amplitude of the leakage current signal, a compensation coefficient is calculated, and the measured leakage current is converted into an equivalent insulation resistance value under a standard environment of 25°C and 50% relative humidity using the compensation coefficient.

[0041] It should be further explained that when the target device (such as a high-speed hair dryer) is powered on and starts running, the system responds to the detection start command and initiates the insulation performance testing process. The detection start command trigger has two modes, which can be switched via program configuration:

[0042] Automatic trigger mode: After the target device is powered on and the heating rack enters the rated voltage steady state operation, the detection command is automatically triggered; during operation, continuous data acquisition is triggered once every preset period (preferably 10 minutes) to achieve online monitoring throughout the entire life cycle.

[0043] Manual trigger mode: Receives manual detection commands issued by users through user terminals (mobile APP, device buttons) and immediately starts data collection.

[0044] Leakage current signal acquisition implementation details

[0045] Data acquisition hardware: A high-precision micro-current transformer based on the fluxgate principle is used, paired with a precision sampling resistor. The resolution is ≥1μA and the bandwidth covers 0-10kHz. It is connected in series to the live wire of the independent power supply circuit of the heating frame to eliminate leakage current interference from other components such as motors and control boards.

[0046] Signal preprocessing: The acquired analog leakage current signal first passes through a preamplifier (adjustable gain, preferably 100 times) and a second-order Butterworth low-pass filter circuit (cutoff frequency 2kHz) to filter out high-frequency commutation interference from the motor and grid noise, thus completing anti-aliasing processing.

[0047] Digital conversion and sampling rules: Analog-to-digital conversion is performed using a 16-bit or higher synchronous sampling ADC with a sampling frequency of not less than 10kHz and a single acquisition duration of not less than 20 power frequency cycles (400ms at 50Hz power frequency) to ensure that the complete power frequency fundamental and harmonic signals are acquired and to avoid random errors in a single sampling.

[0048] Data synchronization: The collected leakage current digital signal is timestamped synchronously, and the time deviation with the subsequent temperature and humidity data is ≤10ms before being transmitted to the core processing unit.

[0049] S2. Based on the ambient temperature data and the ambient humidity data, the leakage current signal is compensated. Through the pre-established temperature and humidity compensation model, the measured leakage current is converted into the equivalent insulation resistance value under the standard environment of 25℃ and 50% relative humidity.

[0050] The core of this step is to eliminate the interference of ambient temperature and humidity on leakage current through a pre-calibrated temperature and humidity compensation model, thereby obtaining a standard environmental equivalent insulation resistance that can be compared laterally. The specific implementation is as follows:

[0051] 1. Pre-establishment and calibration of the temperature and humidity compensation model

[0052] The model is calibrated before mass production and stored in the non-volatile memory of the core processing unit. The calibration process is as follows:

[0053] Calibration environment: A programmable constant temperature and humidity test chamber is used, with a temperature range of -10℃ to 60℃ and a relative humidity range of 20% to 95%RH, which fully covers the actual operating conditions of the high-speed blower.

[0054] Calibration Samples: Select no fewer than 10 healthy heating racks from the same batch that have passed factory inspection to eliminate individual differences; each sample is operated stably under rated operating voltage, and the fundamental amplitude of the leakage current is collected under each temperature-humidity combination condition. The average value of all samples is taken as the standard leakage current reference value under that condition. .

[0055] Model storage: Store all calibration conditions (T, H, I) base The data is constructed into a two-dimensional lookup table, and interpolation calculation formulas are generated through multivariate nonlinear fitting to handle parameter calculations for non-calibrated operating conditions, together forming a temperature and humidity compensation model.

[0056] 2. Synchronous collection of temperature and humidity data

[0057] Temperature data: The temperature is collected by a PT100 platinum resistance temperature sensor mounted on the mica insulation support body of the heating frame. The measurement range is -40℃ to 120℃, and the sampling accuracy is ±0.2℃, which directly reflects the actual working temperature of the insulation medium of the heating frame.

[0058] Humidity data: collected by a capacitive humidity sensor (preferably SHT30) installed at the air inlet of the device, with a measurement range of 0-100%RH and a sampling accuracy of ±2%RH. The installation position should avoid direct airflow from the heating frame to ensure that the collected humidity is the true ambient humidity.

[0059] 3. Compensation treatment and equivalent insulation resistance calculation

[0060] Reference value matching: Input the synchronously collected temperature T and humidity H into the temperature and humidity compensation model. If the operating conditions are within the calibration range, directly match the corresponding standard leakage current reference value I through a two-dimensional lookup table. base If it exceeds the calibration range, I is obtained by interpolation using the fitting formula. base .

[0061] Compensation coefficient calculation: The formula for calculating the compensation coefficient is as follows:

[0062] K=I base / A1, where A1 is the fundamental amplitude of the measured leakage current signal.

[0063] Standard environment conversion: Standard environment is defined as 25℃, 50% relative humidity, and rated operating voltage of U. n (Civilian scenario: 220V AC); After compensation, the measured leakage current is the equivalent leakage current I under standard conditions. eq =A1×K, the final equivalent insulation resistance value is calculated using the following formula:

[0064] R eq =U n / I eq

[0065] The calculated R eq It is transmitted to the subsequent computing module and simultaneously stored in local storage.

