High temperature resistance test method of foamed silicone rubber based on data identification

Abstract

The invention relates to the technical field of high temperature resistance tests of foamed silicone rubber based on data identification, and discloses a high temperature resistance test method of foamed silicone rubber based on data identification. The method comprises the steps of system parameter setting, test device building, short pulse excitation sampling, frequency domain response extraction, temperature step length setting, main frequency statistical analysis, normalized frequency shift ratio and second-order differential acceleration calculation, failure judgment, retest confirmation and the like. By constructing a complete data-driven test process, the problems that a traditional thermal failure test is low in efficiency, depends on subjective experience, and failure points are difficult to accurately judge are solved. A non-destructive spectrum analysis method is adopted to improve test sensitivity and repeatability, a datamation judgment mode enhances objectivity and automation degree, a retest mechanism is introduced to guarantee judgment reliability, and a structured test report is finally generated.

Description

Technical Field

[0001] The present invention relates to the technical field of high-temperature resistance testing of foamed silicone rubber based on data identification, and in particular to a high-temperature resistance testing method of foamed silicone rubber based on data identification. Background Art

[0002] Due to its excellent heat-insulating and mechanically cushioning properties, foamed silicone rubber is widely used in aerospace, automotive electronics, and high-temperature sealing applications. Accurately assessing the failure temperature of foamed silicone rubber in high-temperature environments is crucial for optimizing material formulations, predicting service life, and ensuring reliability. Existing methods for evaluating high-temperature performance primarily rely on thermogravimetric analysis, differential scanning calorimetry, and traditional dynamic mechanical analysis, with a few experimental techniques based on acoustic emission or infrared thermography also available.

[0003] Traditional thermogravimetric analysis (TGA) techniques identify the thermal decomposition or weight loss inflection point of a material by measuring the mass-temperature curve of a sample. While this technique can provide a rough estimate of the thermal decomposition temperature, the discrete nature of the heating rate and mass change results in inaccurate failure temperature determination. Furthermore, this method only captures macroscopic weight loss characteristics and fails to reflect changes in the material's internal microstructure. Differential scanning calorimetry (DSC) focuses on detecting peaks in heat release or endothermic activity. However, for porous, multi-interface systems like foamed silicone rubber, the heat flow signal is often complex, with overlapping peaks and susceptible to environmental interference, resulting in unstable results. Dynamic mechanical analysis (DMAC) measures the elastic modulus and loss modulus of a material at different temperatures to characterize mechanical property degradation. However, this technique requires complex experimental setup, a lengthy measurement process, and sensitive amplitude control. Furthermore, it requires high uniformity of the sample temperature field, making it difficult to detect failure points in real time. Recent studies have also attempted to use acoustic emission technology to monitor microcracks or pore growth at high temperatures, indirectly determining failure through spectral analysis of the acoustic emission signal. However, acoustic emission signals are significantly affected by device coupling, external noise, and specimen geometry, making stable spectral feature extraction difficult. Traditional spectral differentiation or threshold determination methods often rely on empirical threshold settings and lack a unified, repeatable mathematical determination model. Other researchers have used infrared imaging to perform time-series analysis of surface temperature fields and radiation intensity to identify thermal runaway or chemical decomposition. However, this method places extremely high demands on infrared detector resolution and background radiation control, and is significantly affected by device calibration drift, making it difficult to apply in industrial settings. Furthermore, while existing high-temperature performance assessment schemes based on resonant frequency detection can capture changes in internal structural stiffness through a stimulus-response approach, they often rely on single-point or small-point experiments with large temperature step sizes. The extraction and determination of the main frequency primarily relies on the absolute value or first-order derivative of the maximum amplitude peak, failing to fully utilize the second-order derivative of the frequency shift with respect to temperature, known as "acceleration." This results in limited resolution for failure temperature localization and susceptibility to noise, leading to misjudgments. Furthermore, existing data processing techniques lack a mechanism for statistically adaptive threshold calculation within the initial fluctuation range. Instead, they often rely on empirical settings or single-peak comparisons, making it difficult to balance detection sensitivity with interference immunity.

