A system for testing the operating performance of a quartz heater
By constructing a data-driven extreme test process generation architecture for quartz heaters under normal operating conditions, and using deep learning models for consistency assessment and risk quantification, the problem of risk control and intelligent test process planning under extreme operating conditions in batch testing of quartz heaters is solved, achieving efficient and accurate quality control and reliability assessment.
Patent Information
- Application Number
- CN202510623893.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-15
- Publication Date
- 2025-12-12
- Estimated Expiration
- 2045-05-15
AI Technical Summary
Existing technologies have systematic shortcomings in batch testing of quartz heaters, especially in the lack of effective means for risk control under extreme conditions and intelligent planning of test processes, which leads to the loss of low-risk condition data due to premature sample damage.
An "architecture for generating extreme test processes driven by normal operating condition data" was constructed. Through a batch normal operating condition data set generation module, an extreme reference batch analysis module, and an extreme operating condition performance test process generation module, a deep learning model is used to perform consistency assessment and risk quantification, generating test processes that prioritize low risk and postpone destructive actions.
It significantly reduces sample waste and data distortion caused by improper testing sequence, provides efficient and accurate quality control throughout the entire life cycle of quartz heaters, ensures that the testing process conforms to the actual failure path, and improves the efficiency of process optimization on the production side and reliability assessment on the client side.
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Figure CN120539505B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of quartz heater performance detection, more particularly, it relates to a running performance test system for quartz heater. BACKGROUND
[0002] As the core component in the fields of industrial heating, semiconductor manufacturing, medical equipment, etc., the running performance (such as temperature uniformity, power stability, and extreme environment adaptability) of quartz heater directly affects the safety and efficiency of the terminal equipment. Although the existing test technology can complete performance detection under single working condition, it has significant deficiencies in systematic batch testing, risk control under extreme working condition, and intelligent planning of test process.
[0003] Since the quartz heater will not be irreversibly damaged when performing running performance test under ordinary working condition, the quartz heater is usually arranged for running performance test under ordinary working condition first. After the test under ordinary working condition is completed, the quartz heater is usually tested under extreme working condition by using a "random loading" or "severe working condition first" strategy, which is easy to cause the loss of low-risk working condition data due to early damage of samples (such as vibration working condition after super-high temperature test).
[0004] With the development of quartz heater towards high precision and high reliability, there is an urgent need for a systematic solution integrating batch consistency intelligent evaluation, extreme risk quantification analysis, and test process dynamic optimization. SUMMARY
[0005] In view of the deficiencies of the prior art, the purpose of the present application is to provide a running performance test system for quartz heater. The present application solves the core problems of batch screening, risk control, and process planning of the prior art by constructing an "ordinary working condition data driven extreme test process generation architecture", and provides an efficient technical path for the whole life cycle quality control of quartz heater.
[0006] To achieve the above purpose, the present application provides the following technical scheme:
[0007] A running performance test system for quartz heater, comprising
[0008] A batch ordinary working condition data set generation module, a plurality of quartz heaters are extracted from each batch of quartz heaters according to the sampling ratio, and the ordinary working condition data set of each quartz heater is obtained;
[0009] An extreme reference batch analysis module determines the previous extreme reference batch;
[0010] The extreme working condition operation performance test flow generation module determines the extreme working condition operation performance test flow of the batch of quartz heaters based on all the extreme reference batches, and sequentially performs operation performance tests of various extreme working conditions on the batch of quartz heaters according to the extreme working condition operation performance test flow.
[0011] Further, the specific way of obtaining the ordinary working condition data set of the quartz heater is: selecting a quartz heater, obtaining the corresponding test data of the quartz heater under the operation performance test of each ordinary working condition, and integrating all the test data into an ordinary working condition data set.
[0012] Further, the extreme reference batch before determination: determining the ordinary working condition consistency index of each batch before determination, setting an ordinary working condition consistency threshold index, and marking the corresponding batch as an extreme reference batch when the ordinary working condition consistency index of a batch is greater than or equal to the ordinary working condition consistency threshold index.