[0066] S3. Perform spectrum analysis on the leakage current signal, extract the harmonic component characteristics of the fundamental amplitude, third harmonic amplitude, and fifth harmonic amplitude in the leakage current signal, and generate an insulation nonlinear degradation index based on the weighted summation of the ratio of the harmonic amplitude to the fundamental amplitude according to the harmonic component characteristics.

[0067] Furthermore, in the above technical solution, the step of performing spectral analysis on the leakage current signal, extracting the harmonic component characteristics of the leakage current signal, and generating an insulation nonlinear degradation index based on the harmonic component characteristics includes:

[0068] After performing detrending and Hanning window preprocessing on the collected leakage current signal, a fast Fourier transform is performed on the leakage current signal to extract the fundamental amplitude, third harmonic amplitude and fifth harmonic amplitude.

[0069] Calculate the relative content of the third harmonic amplitude to the third harmonic of the fundamental amplitude (first ratio), and the relative content of the fifth harmonic amplitude to the fifth harmonic of the fundamental amplitude (second ratio).

[0070] The first ratio and the second ratio are weighted and summed to generate an insulation nonlinear degradation index, which is used to characterize the aging degree and micro-discharge state of the insulating medium. The first weighting coefficient corresponding to the third harmonic ranges from 0.6 to 0.8, the second weighting coefficient corresponding to the fifth harmonic ranges from 0.2 to 0.4, and the sum of the first weighting coefficient and the second weighting coefficient is 1.

[0071] It should be further explained that the core of this step is to capture the nonlinear distortion and micro-discharge characteristics caused by the early aging of the insulating medium through harmonic feature extraction, thereby compensating for the deficiency that a single insulation resistance cannot identify microscopic degradation. The specific implementation is as follows:

[0072] 1. Spectrum Analysis Preprocessing

[0073] The acquired digital signal of leakage current is first detrended to eliminate DC offset; then a Hanning window is applied to suppress spectral leakage of FFT operation; finally, a 1024-point Fast Fourier Transform (FFT) is performed with a frequency resolution of not less than 5Hz to accurately separate each harmonic component.

[0074] 2. Harmonic component feature extraction

[0075] After aging, the current-voltage characteristics of the insulating dielectric exhibit strong nonlinearity, producing significant odd harmonics at power frequency voltage. The 3rd and 5th harmonics are the core characteristics of aging, while even harmonics are present in negligible amounts. Extracted from the FFT spectrum:

[0076] The fundamental frequency amplitude A1 corresponding to a 50Hz power frequency;

[0077] The third harmonic amplitude A3 corresponds to 150Hz;

[0078] The amplitude of the fifth harmonic corresponding to 250Hz is A5.

[0079] 3. Calculation of Insulation Nonlinear Degradation Index

[0080] First, calculate the harmonic ratio: the first ratio of the third harmonic to the fundamental frequency, THD3 = A3 / A1, and the second ratio of the fifth harmonic to the fundamental frequency, THD5 = A5 / A1;

[0081] The insulation nonlinear degradation index D is then generated by weighted summation, and the calculation formula is as follows:

[0082] D = w3 × THD3 + w5 × THD5

[0083] Wherein, w3 is the third harmonic weighting coefficient, with a value range of 0.6 to 0.8; w5 is the fifth harmonic weighting coefficient, with a value range of 0.2 to 0.4, and w3+w5=1; in the preferred embodiment, w3=0.7 and w5=0.3 (the third harmonic is more sensitive to early aging of insulation).

[0084] Physical meaning of the index: The larger the D value, the stronger the nonlinearity of the insulating medium, the more severe the aging, and the more obvious the local micro-discharge phenomenon; when the D value approaches 0, it indicates that the insulating medium is in a healthy linear state. The calculated D value is transmitted to the subsequent calculation module.

[0085] As another alternative implementation, when the insulating material is a polyimide film or a ceramic matrix, the seventh harmonic content may also change significantly during aging. In such applications, the calculation formula for the insulation nonlinear degradation index D can be extended to:

[0086] D = w3 × THD3 + w5 × THD5 + w7 × THD7

[0087] Where THD7 is the ratio of the seventh harmonic amplitude to the fundamental amplitude, w7 is the corresponding weighting coefficient, and w3+w5+w7=1. The value of w7 ranges from 0.1 to 0.2, and the values ​​of w3 and w5 are adjusted accordingly.

[0088] S4. Based on the equivalent insulation resistance value and the insulation nonlinear degradation index, calculate the current health index of the heating frame by weighted fusion of resistance retention rate and harmonic deviation. Combine the historical health index data of the heating frame and predict the remaining insulation life of the heating frame based on the insulation aging index degradation model.

[0089] Furthermore, in the above technical solution, calculating the current health index of the heating frame based on the equivalent insulation resistance value and the insulation nonlinear degradation index includes:

[0090] Obtain the initial nominal insulation resistance value calibrated when the heating frame leaves the factory, and calculate the resistance retention rate between the equivalent insulation resistance value and the nominal insulation resistance value;

[0091] Obtain a preset harmonic distortion threshold obtained through the calibration of insulation critical failure state samples, and calculate the deviation between the insulation nonlinear degradation index and the harmonic distortion threshold; the calculation rule for the deviation is as follows: when the insulation nonlinear degradation index ≤ harmonic distortion threshold, the deviation = 1 - (insulation nonlinear degradation index / harmonic distortion threshold); when the insulation nonlinear degradation index > harmonic distortion threshold, the deviation = 0.