[0004] To this end, this case aims to propose a data-recognition-based high-temperature resistance testing method for foamed silicone rubber. This method automatically identifies the failure temperature by dynamically analyzing the resonant frequency response of the foamed silicone rubber at different temperatures. This method incorporates multiple frequency-domain analysis techniques, such as normalized frequency shift, second-order frequency differential acceleration, and a statistical threshold model. This method extracts the dominant frequency variation trend from measurements at multiple temperature points, uses statistical methods to identify spectral mutation points, and determines the material failure location. A retest mechanism enhances the reliability of the results. Summary of the Invention

[0005] The present invention provides a high-temperature resistance test method for foamed silicone rubber based on data recognition, which helps solve the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a high temperature resistance test method for foamed silicone rubber based on data recognition, comprising: Determine the maximum resonant frequency, sampling frequency, frequency resolution, sampling window length, and number of sampling points, and configure a test device consisting of a piezoelectric transducer, thermocouple, and rigid bracket; The time domain response signal is collected by multiple short pulse excitations, the frequency domain response is calculated and the main resonant frequency is extracted, and the frequency mean and standard deviation are statistically calculated; Set the temperature step and maximum test temperature, determine the total number of measurement points, and raise the temperature point by point to the target temperature. After the temperature stabilizes, collect the response. Repeat the excitation at each measuring point, record the response, calculate the frequency domain data and calculate the mean and variance of the main resonant frequency; Calculate the normalized frequency shift ratio based on the main frequency changes of each measuring point; Based on the frequency response sequence, calculate the second-order differential acceleration of the frequency change between adjacent measuring points; Select the damage-free fluctuation range, calculate its second-order difference mean and standard deviation, set the judgment threshold and identify the failure temperature exceeding the threshold; Set retest temperature points near the identified failure temperature points, resample and calculate, verify the consistency of the judgment, and generate the final test report.

[0007] Optionally, the steps of determining the maximum resonant frequency, sampling frequency, frequency resolution, sampling window length, and number of sampling points, and configuring a test device consisting of a piezoelectric transducer, a thermocouple, and a rigid bracket specifically include: Assume the maximum resonant frequency is , so the sampling frequency is ; Assume the required frequency resolution is , then the sampling window length is ; Calculate the number of sampling points ;in, is the ceiling function; Take a piece of foamed silicone rubber sample, adhere a piezoelectric transducer to one end of the sample, and fix the other end to a rigid bracket; A thermocouple is mounted at the center of the sample to ensure accurate temperature measurement. .

[0008] Optionally, the step of collecting a time domain response signal by multiple short pulse excitations, calculating a frequency domain response and extracting a main resonant frequency, and performing statistical analysis of a frequency mean and standard deviation may specifically include: At room temperature Next, proceed Short pulse excitation, record the Secondary time domain response signal ; Each response does , calculate the Sub-frequency domain response ; Extract sub-primary resonant frequency ; Calculate the mean baseline frequency ; Calculate baseline frequency standard deviation ; Set the baseline frequency mean , set the baseline frequency shift ratio .

[0009] Optionally, the temperature step size and maximum test temperature are set, the total number of measurement points is determined, and the temperature is raised point by point to the target temperature, and response acquisition is performed after the temperature stabilizes, specifically including: Set the temperature step ; Assume the maximum test temperature is , total number of measurement points ; For the first Target temperature of secondary measuring point The temperature is raised; wherein, is the index of the measuring point; When the thermocouple reading meets When , keep the sampling window length Response collection is performed; wherein, is the actual measured temperature.

[0010] Optionally, the step of repeatedly exciting at each measuring point, recording the response, calculating the frequency domain data, and counting the mean and variance of the main resonant frequency specifically includes: For each measuring point Perform the following steps: S101, repeat the stimulation, record the Measuring point Secondary time domain response signal ; S102, each response to do , calculate the Measuring point Sub-frequency domain response : ; S103, extract Measuring point sub-primary resonant frequency ; S104, calculate the Measurement point frequency mean ; S105, calculate the Measurement point frequency variance .

[0011] Optionally, calculating the normalized frequency shift ratio based on the change of the main frequency of each measuring point specifically includes: For each , calculate the The normalized frequency shift ratio is calculated based on the main frequency changes of each measuring point. .