[0013] Further, the specific way of determining the ordinary working condition consistency index of a batch is: selecting a batch before determination, marking each quartz heater extracted from the batch as a comparison quartz heater, obtaining the target batch comparison index of each comparison quartz heater, setting a target batch comparison high index and a target batch comparison low index, marking the corresponding comparison quartz heater as a prominent consistent heater when the target batch comparison index of the comparison quartz heater is greater than or equal to the target batch comparison high index, marking the corresponding comparison quartz heater as a prominent different heater when the target batch comparison index of the comparison quartz heater is less than or equal to the target batch comparison low index, marking the corresponding comparison quartz heater as a boundary defined heater when the target batch comparison low index is less than the target batch comparison index of the comparison quartz heater and the target batch comparison high index, marking the number of prominent consistent heaters as a prominent consistent number, marking the number of prominent different heaters as a prominent different number, calculating the sum of the target batch comparison indexes of all boundary defined heaters and taking the average value to calculate a boundary comparison index, and calculating the ordinary working condition consistency index of the batch of quartz heaters based on the prominent consistent number, the prominent different number, and the boundary comparison index.
[0014] Further, the specific way of obtaining the target batch comparison index of the comparison quartz heater is: selecting a comparison quartz heater, marking the ordinary working condition data set of each quartz heater extracted from the current batch as a target quartz heater, matching the comparison quartz heater and each target quartz heater as a device comparison combination, obtaining the individual comparison index of each device comparison combination, calculating the sum of the individual comparison indexes of each device comparison combination and taking the average value to calculate the target batch comparison index of the comparison quartz heater.
[0015] Further, the specific way of obtaining the single comparison index of the device comparison combination: obtaining the common working condition data set of the comparison quartz heater and the common working condition data set of the target quartz heater in the device comparison combination, and importing the two common working condition data sets into the common working condition set comparison model, and the common working condition set comparison model exports the single comparison index of the device comparison combination.
[0016] Further, the extreme working condition operation performance test flow of the batch quartz heater is determined based on all the extreme reference batches: the extreme working condition damage index of each extreme working condition corresponding to each extreme reference batch is obtained, and then the extreme working condition flow index of each extreme working condition is determined, the extreme working conditions are sequentially sorted based on the numerical value of the extreme working condition flow index from small to large, and the extreme working condition operation performance test flow is determined according to the sorting result.
[0017] Further, the specific way of obtaining the extreme working condition damage index of one extreme reference batch corresponding to one extreme working condition: one extreme reference batch is selected, and one extreme working condition is selected, each quartz heater in the extreme reference batch is determined, the extreme test data of each quartz heater for the extreme working condition is obtained, each extreme test data is sequentially imported into the extreme damage model corresponding to the extreme working condition, the single damage index of each quartz heater is sequentially determined, the single damage indexes of all the quartz heaters are calculated by summing and taking the average value, and the extreme working condition damage index of the extreme reference batch corresponding to the extreme working condition is calculated.
[0018] Further, the specific way of determining the extreme working condition flow index of one extreme working condition: one extreme working condition is selected, the extreme working condition damage indexes of each extreme reference batch corresponding to the extreme working condition are calculated by summing and taking the average value, and the extreme working condition damage average index PR is calculated, when the extreme working condition and n extreme working conditions have a direct relationship in the extreme working condition relationship tree, the extreme working condition mechanism coupling number n is obtained, and the extreme working condition flow index of the extreme working condition is calculated by .
[0019] Compared with the prior art, the present application has the following beneficial effects:
[0020] The system of the application tests the operation performance of the quartz heater in batches, first tests the operation performance of the quartz heater under ordinary working conditions, based on the operation performance test under ordinary working conditions, accurately selects high consistency historical batches as reference based on the consistency evaluation model driven by deep learning, ensures the reliability of subsequent extreme working condition process arrangement, analyzes the coupling of extreme working condition relationship tree and damage mechanism, combines the risk level quantified by the extreme damage model, intelligently generates the test process of "low risk priority, destructive postposition", strictly follows the engineering logic of "non-destructive → destructive, data can be reserved → easy to lose", avoids the interference of high-risk working conditions on low-risk data, makes the test process more suitable for the actual failure path of the quartz heater, significantly reduces the sample waste and data distortion problem caused by improper test order, and provides efficient and accurate technical support for production end process optimization and customer end reliability evaluation. BRIEF DESCRIPTION OF DRAWINGS
[0021] Fig. 1 is a principle block diagram of the application;
[0022] Fig. 2 is a running flowchart of the application. DETAILED DESCRIPTION
[0023] As Figs. 1-2 A running performance test system for a quartz heater, comprising a batch ordinary working condition data set generation module, an extreme reference batch analysis module and an extreme working condition operation performance test flow generation module.