[0092] The current health index of the heating element is generated by multiplying the resistance retention rate by the first weighting coefficient and adding the deviation rate by the second weighting coefficient. The health index ranges from 0 to 1, with a higher value indicating healthier insulation performance. The first weighting coefficient corresponding to the resistance retention rate ranges from 0.4 to 0.6, the second weighting coefficient corresponding to the deviation rate ranges from 0.4 to 0.6, and the sum of the first and second weighting coefficients is 1.

[0093] Furthermore, in the above technical solution, predicting the remaining insulation life of the heating frame by combining historical health index data of the heating frame includes:

[0094] Extract multiple historical health indices of the heating rack within a preset historical period from the historical database. Each historical health index corresponds to a cumulative running time, and construct a health index degradation time series.

[0095] The least squares method is used to fit the insulation aging index degradation model to the health index degradation time series, generating a health index degradation curve. The instantaneous degradation rate of the health index degradation curve is calculated by differentiating the degradation curve.

[0096] Based on the instantaneous degradation rate and the preset insulation failure health index threshold, the remaining time required for the heating frame to drop from the current health index to the failure threshold is predicted, and the remaining insulation lifetime is generated; when the instantaneous degradation rate exceeds the preset rate threshold, it is determined as a sudden insulation degradation, and the remaining insulation lifetime is directly set to 0.

[0097] It should be further explained that this step integrates macroscopic insulation resistance characteristics with microscopic harmonic aging features to achieve a quantitative assessment of insulation health status and to predict remaining lifetime based on degradation trends. The specific implementation is as follows:

[0098] 1. Current Health Index (HI) Calculation

[0099] The health index ranges from 0 to 1. A higher value indicates healthier insulation performance. HI=1 represents a brand new, healthy state, while HI=0 represents a complete failure state. The calculation steps are as follows:

[0100] Step 1: Calculate the resistance retention rate. Obtain the nominal insulation resistance value R of the heating element at the time of manufacture. new (High-speed hair dryer heating pad preferred R) new ≥100MΩ), resistance retention rate η=R eq / R new The value range is 0 to 1.

[0101] Step 2: Calculate the harmonic deviation. Obtain the preset harmonic distortion threshold γ. th This threshold is obtained by calibrating a heating frame sample aged to a critical failure state. In the preferred embodiment, γ is used. th =0.15; the deviation calculation formula is:

[0102] The deviation δ ranges from 0 to 1, with a lower value indicating more severe harmonic distortion.

[0103] Step 3: Calculate the weighted health index. The calculation formula is:

[0104] HI = α × η + β × δ

[0105] Wherein, α is the first weighting coefficient of resistance retention rate, β is the second weighting coefficient of deviation, α+β=1, α ranges from 0.4 to 0.6, and β ranges from 0.4 to 0.6; in the preferred embodiment, α=0.5 and β=0.5 are taken to balance the macroscopic performance of insulation and the microscopic aging characteristics.

[0106] 2. Remaining Insulation Life (RUL) Prediction

[0107] Based on the exponential degradation characteristics of insulation aging, the remaining lifetime is quantitatively predicted by fitting historical data. The specific steps are as follows:

[0108] Step 1: Construct a time series of health index degradation. Extract historical health index data from local storage or a cloud database for a preset historical period (preferably with a cumulative runtime ≥ 10 hours and a time span ≤ 90 days). Each data point includes the cumulative runtime t. i and the corresponding health index HI i Construct a time series {(t1,HI1),(t2,HI2),...,(t n HI n )}, where n≥10, to ensure sufficient amount of fitting data.

[0109] Step 2: Degradation Curve Fitting. The least squares method is used to fit the time series to an exponential degradation model (insulation aging is a typical exponential degradation process). The fitting formula is:

[0110] HI(t) = HI0 × exp(−λ × t)

[0111] Where HI0 is the initial health index, λ is the degradation rate coefficient, and t is the cumulative running time; the optimal HI0 and λ are obtained by fitting, and the health index degradation curve is generated.

[0112] Step 3: Calculation of instantaneous degradation rate. Differentiate the degradation curve to obtain the instantaneous degradation rate.

[0113] v(t) = dHI(t) / dt = −HI0×λ×exp(−λ×t), taking the absolute value |v(t)| as the effective degradation rate, with the unit being 1 / hour. The larger the value, the faster the insulation degradation.

[0114] Step 4: Remaining life calculation. Preset insulation failure threshold HI. fail In the preferred embodiment, the health index is set to 0.2, meaning that insulation failure is determined when the health index drops to 0.2; based on the fitted degradation curve, the equation HI(t) is solved. end )=HI fail The cumulative operating time t when the insulation reaches a failure state is obtained. end The final insulation remaining life calculation formula is: RUL=t end -t now

[0115] Among them, t now The current cumulative operating time of the device is expressed in hours.

[0116] Sudden degradation determination: If the instantaneous degradation rate |v(t)| exceeds the preset rate threshold (preferably 0.01 / hour), it is determined as a sudden degradation of insulation, and the remaining lifetime RUL is directly set to 0, triggering the highest level warning to deal with the risk of sudden insulation breakdown.