[0012] Optionally, the step of calculating the second-order differential acceleration of frequency changes between adjacent measuring points based on the frequency response sequence specifically includes: For each , calculate the The frequency shift of the measuring point is based on the frequency response sequence, and the second-order differential acceleration of the frequency change between adjacent measuring points is calculated : .

[0013] Optionally, selecting a damage-free fluctuation interval, calculating its second-order difference mean and standard deviation, setting a determination threshold, and identifying a failure temperature exceeding the threshold specifically includes: Take the damage-free fluctuation interval index , ;in, is the end point index of the initial statistical interval; Calculate the initial interval second-order difference mean ; Calculate the standard deviation of the second-order difference of the initial interval ; Set the decision threshold ; since From the beginning, the first satisfaction season , and terminate the detection; among them, is the identified failure temperature.

[0014] Optionally, setting a retest temperature point near the identified failure temperature point, resampling and calculating, verifying the consistency of the determination, and generating a final test report specifically includes: Get the identified failure temperature index , corresponding to temperature ; Set three points for re-testing temperature: ; For each temperature point, Sub-stimulus sampling and calculation and ; Compute the second-order difference of three points ; Verification conditions: , , ; If you test again and still have multiple If it is judged as failure, each identification temperature is recorded as a set: ; Calculate the minimum value With the maximum value : , ; Assume the uncertainty interval is ; Build a table listing each measurement point of: ; Mark the failure point with an asterisk .

[0015] The present invention has the following beneficial effects: 1. Establish a reasonable sampling model through the intrinsic mathematical relationship between parameters such as resonant frequency, sampling frequency, and frequency resolution to ensure that the FFT result has the required resolution and meets the Nyquist sampling conditions, thereby improving the accuracy of subsequent signal processing from the source. At the same time, combined with the physical layout logic of the piezoelectric transducer and the rigid bracket, the excitation and response paths are ensured to be symmetrical and stable, so that the test system has both engineering feasibility and structural response consistency. Compared with existing thermal test technologies, traditional methods rely on thermal deformation or stress analysis. This solution uses frequency variables to characterize stiffness degradation, avoiding high-power stress loading or irreversible damage, and is particularly suitable for micro-damage monitoring of flexible and foam-like polymer materials.

[0016] 2. A "room temperature baseline" spectral distribution model was established. Through multiple short pulse excitations, steady-state statistics of the material's natural frequency were achieved, and the natural fluctuation range was characterized by the mean and standard deviation, providing a "normalized" reference benchmark for determining subsequent excursions during temperature rise. This method combines theoretical support with data stability. Traditional approaches often ignore the material's spectral fluctuations in its initial state. This approach incorporates these into the statistical model, effectively distinguishing "natural fluctuations" from sudden changes caused by "material failure," thereby increasing the confidence level of failure identification.

[0017] 3. By controlling the temperature step size, the frequency variation collected at each temperature point is made greater than one order of magnitude of the baseline frequency shift standard deviation, thereby achieving the distinguishability of temperature changes at the frequency level. This "differential identifiability control" strategy improves the sensitivity and temperature matching of spectral dynamic analysis. Unlike existing temperature rise loading methods that mostly use 10°C units, this method can dynamically adjust the step size based on the material response, providing adjustability and analytical flexibility, and better meeting the demand for refined temperature rise resolution in engineering scenarios.

[0018] 4. Performing multiple excitations at each measurement point, recording responses, and performing frequency-domain statistics are key to this method's noise resistance. Compared to single spectrum acquisition, the calculation of average frequency and variance provides confidence assessment, preventing single anomalies from misleading overall conclusions. Multiple acquisitions form a spectrum cloud map, and central features are statistically extracted, increasing the stability of the frequency-shifted signal. Compared to the existing "single-point sampling + single-curve inference" approach, this solution more closely integrates signal processing and statistics, significantly improving the reliability of the results.

