Rail transit electronic device vibration-temperature-humidity-electromagnetic comprehensive reliability test method

Abstract

The present application relates to the field of rail transit technology, in particular to a rail transit electronic device vibration-temperature and humidity-electromagnetic comprehensive reliability test method, which is characterized by setting a material aging attenuation model, dynamically correcting test parameters in combination with the characteristics of each stage of the full life cycle of the device, solving the technical pain point that the existing test is only based on new equipment and cannot predict the reliability in the later stage, achieving full coverage of the reliability test of the full life cycle of the device, and the test result is more in line with the actual operation demand, the present application sets up a temperature and humidity gradient precision control and anti-condensation self-adaptive adjustment cooperative mechanism, simulates the local temperature and humidity environment where each core component of the device is actually located, effectively avoids the condensation risk, solves the problem of uniformity and easy condensation of the existing temperature and humidity test, improves the authenticity of the test scene and the accuracy of the test data, and builds a closed-loop test logic of "data acquisition-model construction-parameter correction-loading test-model calibration".

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and more specifically, to a comprehensive reliability testing method for rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields. Background Technology

[0002] Rail transit electronic equipment (such as on-board controllers, trackside communication modules, front-end sensors, and signal transmission terminals) is a core component ensuring the safe, stable, and efficient operation of rail transit systems. Its operational reliability directly affects train safety, operational efficiency, and passenger travel experience. Unlike ordinary civilian electronic equipment, rail transit electronic equipment operates in a complex and harsh environment, simultaneously enduring the continuous effects of multiple environmental stresses: starting, braking, climbing, and track irregularities during train operation generate vibration stresses across a wide frequency range, easily leading to faults such as weld point detachment, structural deformation, and poor line contact; environmental temperature and humidity fluctuations in different regions and seasons (such as high temperature, low temperature, high humidity, and drastic diurnal temperature variations) accelerate material aging, reduce insulation performance, and even cause short circuits and component failures; at the same time, the train's internal electrical system, along-line communication equipment, and external electromagnetic radiation create a complex electromagnetic interference environment, easily interfering with the signal transmission accuracy of electronic equipment, leading to risks such as mis-sent control commands and data transmission interruptions. Therefore, conducting comprehensive reliability tests on rail transit electronic equipment involving vibration, temperature and humidity, and electromagnetic stress to fully evaluate its tolerance and operational reliability in actual service environments is a key step in the research, development, production, acceptance, and maintenance of rail transit electronic equipment. Current reliability testing methods for rail transit electronic equipment have drawbacks. Most testing methods typically employ single stress or simple superposition testing modes, which may be insufficient to meet the needs of accurate assessment throughout the entire life cycle. Fixed test parameters fail to consider material aging and degradation during long-term service and cannot simulate performance states at different aging stages, resulting in significant deviations between test results and actual reliability. Temperature and humidity tests use overall uniform control, failing to consider temperature and humidity gradient differences at the actual installation locations of components, making it difficult to accurately assess component tolerance. The tests lack anti-condensation adaptive adjustment mechanisms, relying on manually preset thresholds or passively interrupting tests. Multi-stress test loading parameters lack dynamic adaptability, and test data and model calibration do not form a closed loop, making it difficult to continuously improve test accuracy. Existing tests cannot achieve systematic assessment throughout the entire life cycle, cannot grasp the reliability change patterns at different service stages, and are detrimental to safe operation and maintenance and cost control. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of existing technologies, this invention provides a comprehensive reliability testing method for vibration, temperature and humidity, and electromagnetic fields of rail transit electronic equipment. The technical problems this invention aims to solve are: using single stress or simple superposition testing modes may fail to meet the requirements for accurate evaluation throughout the entire lifecycle; fixed test parameters do not consider material aging and degradation during long-term service, making it impossible to simulate performance states at different aging stages, resulting in significant deviations between test results and actual reliability; temperature and humidity testing employs overall uniform control, failing to consider temperature and humidity gradient differences at the actual installation locations of components, making it difficult to accurately assess component tolerance; the test lacks an anti-condensation adaptive adjustment mechanism, relying on manually preset thresholds or passively interrupting the test; multi-stress test loading parameters lack dynamic adaptability; and test data and model calibration do not form a closed loop, making it difficult to continuously improve test accuracy. Existing tests cannot achieve systematic evaluation throughout the entire lifecycle, cannot grasp the reliability change patterns at different service stages, and are detrimental to safe operation and maintenance and cost control.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A comprehensive reliability test method for rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields includes the following steps: Step 1: Basic Data Acquisition and Database Construction: Collect material property parameters, actual application scenario data, and historical test data of the core components of the tested rail transit electronic equipment to construct a multi-dimensional basic test database; Step 2: Comprehensive test model construction and parameter correction: Based on the basic database, a material aging and degradation model is constructed, the performance degradation coefficient of each core component at different aging stages is calculated, and the vibration, temperature and humidity, and electromagnetic loading parameters at the corresponding stages are corrected. Step 3: Test area division and temperature and humidity gradient standard determination: Divide the test area into independent control areas according to the actual installation location of the core components of the electronic equipment under test, configure temperature and humidity sensors and adjustment actuators, and determine the target temperature and humidity of each area based on the corrected temperature and humidity parameters to form a temperature and humidity gradient control standard. Step 4: Multi-stress synergistic loading and adaptive adjustment: Apply vibration-temperature-humidity-electromagnetic multi-stress synergistic loading to the device under test, and simultaneously achieve precise adjustment of temperature and humidity gradients in each area and adaptive adjustment to prevent condensation. Step 5: Data Acquisition and Model Calibration: Collect multi-dimensional data in real time during the test process, preprocess the data, compare it with the predicted data of the material aging and decay model, calculate the deviation value, and complete the adaptive calibration of the model. Step Six: Multi-Aging Stage Cyclic Test: Repeat Steps Two through Five to complete the comprehensive reliability test of each aging stage of the device under test in sequence; Step 7: Full life cycle reliability assessment: Summarize the test data of each aging stage, comprehensively assess the reliability of the equipment throughout its entire life cycle, and output an assessment report.