[0117] S5. Based on the health index and the remaining insulation life, determine whether the heating frame is in an abnormal insulation performance state based on the preset two-level health threshold and life threshold; if the heating frame is in an abnormal insulation performance state, generate corresponding fault warning information according to the abnormality level, and send the fault warning information to the user terminal.

[0118] Furthermore, in the above technical solution, the step of determining whether the heating frame is in an abnormal insulation performance state based on the health index and the remaining insulation life; if the heating frame is in an abnormal insulation performance state, then generating corresponding fault warning information according to the abnormality level, including:

[0119] The health index is compared with a preset first health threshold and a second health threshold. The first health threshold is an insulation attention level warning threshold, and its value is higher than the second health threshold (insulation hazard level warning threshold).

[0120] When the health index is lower than the first health threshold and higher than or equal to the second health threshold, it is determined to be a level one abnormal state, and a warning message is generated to prompt the user to perform insulation drying treatment or pay attention to maintenance. In this state, the device is kept powered normally, and only local prompts and user terminal message pushes are triggered.

[0121] When the health index is lower than the second health threshold, or the remaining insulation life is less than the preset life threshold, or the instantaneous degradation rate exceeds the preset rate threshold, or the insulation nonlinear degradation index exceeds the harmonic distortion threshold, it is determined to be a level two abnormal state, a danger level warning message is generated, a corresponding fault code is generated simultaneously, and the power supply circuit of the heating frame is immediately cut off. The fan is kept running at 30%-50% of the rated speed until the heating frame cools down and the whole machine is powered off.

[0122] Furthermore, in the above technical solution, the method further includes:

[0123] Obtain the user characteristics of the target user, including user role and operation permission level;

[0124] Based on the user characteristics, the fault warning information is adjusted to generate target fault warning information;

[0125] The target fault warning information is sent to the user terminal.

[0126] It should be further explained that this step employs a two-level anomaly detection mechanism to achieve differentiated early warning and security control, and also supports differentiated push of early warning information based on user permissions, such as... Figure 2 As shown, the specific implementation is as follows:

[0127] 1. Preset threshold definition

[0128] The grading threshold can be configured in advance in the program and updated remotely via the cloud. Specifically:

[0129] First health threshold HI th1 (Note the threshold value), preferably 0.6;

[0130] Second health threshold HI th2 (Hazard level threshold), preferably 0.3;

[0131] Lifetime Threshold RUL th The preferred value is 50 hours;

[0132] The preferred rate threshold is 0.01 / hour;

[0133] Harmonic distortion threshold γ th =0.15.

[0134] The selection of the above thresholds is based on the following experimental criteria:

[0135] First health threshold HI th1 Take 0.6, the second health threshold HI th2 The basis for determining 0.3 is as follows: Through accelerated aging tests and insulation breakdown tests on 200 heating rack samples with different aging levels, it was found that when the health index HI is lower than 0.6, the probability of accelerated insulation performance degradation of the sample increases significantly in the following 100 hours; when HI is lower than 0.3, the probability of insulation breakdown of the sample exceeds 95% in the following 72 hours.

[0136] Harmonic distortion threshold γ th The determination of 0.15 is based on the following: the insulation nonlinear degradation index D of 100 healthy heating rack samples was statistically analyzed, and its 99% confidence interval upper limit is 0.12. After taking 1.25 times the safety margin, it was determined to be 0.15, so as to effectively distinguish between normal fluctuations and early aging.

[0137] Lifetime Threshold RUL th The 50-hour timeframe was determined based on the average daily usage time of typical users (approximately 0.5-1 hour). 50 hours corresponds to a usage window of approximately 2-3 months, which is sufficient to allow users reasonable maintenance response time.

[0138] 2. Abnormal Status Classification and Early Warning Execution

[0139] Normal state: When HI now ≥HI th1 And RUL≥RUL th If the insulation performance is deemed normal, no warning information is generated; only the test data is synchronously stored locally and in the cloud, and the real-time health status can be displayed through the user terminal.

[0140] Level 1 Abnormal State (Attention Level): When HI th2 ≤HI now <HI th1 And RUL≥RUL th If the condition is detected, it is classified as a Level 1 anomaly, corresponding to insulation dampness or early minor aging. A warning message is generated, including the current health index, equivalent insulation resistance value, anomaly cause analysis, and maintenance recommendations (such as "Insulation dampness, power off and dry treatment recommended" or "Minor insulation aging, monitor operating status recommended"). This is indicated by a flashing yellow indicator light on the device without disconnecting the power supply, and the warning message is simultaneously pushed to the user's linked mobile app.

[0141] Level 2 Abnormal Status (Hazard Level): A status is determined to be Level 2 Abnormal if any of the following conditions are met: ①HI now <HI th2 ; ②RUL <RUL th ③ The instantaneous degradation rate exceeds the rate threshold; ④ The insulation nonlinear degradation index D > γ th At this point, a danger warning message is generated, including fault codes (E01 = severe insulation degradation, E02 = insufficient remaining life, E03 = risk of sudden breakdown), current insulation status parameters, and emergency handling suggestions; simultaneously, the red indicator light on the drive device remains constantly lit, and the buzzer sounds intermittently. The core processing unit immediately sends a power-off command to the power control module, cutting off the power supply circuit to the heating frame, leaving only the fan running at 30%-50% of its rated speed until the heating frame cools down, after which the entire machine is powered off to prevent further insulation degradation and breakdown at high temperatures; the warning message is simultaneously pushed to the user terminal and the brand's after-sales management platform, triggering proactive after-sales service.