[0019] 5. The proposed method of calculating the normalized frequency shift ratio using the baseline frequency as a reference represents a significant breakthrough in signal standardization. Its significance lies in eliminating the interference of initial frequency differences between samples on experimental results, making the results of different samples and different runs comparable. This frequency-domain normalization technique fills the gap in the existing lack of a relative scaling system in polymer thermal testing and has great potential for application in batch consistency verification of industrial products.

[0020] 6. Taking a second-order difference of the frequency shift sequence, using the frequency change as an identification indicator, simulates a "frequency shift acceleration" response, enhancing sensitivity to identifying sudden changes in material properties. Finite-difference approximation of mathematical derivatives captures slope changes after small fluctuations, providing a deeper structural understanding of spectral response behavior. Compared to directly observing frequency mutations, second-order differences can capture "acceleration-like changes," making them suitable for capturing the critical point from slow aging to instantaneous failure. This represents a highly promising technical approach for polymer thermal failure monitoring.

[0021] 7. For failure temperature identification, the introduction of a judgment model based on the mean + standard deviation × n of the "damage-free range" represents a data-driven statistical innovation. Essentially, this model transforms normal material behavior patterns into judgment criteria, bypassing the threshold of human experience and providing an automated, quantifiable method for failure identification. Compared to the traditional "subjective judgment + set safety temperature" approach, this solution establishes an objective identification mechanism based on data distribution, which is more replicable and algorithmically embeddable, laying the foundation for intelligent, high-throughput material testing.

[0022] 8. Introducing a retesting mechanism and uncertainty analysis. Identified failure points are retested and their consistency determined to avoid erroneous conclusions due to measurement errors or accidental disturbances. Upper and lower limits are set to form a confidence band for the failure temperature. This data-revalidation-based strategy ensures that the experimental results have greater scientific validity and engineering reliability. Furthermore, report structure optimization methods such as data annotation and asterisk marking are introduced to ensure that the final test report is not only clear in results but also fully auditable, facilitating engineering applications and third-party replication. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] Example, see Figure 1 , a high temperature resistance test method for foamed silicone rubber based on data recognition, comprising: Determine the maximum resonant frequency, sampling frequency, frequency resolution, sampling window length, and number of sampling points, and configure a test device consisting of a piezoelectric transducer, thermocouple, and rigid bracket; The time domain response signal is collected by multiple short pulse excitations, the frequency domain response is calculated and the main resonant frequency is extracted, and the frequency mean and standard deviation are statistically calculated; Set the temperature step and maximum test temperature, determine the total number of measurement points, and raise the temperature point by point to the target temperature. After the temperature stabilizes, collect the response. Repeat the excitation at each measuring point, record the response, calculate the frequency domain data and calculate the mean and variance of the main resonant frequency; Calculate the normalized frequency shift ratio based on the main frequency changes of each measuring point; Based on the frequency response sequence, calculate the second-order differential acceleration of the frequency change between adjacent measuring points; Select the damage-free fluctuation range, calculate its second-order difference mean and standard deviation, set the judgment threshold and identify the failure temperature exceeding the threshold; Set retest temperature points near the identified failure temperature points, resample and calculate, verify the consistency of the judgment, and generate the final test report.

[0026] A high-temperature resistance test method for foamed silicone rubber based on data identification was proposed, and a complete test process was constructed, including system parameter setting, response acquisition, frequency domain analysis, normalization processing, acceleration calculation, statistical judgment and retest confirmation. Through these steps, the problems of low test efficiency, strong subjectivity, and difficulty in accurately defining the failure point in traditional thermal failure testing were effectively solved. The use of frequency domain signal recognition avoids damage to the material; failure judgment is performed through digital means, which has the advantages of automation, strong objectivity, and high repeatability; the introduction of a retest mechanism improves the credibility of the results; and a structured test report is formed to facilitate subsequent review or quality tracking management. Overall, a standardized, highly sensitive, non-destructive thermal stability testing system has been constructed, which is particularly suitable for high-temperature performance research scenarios of flexible and foam materials.