[0024] Batch normal working condition data set generation module, after producing a batch of quartz heaters, a plurality of quartz heaters are extracted from the batch according to the sampling ratio (the sampling ratio is set according to the product characteristics of the quartz heater, the stability of the production process, the industry standard and the regulatory requirements), and the running performance test of each quartz heater is carried out under each normal working condition (the running performance test under normal working condition includes but is not limited to the following items: rated voltage operation item, voltage fluctuation adaptability item, frequency adaptability item, etc., the test conditions and test data corresponding to different items are different, such as the test conditions corresponding to the rated voltage operation item: 100% rated voltage, test data: power deviation rate, test conditions corresponding to the voltage fluctuation adaptability item: rated voltage ± 10%, continuous operation for 30 minutes, test data: power fluctuation rate, test conditions corresponding to the frequency adaptability item: rated frequency ± 5%, continuous operation for 30 minutes, test data: temperature rise time change rate, power deviation rate definition: the deviation percentage of the measured input power and the rated power under rated voltage, used to evaluate the production consistency and design compliance, power fluctuation rate definition: the maximum fluctuation amplitude of the measured power under voltage fluctuation condition (such as rated voltage ± 10%) and the percentage of average power, reflecting the sensitivity of the heater to voltage change, temperature rise time change rate definition: the change percentage of the measured temperature rise time under frequency fluctuation condition and the temperature rise time under rated frequency, evaluating the influence of alternating current power frequency on electromagnetic induction type heater (such as high frequency quartz heating tube)), and obtaining the normal working condition data set of each quartz heater.
[0025] The specific way of obtaining the normal working condition data set of the quartz heater: selecting a quartz heater, obtaining the test data corresponding to each normal working condition running performance test of the quartz heater (each normal working condition running performance test corresponds to a test data, such as the power fluctuation rate corresponding to the voltage fluctuation adaptability item running performance test, and the power deviation rate corresponding to the rated voltage operation item running performance test), and integrating all test data into a normal working condition data set.
[0026] Extreme reference batch analysis module, determine the normal working condition consistency index of each batch, set the normal working condition consistency threshold index (the core of the setting standard of the normal working condition consistency threshold index is to define the "sufficiently consistent" batch through quantitative method, which avoids the interference of extreme test by low consistency batch, and prevents excessive strictness from leading to insufficient sample), when the normal working condition consistency index of a batch ≥ normal working condition consistency threshold index, the corresponding batch is marked as extreme reference batch (when the normal working condition consistency index of a batch < normal working condition consistency threshold index, it is not marked).
[0027] The specific method for determining the normal working condition consistency index of a batch of quartz heaters is as follows: select a batch before the current time, label each quartz heater extracted in the batch as a comparison quartz heater, obtain the target batch comparison index of each comparison quartz heater, set a target batch comparison high index and a target batch comparison low index (the target batch comparison high index is greater than the target batch comparison low index, and the boundary determination criteria of the target batch comparison high index and the target batch comparison low index are determined in combination with data statistical characteristics), when the target batch comparison index of the comparison quartz heater is greater than or equal to the target batch comparison high index, label the corresponding comparison quartz heater as a prominent consistent heater, when the target batch comparison index of the comparison quartz heater is less than or equal to the target batch comparison low index, label the corresponding comparison quartz heater as a prominent different heater, and when the target batch comparison low index is less than the target batch comparison index of the comparison quartz heater and the target batch comparison index of the comparison quartz heater is less than the target batch comparison high index, label the corresponding comparison quartz heater as a boundary defined heater, label the number of prominent consistent heaters as a prominent consistent number PR(a) , label the number of prominent different heaters as a prominent different number PR(a), calculate the sum of the target batch comparison indexes of all boundary defined heaters and take the average value to obtain a boundary comparison index UA(g), and calculate the normal working condition consistency index of the batch of quartz heaters by , wherein is a boundary optimization coefficient, and is a denominator protection coefficient, the value of the boundary optimization coefficient is 0.38, and the value of the denominator protection coefficient is 1.09.