[0142] 3. Differentiated adaptation of early warning information based on user permissions

[0143] First, obtain the user characteristics of the target users, including user roles and operation permission levels, which are divided into three categories: ① ordinary users (end consumers), permission level 1; ② after-sales maintenance personnel, permission level 2; ③ R&D test personnel, permission level 3.

[0144] For ordinary users with permission level 1, simplified target warning information is generated, showing only the fault level, handling suggestions, and after-sales contact information, while hiding professional technical parameters to lower the user's understanding threshold.

[0145] For after-sales maintenance personnel with access level 2, professional-grade target early warning information is generated, including fault level, detailed insulation parameters, historical degradation curves, fault location, and standardized maintenance plan to support on-site inspection.

[0146] For R&D testers with permission level 3, generate full target early warning information, including raw data collection, spectrum analysis results, the entire model calculation process, and full data on degradation trends, for product optimization and iteration.

[0147] Finally, the adapted target warning information will be sent to the corresponding user's mobile APP, PC management platform, and other user terminals via WiFi / Bluetooth / NB-IoT communication modules.

[0148] Furthermore, in the above technical solutions, such as Figure 3 As shown, the heating frame insulation performance testing and fault early warning system includes: a data acquisition module, a compensation module, an analysis module, a calculation module, and an early warning module; wherein,

[0149] The acquisition module is used to respond to the detection start command of the target device, acquire the leakage current signal of the heating rack in the target device during operation, as well as the ambient temperature and humidity data during the operation of the heating rack, and transmit the synchronously acquired leakage current signal and temperature and humidity data to the compensation module and the analysis module respectively.

[0150] The compensation module is used to receive temperature and humidity data and leakage current fundamental amplitude value transmitted by the acquisition module, convert the measured leakage current into equivalent insulation resistance value under standard environment through the pre-stored temperature and humidity compensation model, and transmit the equivalent insulation resistance value to the calculation module.

[0151] The analysis module is used to receive the leakage current signal transmitted by the acquisition module, perform spectrum analysis on the leakage current signal, extract the harmonic component characteristics of the fundamental amplitude, third harmonic amplitude, and fifth harmonic amplitude in the leakage current signal, generate an insulation nonlinear degradation index based on the weighted summation of the ratio of the harmonic amplitude to the fundamental amplitude according to the harmonic component characteristics, and transmit the insulation nonlinear degradation index to the calculation module.

[0152] The calculation module is used to receive the equivalent insulation resistance value of the compensation module and the insulation nonlinear degradation index of the analysis module. It calculates the current health index of the heating frame by weighted fusion of resistance retention rate and harmonic deviation. Combining the historical health index data of the heating frame, it predicts the remaining insulation life of the heating frame based on the insulation aging index degradation model, and transmits the health index and remaining insulation life to the early warning module.

[0153] The early warning module is used to receive the health index and remaining insulation life from the calculation module, and determine whether the heating frame is in an abnormal insulation performance state based on the preset two-level health threshold and life threshold; if the heating frame is in an abnormal insulation performance state, the module generates corresponding fault early warning information according to the abnormality level and sends the fault early warning information to the user terminal.

[0154] It should be further explained that, to implement the functional architecture of the above methods, this system is divided into a hardware layer and a software layer. The hardware layer is deployed on the target device or independent testing fixture, and the software layer runs in the core processing unit. The detailed implementation of each module of the system is as follows:

[0155] 1. Data Acquisition Module

[0156] Hardware components: high-precision micro-current sensor, PT100 temperature sensor, capacitive humidity sensor, signal conditioning circuit, high-speed ADC acquisition circuit, and acquisition driver program in the core processing unit.

[0157] Functional implementation: Responding to the detection start command, controlling multiple sensors to collect data synchronously, completing the amplification, filtering, and analog-to-digital conversion of leakage current, temperature, and humidity signals, ensuring that the time synchronization deviation of multi-source data is ≤10ms; after timestamping the raw data, it is transmitted to the compensation module and the analysis module respectively, and simultaneously synchronized to the storage module.

[0158] Key parameters: Microcurrent sensor resolution ≥1μA, bandwidth 0-10kHz; Temperature sensor accuracy ±0.2℃; Humidity sensor accuracy ±2% RH; ADC sampling bit depth ≥16 bits, sampling frequency ≥10kHz.

[0159] 2. Compensation Module

[0160] Hardware carrier: core processing unit MCU / DSP, built-in Flash memory.

[0161] Functional implementation: The calibrated temperature and humidity compensation model is pre-stored in Flash. It receives temperature and humidity data and leakage current fundamental amplitude value transmitted by the acquisition module, completes the working condition matching and benchmark value calculation, generates compensation coefficients, and calculates the equivalent insulation resistance value under standard environment. The calculation results are transmitted to the calculation module and simultaneously synchronized to the storage module.

[0162] 3. Analysis Module

[0163] Hardware platform: MCU / DSP with ARM Cortex-M4F or higher core with DSP instruction set (preferably STM32F407), which has fast FFT operation capability.