[0027] The method of determining the maximum resonant frequency, sampling frequency, frequency resolution, sampling time window length, and number of sampling points, and configuring a test device consisting of a piezoelectric transducer, a thermocouple, and a rigid bracket specifically includes: Assume the maximum resonant frequency is , determine the upper limit of the possible maximum resonant frequency of the sample to guide the subsequent selection of sampling frequency; thus, the sampling frequency is taken as ; Satisfy Nyquist sampling theorem and avoid spectrum aliasing; Assume the required frequency resolution is , set the minimum identifiable frequency interval required for frequency domain analysis; then the sampling window length is ; Ensure that the FFT resolution meets ; Calculate the number of sampling points ;in, is the rounding function; calculate the required number of sampling points; Take a piece of foamed silicone rubber sample, adhere a piezoelectric transducer to one end of the sample, and fix the other end to a rigid bracket to ensure consistent coupling between the excitation and response; A thermocouple is mounted at the center of the sample to ensure accurate temperature measurement. ; High-precision record of sample temperature to ensure stable temperature conditions.

[0028] The key is to clearly define the frequency parameter system (maximum resonant frequency, sampling frequency, number of sampling points, etc.) and the physical test device configuration (piezoelectric transducer, thermocouple, rigid bracket, etc.) that the test relies on. This step effectively resolves problems such as spectral aliasing, insufficient sampling, and inaccurate temperature monitoring in previous tests. The sampling frequency is set according to the Nyquist criterion based on the maximum resonant frequency to avoid frequency folding; the number of sampling points ensures that the FFT has the required frequency resolution, improving the accuracy of spectrum analysis; the piezoelectric transducer and rigid bracket structure ensure clear excitation and response paths, and the thermocouple mounting position is rationally designed to ensure that temperature measurements truly reflect the core temperature of the specimen. This provides a set of test hardware and parameter design specifications with a rigorous theoretical foundation, ensuring the accuracy, consistency, and physical repeatability of subsequent test data, providing a foundational guarantee for the entire frequency-domain failure identification system.

[0029] The method includes collecting time domain response signals by multiple short pulse excitations, calculating frequency domain response and extracting main resonant frequency, and calculating frequency mean and standard deviation, specifically including: At room temperature Next, proceed Short pulse excitation, record the Secondary time domain response signal ; Collect enough samples and calculate the baseline frequency distribution; Each response does , calculate the Sub-frequency domain response ;Convert the time domain signal to the frequency domain; Extract sub-primary resonant frequency ; Locate the main resonant frequency corresponding to the spectrum peak; Calculate the mean baseline frequency ; Calculate baseline frequency standard deviation ; Obtain the mean and standard deviation of the baseline frequency to provide a basis for normalization and threshold setting; Set the baseline frequency mean , set the baseline frequency shift ratio ; Determine the normalized base frequency and initial ratio.

[0030] By collecting the frequency domain response at room temperature through multiple short pulse excitations and calculating the main frequency mean and standard deviation, a "baseline spectrum model" of the material is established. This step solves the problem of the lack of an initial benchmark in traditional methods, which makes it impossible to quantify subsequent temperature rise spectrum changes. By calculating the frequency mean and standard deviation of a large number of samples, a reference system for normalization processing and a noise boundary for the judgment model are constructed, providing a quantitative basis for subsequent frequency shifts and abnormal changes. Improving the sensitivity of frequency shift detection can distinguish subtle thermal effect changes; providing an error tolerance basis, enhancing the stability and anti-interference ability of the judgment model; and providing basic data support for failure threshold calculation, so that the entire data processing process has a structured and mathematical support.

[0031] The temperature step size and maximum test temperature are set, the total number of test points is determined, and the temperature is raised point by point to the target temperature. After the temperature stabilizes, response acquisition is performed, specifically including: Set the temperature step ; Ensure that each frequency shift exceeds one tenth of the baseline standard deviation and can be reliably resolved; Assume the maximum test temperature is , total number of measurement points ;The number of measurement points required to cover the target temperature range; For the first Target temperature of secondary measuring point The temperature is raised; wherein, is the index of the measuring point; When the thermocouple reading meets When , keep the sampling window length Response collection is performed; wherein, To obtain the actual temperature, ensure that data is collected under stable target temperature conditions to eliminate the impact of temperature drift.