[0028] The specific method for obtaining the target batch comparison index of the comparison quartz heater is as follows: select a comparison quartz heater, label each quartz heater extracted in the current batch as a target quartz heater, match the comparison quartz heater and each target quartz heater as a device comparison combination, obtain the individual comparison index of each device comparison combination, calculate the sum of the individual comparison indexes of each device comparison combination and take the average value to obtain the target batch comparison index of the comparison quartz heater.
[0029] The specific method for obtaining the individual comparison index of the device comparison combination is as follows: obtain the normal working condition data set of the comparison quartz heater and the normal working condition data set of the target quartz heater in the device comparison combination, import the two normal working condition data sets into a normal working condition set comparison model, and the normal working condition set comparison model outputs the individual comparison index of the device comparison combination.
[0030] The steps of building the ordinary working condition set comparison model are as follows: building a deep learning model, collecting a plurality of ordinary working condition data set comparison groups, each of which comprises two ordinary working condition data sets, taking the ordinary working condition data set comparison group as the basic data, and training the built deep learning model, in which a single comparison index is assigned to each ordinary working condition data set comparison group, and the value of the single comparison index is set to be between 1 and 30. The larger the single comparison index, the more similar the test data of the two ordinary working condition data sets in the ordinary working condition data set comparison group, and the closer the performance of the two quartz heaters corresponding to the ordinary working condition data set comparison group. Then, the plurality of ordinary working condition data set comparison groups are divided into a training set, a validation set and a test set according to a certain proportion, and the specific division proportion is determined to be 70:20:10. The training set is used to repeatedly train the deep learning model, and the performance of the model in the training stage is verified in time by means of the validation set. According to the verification result, the parameters of the model are adjusted in time, and the structure of the model is optimized, so that the model develops in a more accurate and stable direction. Finally, the ordinary working condition set comparison model is built.
[0031] The extreme working condition running performance test flow generation module determines the extreme working condition running performance test flow of the batch of quartz heaters based on all the extreme reference batches. According to the extreme working condition running performance test flow, the running performance of the batch of quartz heaters is tested in each extreme working condition in turn (ensuring that the test of the extreme working condition with low risk of irreversible damage is performed first, and then the test of the extreme working condition with high risk is performed. This order follows the engineering logic of “from non-destructive to destructive, from low risk to high risk, from data retention to data loss”, ensuring the effectiveness of the test and maximizing the utilization rate of the sampled quartz heaters).
[0032] The extreme working condition running performance test flow of the batch of quartz heaters is determined based on all the extreme reference batches: the extreme working condition damage index of each extreme working condition corresponding to each extreme reference batch is obtained (the running performance test of the extreme working condition includes but is not limited to the following items: ultra-high temperature running item, ultra-low temperature starting item, temperature impact item, etc. The extreme test conditions and extreme test data corresponding to different items are different, such as the extreme test conditions corresponding to the ultra-low temperature starting item: environmental temperature < -40℃ (such as -55℃, -196℃ liquid nitrogen environment), and the extreme test data: electrical parameters, temperature and thermal characteristic data, mechanical and structural data, etc.), and then the extreme working condition flow index of each extreme working condition is determined. Based on the value of the extreme working condition flow index, each extreme working condition is sequentially sorted from small to large, and the extreme working condition running performance test flow is determined according to the sorting result (the extreme working condition running performance test flow is to arrange each extreme working condition in a certain order).
[0033] The specific method for obtaining the extreme working condition damage index corresponding to one extreme reference batch for one extreme working condition is as follows: one extreme reference batch is selected, and one extreme working condition is selected, the extreme test data of each quartz heater drawn from the extreme reference batch for the extreme working condition is obtained (if one quartz heater is irreversibly damaged after performing the operation performance test of one extreme working condition, there is no extreme test data for other extreme working conditions which are not tested), each extreme test data is sequentially imported into the extreme damage model corresponding to the extreme working condition, the individual damage index of each quartz heater is sequentially determined, the sum value of the individual damage indexes of all quartz heaters is calculated and averaged, and the extreme working condition damage index of the extreme reference batch corresponding to the extreme working condition is calculated.