[0164] Functional implementation: Receives the leakage current digital signal transmitted by the acquisition module, performs detrending and windowing preprocessing, executes 1024-point FFT spectrum analysis, extracts the fundamental, third, and fifth harmonic amplitudes, calculates the harmonic ratio and insulation nonlinear degradation index; transmits the calculation results to the calculation module, and simultaneously synchronizes the spectrum analysis results to the storage module.

[0165] 4. Calculation Module

[0166] Hardware carriers: core processing unit MCU / DSP, local / cloud storage database.

[0167] Functional Implementation: Receives the equivalent insulation resistance value from the compensation module and the insulation nonlinear degradation index from the analysis module; reads pre-stored factory parameters, thresholds, and weighting coefficients; calculates the current health index; extracts historical health index data from the storage module; constructs a degradation time series; completes curve fitting, degradation rate calculation, and insulation remaining life prediction; transmits the health index and remaining life data to the early warning module; and simultaneously synchronizes the full calculation results to the storage module and communication module.

[0168] 5. Early Warning Module

[0169] Hardware components: core processing unit, local indicator lights, buzzer, power control relay, and communication unit.

[0170] Functional Implementation: Receives health index and remaining lifespan data from the calculation module and completes the classification and judgment of abnormal states; drives the local early warning peripheral to perform corresponding actions according to the level of abnormality. Level 1 abnormality causes the yellow indicator light to flash, and level 2 abnormality causes the red indicator light and buzzer to activate and cut off the power to the heating rack; completes differentiated adaptation of early warning information according to user permission level, and sends the target early warning information to the user terminal and cloud platform through the communication module.

[0171] 6. Supplementary Module

[0172] Storage module: Adopts Flash+SD card architecture, with Flash capacity ≥256KB and SD card support for up to 32GB expansion. It is used to store raw collected data, temperature and humidity compensation models, preset thresholds, device factory parameters, and historical health index data. The local data storage time is no less than 1 year.

[0173] Communication module: Integrates WiFi, Bluetooth, and NB-IoT communication units to enable two-way communication between the device and the user terminal, cloud server, and after-sales management platform, supporting data upload, early warning push, remote command issuance, and firmware upgrade.

[0174] This invention provides an electronic device, including a processor, a memory, a user interface, a network interface, a signal acquisition interface, a peripheral control interface, and a power management unit. Each unit achieves bidirectional data interaction through an AHB system bus. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform one or more of the methods described in the above embodiments.

[0175] It should be further noted that the electronic device disclosed in this embodiment, as a hardware carrier for implementing the above method, can be divided into two forms: one is an intelligent control board integrated inside small household appliances such as high-speed hair dryers, and the other is an independent heating element insulation performance offline / online testing fixture. The specific structure and implementation are as follows:

[0176] Processor: The core computing and control unit of the device, preferably a 32-bit MCU with an ARM Cortex-M4F core, a main frequency of ≥168MHz, a built-in DSP instruction set and FPU floating-point unit, and the ability to perform 1024-point FFT fast calculations; The processor is used to execute computer instructions stored in memory, complete all data processing, algorithm calculation, logical judgment and control instruction issuance in the above methods, and is the computing core of the entire device.

[0177] The memory includes non-volatile memory (Flash, capacity ≥ 256KB) and random access memory (SRAM, capacity ≥ 64KB); the Flash is used to store computer programs, temperature and humidity compensation models, preset thresholds, equipment factory parameters, and historical operating data; the SRAM is used for temporary data caching and algorithm calculations during program execution to ensure smooth program operation; the computer program stored in the memory contains instructions corresponding to all steps of the above-mentioned heating rack insulation performance detection and fault early warning method, which can be loaded and executed by the processor.

[0178] Signal acquisition interface: Includes an 8-channel synchronous ADC input interface and a sensor power supply interface, which are electrically connected to external micro-current sensors, temperature sensors, and humidity sensors; provides a stable 3.3V / 5V power supply to the sensors, and receives analog signals acquired by the sensors, which are then transmitted to the internal ADC conversion unit to realize the synchronous acquisition of multi-source data, supporting the synchronous access of up to 8 sensor signals.

[0179] Peripheral control interface: Includes 16 GPIO interfaces, relay drive interfaces, and PWM output interfaces, which are electrically connected to external indicator lights, buzzers, heating rack power control relays, and fan drive modules, respectively; used to drive peripherals to perform early warning actions, power cut-off operations, and fan speed control according to instructions issued by the processor. The interface withstands 30V DC and the drive current is ≥20mA.

[0180] User interface: Includes button input interface, TFT display interface, and Bluetooth 5.0 communication interface, used for local interaction with the user; receives user commands such as detection start, parameter setting, and threshold adjustment, and displays the device operating status, insulation test results, warning information and fault codes locally to the user.

[0181] Network interfaces include WiFi 6, 10M / 100M Ethernet, and NB-IoT interfaces, used for wide area network communication between electronic devices and cloud servers, user remote terminals, and brand after-sales management platforms; enabling cloud storage of detection data, remote push of early warning information, remote parameter configuration, and firmware OTA upgrades.