[0032] By setting a reasonable temperature step size and target maximum temperature, and collecting response data in a distributed manner, we can solve the problems of uneven distribution of spectrum sampling points and significant interference of temperature fluctuations on the results under traditional continuous temperature rise. In particular, the step size is set to 1 / 10 of the standard deviation of the standard reference baseline frequency as the discrimination requirement to ensure that the frequency changes caused by each temperature rise step can be significantly detected, thereby improving the analysis resolution and stability. The controllability of the spectrum response under temperature control is enhanced, making the frequency shift trend clear and monotonic; achieving uniform coverage of measurement points in the temperature space, which is conducive to constructing a complete temperature-frequency response curve; ensuring that data is collected after the target temperature stabilizes, effectively eliminating transient interference caused by temperature disturbances and ensuring signal quality.

[0033] Repeating the excitation at each measuring point, recording the response, calculating the frequency domain data, and counting the mean and variance of the main resonant frequency specifically includes: For each measuring point Perform the following steps: S101, repeat the stimulation, record the Measuring point Secondary time domain response signal ; Get multiple response samples at the current temperature; S102, each response to do , calculate the Measuring point Sub-frequency domain response : ;Convert to frequency domain to facilitate frequency extraction; S103, extract Measuring point sub-primary resonant frequency ;Identify the main resonant frequency; S104, calculate the Measurement point frequency mean ; S105, calculate the Measurement point frequency variance ; Calculate the current temperature frequency mean and variance to evaluate measurement consistency.

[0034] Multiple excitations and frequency domain response calculations are performed at each temperature point, and the frequency mean and variance are calculated to enhance data stability. This step effectively addresses the problem of single-shot acquisition being significantly affected by random noise and resulting in unstable results in traditional methods. Repeated sampling forms a frequency distribution cloud map, from which stable eigenvalues ​​are extracted, thereby avoiding the risk of occasional perturbations misleading failure judgments. This improves the accuracy of frequency trend judgments, provides high-quality input data for subsequent normalization and acceleration calculations, and, through the combined control of mean and variance, enables dynamic identification of data outliers, providing a sound basis for identifying the nonlinear responses of complex materials.

[0035] The calculation of the normalized frequency shift ratio based on the main frequency change of each measuring point specifically includes: For each , calculate the The normalized frequency shift ratio is calculated based on the main frequency changes of each measuring point. ; Eliminate absolute frequency differences and quantify frequency shifts into relative ratios.

[0036] By calculating the normalized frequency shift ratio for each measurement point, the original frequency change is standardized, solving the problem of interference from fundamental frequency differences between different samples or batches on the overall judgment result. This improves the consistency and comparability of cross-sample comparisons, facilitating standardized judgments in production or quality control. Normalization can effectively reduce the deviation of "absolute frequency" affected by non-temperature factors such as structural dimensions and boundary conditions, making the frequency shift change more focused on the causal relationship between "temperature and performance." This provides support for the unification of scales and simplification of model structures in subsequent differential and failure judgment algorithms, improving the versatility and mathematical interpretability of data models.

[0037] The method of calculating the second-order differential acceleration of the frequency change between adjacent measuring points based on the frequency response sequence specifically includes: For each , calculate the The frequency shift of the measuring point is based on the frequency response sequence, and the second-order differential acceleration of the frequency change between adjacent measuring points is calculated : ; The second-order derivative of the frequency shift with respect to temperature is approximated by finite differences to reflect the sudden change in frequency shift acceleration.

[0038] The second-order differential acceleration of the frequency shift sequence is introduced as an identification variable, which is essentially a finite difference calculation of the second-order derivative of the normalized frequency shift ratio. This method breaks through the "linear assumption" of single frequency changes and can more keenly identify mutation points, inflection points and nonlinear variation characteristics, thus solving the problem of insufficient identification of sudden failure responses of materials by existing methods. Through acceleration measurement, the "mutation trend" of the frequency response can be accurately detected as a signal of the material's transition from steady state to instability; acceleration, as a higher-order statistical feature, is more suitable for clustering or anomaly identification of multiple samples, improving the recognition rate and robustness of the intelligent judgment model; providing more significant discriminant features for subsequent statistical threshold judgment, significantly reducing the false alarm rate.