[0005] As a further aspect of the present invention: the construction and parameter correction process of the material aging degradation model in step two specifically includes: Based on the Arrhenius temperature aging equation and fatigue cumulative damage theory, and combined with the component material property parameters, equipment preset service life and environmental exposure time in the basic database, a material aging and degradation model is constructed. The material aging degradation model calculates the performance degradation coefficient of each core component at different aging stages (including 0 years, 5 years, 10 years, and 15 years). The core components include PCB boards, sealants, capacitors, and sensors. The performance degradation coefficient is used to correct the temperature and humidity loading parameters, vibration loading parameters, and electromagnetic loading parameters for the corresponding aging stages, ensuring that the loading parameters for each stage are adapted to the aging characteristics of the components.

[0006] As a further aspect of the present invention: the construction and parameter correction process of the material aging degradation model in step two specifically includes: Based on the Arrhenius temperature aging equation and fatigue cumulative damage theory, and combined with the component material property parameters, equipment preset service life and environmental exposure time in the basic database, a material aging and degradation model is constructed. The material aging degradation model calculates the performance degradation coefficient of each core component at different aging stages (including 0 years, 5 years, 10 years, and 15 years). The core components include PCB boards, sealants, capacitors, and sensors. The performance degradation coefficient is used to correct the temperature and humidity loading parameters, vibration loading parameters, and electromagnetic loading parameters for the corresponding aging stages, ensuring that the loading parameters for each stage are adapted to the aging characteristics of the components.

[0007] As a further aspect of the present invention: the coordinated control process of precise temperature and humidity gradient adjustment and anti-condensation adaptive adjustment in step four is specifically as follows: The temperature and humidity of each independent control zone are adjusted in real time to ensure that the temperature and humidity gradient control standards are met. Simultaneously collect temperature and humidity data for each area and calculate the dew point temperature. Set a threshold for preventing condensation: when the difference between the actual temperature and the dew point temperature of an area is ≤2℃, it is determined to be close to the state of condensation. When condensation is detected, the system automatically fine-tunes the temperature and humidity parameters of the corresponding area. It avoids the risk of condensation by reducing the RH humidity of the corresponding area by 2%~5% or increasing the temperature by 0.5℃~1℃, and the fine-tuning process does not affect the accuracy of temperature and humidity gradient control.