[0182] Power Management Unit: Includes wide voltage input circuit, LDO linear regulator circuit, and overcurrent / overvoltage / overheat protection circuit; input voltage supports AC 220V mains power or DC 12V / 24V DC power supply, and outputs 3.3V, 5V, and 12V multi-channel stable voltage to power all units such as processor, sensors, communication modules, and peripherals; it also has complete protection functions to ensure the safe operation of the equipment under complex working conditions.

[0183] Equipment workflow: After the equipment is powered on, the processor loads the computer program from the memory and runs it to complete system initialization and peripheral self-test; it collects sensor data in real time through the signal acquisition interface and executes the entire process steps of the above insulation performance detection and fault early warning method; based on the calculation results, it executes the corresponding early warning and safety control actions through the peripheral control interface; and it sends the detection results and early warning information to the user terminal and cloud platform simultaneously through the user interface and network interface.

[0184] The present invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute one or more of the methods described in the above embodiments.

[0185] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 method for detecting insulation performance and early warning of faults of a heating rack, characterized in that, The method includes: S1. In response to the detection start command of the target device, synchronously collect the leakage current signal of the heating rack in the target device during operation, as well as the ambient temperature data and ambient humidity data during the operation of the heating rack, and the time synchronization deviation of the multi-source data is ≤10ms. S2. Based on the ambient temperature data and the ambient humidity data, the leakage current signal is compensated. Through the pre-established temperature and humidity compensation model, the measured leakage current is converted into the equivalent insulation resistance value under the standard environment of 25℃ and 50% relative humidity. S3. The leakage current signal is subjected to spectrum analysis to extract the harmonic component characteristics including the fundamental amplitude, the third harmonic amplitude, and the fifth harmonic amplitude. Based on the harmonic component characteristics, the insulation nonlinear degradation index is generated by weighted summation based on the ratio of the harmonic amplitude to the fundamental amplitude. S4. Based on the equivalent insulation resistance value and the insulation nonlinear degradation index, calculate the current health index of the heating frame by weighted fusion of resistance retention rate and harmonic deviation. Combine the historical health index data of the heating frame and predict the remaining insulation life of the heating frame based on the insulation aging index degradation model. S5. Based on the health index and the remaining insulation life, determine whether the heating frame is in an abnormal insulation performance state based on the preset two-level health threshold and life threshold; if the heating frame is in an abnormal insulation performance state, generate corresponding fault warning information according to the abnormality level, and send the fault warning information to the user terminal.

2. The method for detecting the insulation performance of the heat frame and giving early warning of failure according to claim 1, characterized in that: The step of compensating the leakage current signal based on the ambient temperature data and the ambient humidity data to generate an equivalent insulation resistance value under standard conditions includes: A pre-established temperature and humidity compensation model is obtained, which includes the standard leakage current reference value of a qualified healthy heating rack under different temperature and humidity combinations. The qualified healthy heating rack is a sample of heating racks from the same batch whose insulation resistance value is within ±5% of the factory nominal value and whose insulation nonlinear degradation index is confirmed to be lower than the preset reference threshold of 0.05 by spectrum analysis. The standard leakage current reference value is the average value of the fundamental amplitude of the leakage current of no less than 10 qualified healthy heating racks under the corresponding temperature and humidity conditions. The collected ambient temperature and humidity data are input into the temperature and humidity compensation model to match the corresponding standard leakage current reference value; if the collected temperature and humidity conditions exceed the calibration range, the corresponding standard leakage current reference value is calculated through a pre-fitted multivariate nonlinear interpolation formula. Based on the standard leakage current reference value and the fundamental amplitude of the leakage current signal, a compensation coefficient is calculated, and the measured leakage current is converted into an equivalent insulation resistance value under a standard environment of 25°C and 50% relative humidity using the compensation coefficient.

3. The method for detecting the insulation performance of the heat frame and giving early warning of failure according to claim 1, characterized in that: The process involves performing spectral analysis on the leakage current signal, extracting harmonic component characteristics from the leakage current signal, and generating an insulation nonlinear degradation index based on the harmonic component characteristics, including: After performing detrending and Hanning window preprocessing on the collected leakage current signal, a fast Fourier transform is performed on the leakage current signal to extract the fundamental amplitude, third harmonic amplitude and fifth harmonic amplitude. Calculate the relative content of the third harmonic amplitude to the third harmonic of the fundamental amplitude (first ratio), and the relative content of the fifth harmonic amplitude to the fifth harmonic of the fundamental amplitude (second ratio). The first ratio and the second ratio are weighted and summed to generate an insulation nonlinear degradation index, which is used to characterize the aging degree and micro-discharge state of the insulating medium. The first weighting coefficient corresponding to the third harmonic ranges from 0.6 to 0.8, the second weighting coefficient corresponding to the fifth harmonic ranges from 0.2 to 0.4, and the sum of the first weighting coefficient and the second weighting coefficient is 1.