[0039] The process of selecting a damage-free fluctuation range, calculating its second-order difference mean and standard deviation, setting a judgment threshold, and identifying failure temperatures exceeding the threshold specifically includes: Take the damage-free fluctuation interval index , ;in, The index of the end point of the initial statistical interval is used to determine the damage-free fluctuation interval for statistical threshold calculation; Calculate the initial interval second-order difference mean ; Calculate the standard deviation of the second-order difference of the initial interval ; Set the decision threshold ; Based on the initial interval statistical acceleration distribution, three times the standard deviation is set as the failure judgment threshold; since From the beginning, the first satisfaction season , and terminate the detection; among them, The identified failure temperature; the material failure temperature is identified at the acceleration mutation point.

[0040] A statistical standard for the "damage-free fluctuation range" was established, and the mean and standard deviation of the second-order acceleration were used to calculate this standard. This dynamic threshold was then used to identify the failure temperature corresponding to the spectral mutation point. This sample distribution-based threshold strategy addresses the poor adaptability and low sensitivity of the traditional "manually set fixed threshold" approach. Dynamic judgment rules were constructed based on real-world response data, automatically adapting to different materials or test conditions. Statistical discrimination strategies were utilized to make the failure point identification process more objective and robust, making it particularly suitable for embedded applications in automated systems. A "statistical confidence interval" set at three times the standard deviation effectively controlled the risk of misjudgment, improving specificity while maintaining sensitivity.

[0041] The process of setting a retest temperature point near the identified failure temperature point, resampling and calculating, verifying the consistency of the determination, and generating a final test report specifically includes: Get the identified failure temperature index , corresponding to temperature ; Set three points for re-testing temperature: ; For each temperature point, Sub-stimulus sampling and calculation and ; Verify only Failure signals appear at the location rather than misjudgment due to measurement fluctuations; Compute the second-order difference of three points ; Verification conditions: , , ; Accurately filter out measurement noise, only show acceleration mutation at a single temperature point, confirm that this point is the true failure temperature; If you test again and still have multiple If it is judged as failure, each identification temperature is recorded as a set: ; Calculate the minimum value With the maximum value : , ; Assume the uncertainty interval is ; Quantify the failure temperature uncertainty caused by retest fluctuations; Build a table listing each measurement point of: ; Mark the failure point with an asterisk ; All measurement point data are fully presented for easy review and archiving.

[0042] It is proposed to retest the temperature of the identified failure points at three points, verify the consistency of the failure judgment through resampling, and output the uncertainty range of the failure temperature. This mechanism solves the problem of fuzzy boundary point judgment and poor repeatability in traditional methods. The retest mechanism eliminates misjudgments caused by "occasional anomalies" or measurement noise, thereby improving the credibility of the results; the "uncertainty range" formed by the upper and lower limit values ​​provides a more engineering-valuable data reference for applications such as product quality analysis and process control; the final data report displays the frequency shift, differential, failure mark and other information of each measurement point in a structured manner, making the results easy to track, trace, reproduce and compare, and has good industrial and engineering adaptability.

[0043] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0044] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A high temperature resistance test method for foamed silicone rubber based on data recognition, characterized in that: include: Determine the maximum resonant frequency, sampling frequency, frequency resolution, sampling window length, and number of sampling points, and configure a test device consisting of a piezoelectric transducer, thermocouple, and rigid bracket; The time domain response signal is collected by multiple short pulse excitations, the frequency domain response is calculated and the main resonant frequency is extracted, and the frequency mean and standard deviation are statistically calculated; Set the temperature step and maximum test temperature, determine the total number of measurement points, and raise the temperature point by point to the target temperature. After the temperature stabilizes, collect the response. Repeat the excitation at each measuring point, record the response, calculate the frequency domain data and calculate the mean and variance of the main resonant frequency; Calculate the normalized frequency shift ratio based on the main frequency changes of each measuring point; Based on the frequency response sequence, calculate the second-order differential acceleration of the frequency change between adjacent measuring points; Select the damage-free fluctuation range, calculate its second-order difference mean and standard deviation, set the judgment threshold and identify the failure temperature exceeding the threshold; Set retest temperature points near the identified failure temperature points, resample and calculate, verify the consistency of the judgment, and generate the final test report.

2. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 1, characterized in that: The method of determining the maximum resonant frequency, sampling frequency, frequency resolution, sampling time window length, and number of sampling points, and configuring a test device consisting of a piezoelectric transducer, a thermocouple, and a rigid bracket specifically includes: Assume the maximum resonant frequency is , so the sampling frequency is ; Assume the required frequency resolution is , then the sampling window length is ; Calculate the number of sampling points ;in, is the ceiling function; Take a piece of foamed silicone rubber sample, adhere a piezoelectric transducer to one end of the sample, and fix the other end to a rigid bracket; A thermocouple is mounted at the center of the sample to ensure accurate temperature measurement. .

3. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 2, characterized in that: The method includes collecting time domain response signals by multiple short pulse excitations, calculating frequency domain response and extracting main resonant frequency, and calculating frequency mean and standard deviation, specifically including: At room temperature Next, proceed Short pulse excitation, record the Secondary time domain response signal ; Each response does , calculate the Sub-frequency domain response ; Extract sub-primary resonant frequency ; Calculate the mean baseline frequency ; Calculate baseline frequency standard deviation ; Set the baseline frequency mean , set the baseline frequency shift ratio .

4. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 3, characterized in that: The temperature step size and maximum test temperature are set, the total number of test points is determined, and the temperature is raised point by point to the target temperature. After the temperature stabilizes, response acquisition is performed, specifically including: Set the temperature step ; Assume the maximum test temperature is , total number of measurement points ; For the first Target temperature of secondary measuring point The temperature is raised; wherein, is the index of the measuring point; When the thermocouple reading meets When , keep the sampling window length Response collection is performed; wherein, is the actual measured temperature.

5. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 4, characterized in that: Repeating the excitation at each measuring point, recording the response, calculating the frequency domain data, and counting the mean and variance of the main resonant frequency specifically includes: For each measuring point Perform the following steps: S101, repeat the stimulation, record the Measuring point Secondary time domain response signal ; S102, each response to do , calculate the Measuring point Sub-frequency domain response : ； S103, extract Measuring point sub-primary resonant frequency ; S104, calculate the Measurement point frequency mean ; S105, calculate the Measurement point frequency variance .

6. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 5, characterized in that: The calculation of the normalized frequency shift ratio based on the main frequency change of each measuring point specifically includes: For each , calculate the The normalized frequency shift ratio is calculated based on the main frequency changes of each measuring point. .

7. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 6, characterized in that: The method of calculating the second-order differential acceleration of the frequency change between adjacent measuring points based on the frequency response sequence specifically includes: For each , calculate the The frequency shift of the measuring point is based on the frequency response sequence, and the second-order differential acceleration of the frequency change between adjacent measuring points is calculated : 。 8. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 7, characterized in that: The process of selecting a damage-free fluctuation range, calculating its second-order difference mean and standard deviation, setting a judgment threshold, and identifying failure temperatures exceeding the threshold specifically includes: Take the damage-free fluctuation interval index , ;in, is the end point index of the initial statistical interval; Calculate the initial interval second-order difference mean ; Calculate the standard deviation of the second-order difference of the initial interval ; Set the decision threshold ; since From the beginning, the first satisfaction season , and terminate the detection; among them, is the identified failure temperature.

9. A high temperature resistance test method for foamed silicone rubber based on data recognition according to claim 8, characterized in that: The process of setting a retest temperature point near the identified failure temperature point, resampling and calculating, verifying the consistency of the determination, and generating a final test report specifically includes: Get the identified failure temperature index , corresponding to temperature ; Set three points for re-testing temperature: ; For each temperature point, Sub-stimulus sampling and calculation and ; Compute the second-order difference of three points ; Verification conditions: , , ; If you test again and still have multiple If it is judged as failure, each identification temperature is recorded as a set: ； Calculate the minimum value With the maximum value : ， ； Assume the uncertainty interval is ; Build a table listing each measurement point of: ; Mark the failure point with an asterisk .

Images