[0008] As a further aspect of the present invention: the multi-stress synergistic loading in step four specifically includes: The vibration loading unit applies vibration stress in the frequency range of 10Hz to 2000Hz, and the vibration amplitude can be precisely adjusted based on the correction parameters; The temperature and humidity loading unit provides precise temperature and humidity output for each independent control zone, adapting to the requirements of temperature and humidity gradient control. The electromagnetic loading unit applies electromagnetic stress covering the typical interference frequency band of rail transit, with an electromagnetic interference amplitude adjustment range of 2V / m to 10V / m, and the loading parameters are adapted to the characteristics of components at different aging stages.

[0009] As a further aspect of the present invention: the data acquisition and model calibration process in step five specifically includes: At a sampling frequency of no less than 100Hz, temperature and humidity data, dew point temperature data, equipment response data, and adjustment parameters of each module in each independent control area are collected synchronously. The collected data is processed to reduce noise, remove duplicates, and standardize the format to generate a standardized test dataset; The component performance data in the standardized test dataset is compared with the predicted data of the material aging and degradation model to calculate the performance deviation value. When the deviation value exceeds the preset threshold (insulation resistance deviation > 10% or signal stability deviation > 0.5%), the parameters of the material aging and degradation model are automatically adjusted to achieve iterative optimization of the model.

[0010] As a further aspect of the present invention: in step six, a multi-aging stage cyclic test is performed. After each aging stage test is completed, a stage test report is output to clarify the temperature and humidity resistance, vibration and electromagnetic interference adaptability of each core component in the corresponding aging stage.

[0011] In addition, this solution also involves a method for a comprehensive reliability testing system for vibration, temperature and humidity, and electromagnetic fields of rail transit electronic equipment: including a basic data acquisition module, a comprehensive test control module, a multi-stress loading module, a data acquisition feedback module, and a reliability assessment module; each module realizes data interaction and command transmission through an industrial communication bus, and collaboratively completes the comprehensive reliability testing of vibration, temperature and humidity, and electromagnetic fields throughout the entire life cycle of rail transit electronic equipment. Basic data acquisition module: used to collect material property parameters, actual application scenario data and historical test data of core components of rail transit electronic equipment, to build a multi-dimensional test basic database and provide data support for step one; Integrated Test Control Module: As the core control unit of the system, it includes a material aging and degradation control submodule, a temperature and humidity gradient control submodule, and a dew point adaptive adjustment submodule, which are used to realize the dynamic correction of parameters in step two, the determination of temperature and humidity gradient standards in step three, and the adaptive adjustment in step four. Multi-stress loading module: used to perform vibration-temperature-humidity-electromagnetic multi-stress coordinated loading in step four, and the loading parameters can be dynamically adjusted by receiving instructions from the integrated test control module; Data acquisition and feedback module: used to perform multi-dimensional real-time data acquisition, preprocessing and model calibration in step five, and feed the data back to the comprehensive test control module to realize model iteration; Reliability assessment module: Used to perform the full life cycle reliability assessment in step seven, and outputs an assessment report based on test data and the calibrated model.

[0012] As a further aspect of the present invention: the material aging and degradation control submodule is specifically used for: A material aging degradation model was constructed based on the Arrhenius temperature aging equation and fatigue cumulative damage theory. Calculate the performance degradation coefficient of each core component at different aging stages; The loading parameters at each stage are adjusted based on the performance degradation coefficient to ensure that the loading parameters are adapted to the aging characteristics of the components.

[0013] As a further aspect of the present invention: the temperature and humidity gradient control submodule is specifically used for: The system is divided into four independent control areas: the outer casing area, the internal circuit board area, the front-end sensor area, and the rear-end controller area. Equipped with a high-precision temperature and humidity sensor and an independent regulating actuator; It receives the corrected temperature and humidity loading parameters, determines the target temperature and humidity of each area, and achieves precise control of the temperature and humidity gradient.