4. The method for detecting the insulation performance and providing fault early warning of a heating frame according to claim 1, characterized in that: The step of calculating the current health index of the heating frame based on the equivalent insulation resistance value and the insulation nonlinear degradation index includes: Obtain the initial nominal insulation resistance value calibrated when the heating frame leaves the factory, and calculate the resistance retention rate between the equivalent insulation resistance value and the nominal insulation resistance value; Obtain a preset harmonic distortion threshold obtained through the calibration of insulation critical failure state samples, and calculate the deviation between the insulation nonlinear degradation index and the harmonic distortion threshold; the calculation rule for the deviation is as follows: when the insulation nonlinear degradation index ≤ harmonic distortion threshold, the deviation = 1 - (insulation nonlinear degradation index / harmonic distortion threshold); when the insulation nonlinear degradation index > harmonic distortion threshold, the deviation = 0. The current health index of the heating element is generated by multiplying the resistance retention rate by the first weighting coefficient and adding the deviation rate by the second weighting coefficient. The health index ranges from 0 to 1, with a higher value indicating healthier insulation performance. The first weighting coefficient corresponding to the resistance retention rate ranges from 0.4 to 0.6, the second weighting coefficient corresponding to the deviation rate ranges from 0.4 to 0.6, and the sum of the first and second weighting coefficients is 1.

5. The method for detecting the insulation performance and providing fault early warning of a heating frame according to claim 1, characterized in that: The method of predicting the remaining insulation life of the heating element by combining historical health index data includes: Extract multiple historical health indices of the heating rack within a preset historical period from the historical database. Each historical health index corresponds to a cumulative running time, and construct a health index degradation time series. The least squares method is used to fit the insulation aging index degradation model to the health index degradation time series, generating a health index degradation curve. The instantaneous degradation rate of the health index degradation curve is calculated by differentiating the degradation curve. Based on the instantaneous degradation rate and the preset insulation failure health index threshold, the remaining time required for the heating frame to drop from the current health index to the failure threshold is predicted, and the remaining insulation lifetime is generated; when the instantaneous degradation rate exceeds the preset rate threshold, it is determined as a sudden insulation degradation, and the remaining insulation lifetime is directly set to 0.

6. The method for detecting the insulation performance and providing fault early warning of a heating frame according to claim 1, characterized in that: The method determines whether the heating frame is in an abnormal insulation performance state based on the health index and the remaining insulation life. If the heating frame is in an abnormal insulation performance state, a corresponding fault warning message is generated according to the abnormality level, including: The health index is compared with a preset first health threshold and a second health threshold. The first health threshold is an insulation attention level warning threshold, and its value is higher than the second health threshold (insulation hazard level warning threshold). When the health index is lower than the first health threshold and higher than or equal to the second health threshold, it is determined to be a level one abnormal state, and a warning message is generated to prompt the user to perform insulation drying treatment or pay attention to maintenance. In this state, the device is kept powered normally, and only local prompts and user terminal message pushes are triggered. When the health index is lower than the second health threshold, or the remaining insulation life is less than the preset life threshold, or the instantaneous degradation rate exceeds the preset rate threshold, or the insulation nonlinear degradation index exceeds the harmonic distortion threshold, it is determined to be a level two abnormal state, a danger level warning message is generated, a corresponding fault code is generated simultaneously, and the power supply circuit of the heating frame is immediately cut off. The fan is kept running at 30%-50% of the rated speed until the heating frame cools down and the whole machine is powered off.

7. The method for detecting the insulation performance and providing early warning of faults in a heating frame according to claim 6, characterized in that: The method further includes: Obtain the user characteristics of the target user, including user role and operation permission level; Based on the user characteristics, the fault warning information is adjusted to generate target fault warning information; The target fault warning information is sent to the user terminal.

8. A heating frame insulation performance testing and fault early warning system, characterized in that: The system includes: a data acquisition module, a compensation module, an analysis module, a calculation module, and an early warning module; wherein, The acquisition module is used to respond to the detection start command of the target device, acquire the leakage current signal of the heating rack in the target device during operation, as well as the ambient temperature and humidity data during the operation of the heating rack, and transmit the synchronously acquired leakage current signal and temperature and humidity data to the compensation module and the analysis module respectively. The compensation module is used to receive temperature and humidity data and leakage current fundamental amplitude value transmitted by the acquisition module, convert the measured leakage current into equivalent insulation resistance value under standard environment through the pre-stored temperature and humidity compensation model, and transmit the equivalent insulation resistance value to the calculation module. The analysis module is used to receive the leakage current signal transmitted by the acquisition module, perform spectrum analysis on the leakage current signal, extract the harmonic component characteristics of the fundamental amplitude, third harmonic amplitude, and fifth harmonic amplitude in the leakage current signal, generate an insulation nonlinear degradation index based on the weighted summation of the ratio of the harmonic amplitude to the fundamental amplitude according to the harmonic component characteristics, and transmit the insulation nonlinear degradation index to the calculation module. The calculation module is used to receive the equivalent insulation resistance value of the compensation module and the insulation nonlinear degradation index of the analysis module. It calculates the current health index of the heating frame by weighted fusion of resistance retention rate and harmonic deviation. Combining the historical health index data of the heating frame, it predicts the remaining insulation life of the heating frame based on the insulation aging index degradation model, and transmits the health index and remaining insulation life to the early warning module. The early warning module is used to receive the health index and remaining insulation life from the calculation module, and determine whether the heating frame is in an abnormal insulation performance state based on the preset two-level health threshold and life threshold; if the heating frame is in an abnormal insulation performance state, the module generates corresponding fault early warning information according to the abnormality level and sends the fault early warning information to the user terminal.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, a network interface, a signal acquisition interface, a peripheral control interface, and a power management unit. Each unit achieves bidirectional data interaction through an AHB system bus. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

Images