[0034] Each extreme working condition includes an extreme damage model, which is built based on a deep learning model. Hereinafter, the ultra-low temperature starting project is taken as an example to disclose the brief building steps of the extreme damage model corresponding to the ultra-low temperature starting project: a deep learning model is built, extreme test data of multiple quartz heaters for the ultra-low temperature starting project is collected, the extreme test data of the quartz heaters for the ultra-low temperature starting project is taken as basic data, the built deep learning model is trained, in this process, an individual damage index is assigned to the extreme test data of each quartz heater for the ultra-low temperature starting project, the value range of the individual damage index is set to be between 1 and 10, the size of the individual damage index has a clear meaning, and the larger the value is, the more serious the irreversible damage of the corresponding quartz heater in the ultra-low temperature starting project is, then the extreme test data of multiple quartz heaters for the ultra-low temperature starting project is divided into a training set, a validation set and a test set according to a specific proportion, the specific division proportion is determined to be 60:20:20, the training set is first used to repeatedly train the deep learning model, in the training process, the performance of the model in the training stage is verified in time by means of the validation set, the parameters of the model are adjusted in time according to the verification result, the structure of the model is optimized, and the model is developed in a more accurate and stable direction, and finally the extreme damage model corresponding to the ultra-low temperature starting project is built.
[0035] The specific method for determining the extreme working condition process index of one extreme working condition is as follows: one extreme working condition is selected, the extreme working condition damage indexes of each extreme reference batch corresponding to the extreme working condition are summed and averaged to calculate the extreme working condition damage index PR, when the extreme working condition and n extreme working conditions have a direct relationship in the extreme working condition relationship tree, the extreme working condition mechanism coupling number n is obtained, and the extreme working condition process index of the extreme working condition is calculated by .
[0036] The extreme working condition relationship tree contains each extreme working condition and expresses the direct relationship between each extreme working condition in the form of a relationship tree. For example, when two extreme working conditions have a direct relationship in the extreme working condition relationship tree, the essence is that the two extreme working conditions, although having different action forms, will cause the same or similar physical mechanism to cause the same origin irreversible damage to the quartz heater. This correlation is embodied in the relationship tree as "damage mechanism coupling".
[0037] The above system tests the operation performance of the quartz heater by batch. First, the operation performance of the quartz heater under normal working conditions is tested. Based on the operation performance test under normal working conditions, high consistency historical batches are accurately screened as references based on a consistency evaluation model driven by deep learning, to ensure the reliability of subsequent extreme working condition process arrangement. According to the extreme working condition relationship tree and damage mechanism coupling analysis, combined with the risk level quantified by the extreme damage model, the test process of "low risk priority, destructive postposition" is intelligently generated. Strictly follow the engineering logic of "non-destructive → destructive, data can be preserved → easy to lose", avoid the interference of high-risk working conditions on low-risk data, make the test process more in line with the actual failure path of the quartz heater, significantly reduce the sample waste and data distortion problem caused by improper test order, and provide efficient and accurate technical support for production process optimization and customer reliability evaluation.
[0038] The above formulas are dimensionless values, and the formulas are obtained by collecting a large amount of data to simulate the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.
[0039] It should be understood that the size of the serial number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0040] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.
[0041] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.
[0042] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiment is only a logical function division, and there can be another division manner for actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the mutual couplings or direct couplings or communication connections illustrated or discussed are merely conceptual; for example, they can be implemented in an electrical or mechanical manner, or in another manner.
[0043] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims
1. A performance testing system for quartz heaters, characterized in that, include The batch normal operating condition data set generation module generates data sets for each batch of quartz heaters produced. It then selects multiple quartz heaters from the batch according to the sampling ratio and obtains the normal operating condition data set for each quartz heater. The Extreme Reference Batch Analysis module identifies previous extreme reference batches. Identify previous extreme reference batches: Determine the normal operating condition consistency index of each previous batch, set the normal operating condition consistency threshold index, and mark the corresponding batch as an extreme reference batch when the normal operating condition consistency index of a batch is greater than or equal to the normal operating condition consistency threshold index. The specific method for determining the general operating condition consistency index of a batch is as follows: Select a previous batch, and label each quartz heater extracted from that batch as a comparison quartz heater. Obtain the target batch comparison index for each comparison quartz heater. Set a target batch comparison high index and a target batch comparison low index. When the target batch comparison index of the comparison quartz heater is greater than or equal to the target batch comparison high index, the corresponding comparison quartz heater is labeled as a prominent consistency heater. When the target batch comparison index of the comparison quartz heater is less than or equal to the target batch comparison index, the corresponding comparison quartz heater is labeled as a prominent difference heater. When the target batch comparison low index is less than or equal to the target batch comparison index of the comparison quartz heater, the corresponding comparison quartz heater is labeled as a boundary delimiting heater. The number of prominent consistency heaters is labeled as the prominent consistency quantity, and the number of prominent difference heaters is labeled as the prominent difference quantity. The target batch comparison indices of all boundary delimiting heaters are summed and averaged to calculate the boundary comparison index. Based on the prominent consistency quantity, the prominent difference quantity, and the boundary comparison index, the general operating condition consistency index of this batch of quartz heaters is calculated. The specific method for obtaining the target batch comparison index of the quartz heater is as follows: Select a comparison quartz heater, label the normal operating condition data set of each quartz heater extracted in the current batch as the target quartz heater, match the comparison quartz heater with each target quartz heater to form a device comparison combination, obtain the individual comparison index of each device comparison combination, calculate the sum of the individual comparison indices of each device comparison combination and take the average value to calculate the target batch comparison index of the comparison quartz heater. The specific method for obtaining the individual comparison index of the device comparison combination is as follows: obtain the normal operating condition data set of the comparison quartz heater and the target quartz heater in the device comparison combination, import the two normal operating condition data sets into the normal operating condition set comparison model, and derive the individual comparison index of the device comparison combination from the normal operating condition set comparison model. The extreme operating condition performance test process generation module determines the extreme operating condition performance test process for this batch of quartz heaters based on all extreme reference batches, and performs various extreme operating condition performance tests on this batch of quartz heaters in sequence according to the extreme operating condition performance test process.
2. The performance testing system for a quartz heater according to claim 1, characterized in that, The specific method for obtaining the normal operating condition data set of quartz heaters is as follows: Select a quartz heater, obtain the corresponding test data of the quartz heater under various normal operating conditions, and integrate all the test data into a normal operating condition data set.
3. The performance testing system for a quartz heater according to claim 1, characterized in that, The extreme operating condition performance test procedure for this batch of quartz heaters is determined based on all extreme reference batches: obtain the extreme operating condition damage index of each extreme reference batch for each extreme operating condition, and then determine the extreme operating condition process index of each extreme operating condition. Based on the value of the extreme operating condition process index from small to large, the extreme operating conditions are sorted in sequence, and the extreme operating condition performance test procedure is determined according to the sorting result.
4. The performance testing system for a quartz heater according to claim 3, characterized in that, The specific method for obtaining the extreme condition damage index of an extreme reference batch corresponding to an extreme working condition is as follows: Select an extreme reference batch and an extreme working condition, determine each quartz heater extracted from the extreme reference batch, obtain the extreme test data of each quartz heater for the extreme working condition, import each extreme test data into the extreme damage model corresponding to the extreme working condition in sequence, determine the individual damage index of each quartz heater in sequence, sum the individual damage indices of all quartz heaters and take the average value to calculate the extreme condition damage index of the extreme reference batch corresponding to the extreme working condition.
5. The performance testing system for a quartz heater according to claim 3, characterized in that, The specific method for determining the extreme condition process index of an extreme operating condition is as follows: Select an extreme operating condition, sum and average the extreme condition damage indices of each reference batch corresponding to that extreme operating condition, and calculate the extreme condition average damage index PR. When this extreme operating condition has a direct relationship with n extreme operating conditions in the extreme operating condition relationship tree, then obtain the extreme operating condition mechanism coupling quantity n. Calculate the extreme working condition process index for this extreme working condition. .
Citation Information
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