[0014] The beneficial effects of this invention are as follows: 1. This invention solves the technical pain point that existing tests are only based on new equipment and cannot predict the reliability of later stages by setting up a material aging and decay model and dynamically correcting test parameters in combination with the characteristics of each stage of the equipment's entire life cycle. It achieves full coverage of the equipment's entire life cycle reliability test, and the test results are more in line with actual operating requirements. 2. This invention, by setting up a coordinated mechanism of precise temperature and humidity gradient control and anti-condensation adaptive adjustment, simulates the actual local temperature and humidity environment of each core component of the equipment, effectively avoids the risk of condensation, solves the problems of uniformity and easy condensation in existing temperature and humidity tests, and improves the realism of the test scenario and the accuracy of the test data. 3. This invention constructs a closed-loop testing logic of "data acquisition - model building - parameter correction - loading test - model calibration", and calibrates the model in real time with actual test data, continuously improving the test accuracy and solving the problems of existing tests lacking closed loop and difficulty in guaranteeing accuracy; 4. This invention achieves precise coordinated loading of vibration, temperature and humidity, and electromagnetic stresses through the coordinated operation of various modules of the testing system. It can comprehensively evaluate the reliability performance of equipment under complex working conditions and provides a complete technical solution for the full life cycle management of rail transit electronic equipment, which has broad application prospects. Attached Figure Description

[0015] Figure 1 This is a step diagram of the present invention; Figure 2 This is a system diagram of the present invention. Detailed Implementation

[0016] 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.

[0017] Example 1 This embodiment uses a certain model of rail transit vehicle-mounted signal controller as the test object. The core components of this device include a PCB board, sealant, capacitors, and front-end signal sensors. Its design service life conforms to the conventional standards for rail transit electronic equipment. The preset operating line is urban express rail. The test method and system of this invention are used to conduct a comprehensive reliability test throughout the entire life cycle. The specific implementation steps are as follows: Step 1: Basic Data Acquisition and Database Construction: Collect material property parameters, actual application scenario data, and historical test data of the core components of the tested rail transit electronic equipment to construct a multi-dimensional test basic database. Material properties of the core components of the vehicle signal controller were collected: the PCB board is made of epoxy resin with a thermal conductivity of 0.25 W / (m·K), insulation resistance of new equipment ≥1000 MΩ (test voltage 500 V), and fatigue strength of 150 MPa; the sealant is made of silicone with a humidity sensitivity level of 3 and a sealing performance baseline ≥ IP67; the capacitor is an aluminum electrolytic capacitor with an operating temperature range of -40℃ to 85℃ and a fatigue strength of 100 MPa; the front-end signal sensor is a Hall sensor with a thermal conductivity of 0.3 W / (m·K) and insulation resistance ≥500 MΩ. Data collected from actual application scenarios: The track flatness deviation of the preset operating line (urban rail transit) of this equipment is ≤0.5mm / m, the ambient temperature and humidity fluctuation range is -30℃~60℃, 20%RH~95%RH, the vibration frequency distribution is 10Hz~1500Hz (10Hz~50Hz for starting condition, 50Hz~200Hz for braking condition, and 200Hz~1500Hz for constant speed operation condition), the electromagnetic interference frequency band is 800MHz~2.4GHz, and the electromagnetic interference amplitude is 3V / m~8V / m; Collect historical test data: reliability test reports and fault records of similar vehicle signal controllers (past faults were mainly due to sealant aging and water leakage, and PCB board insulation performance degradation), and life assessment data (mean time between failures ≥ 20,000 hours). After classifying, organizing, validating (removing one set of abnormal insulation resistance data) and standardizing the format of the above data, a multi-dimensional test basic database is constructed and stored in an industrial hard drive, supporting data query and retrieval. Step Two: Comprehensive Test Model Construction and Parameter Correction: Based on the basic database, a material aging and degradation model is constructed to calculate the performance degradation coefficients of each core component at different aging stages, and the vibration, temperature and humidity, and electromagnetic loading parameters for the corresponding stages are corrected. Based on the Arrhenius temperature aging equation and fatigue cumulative damage theory, combined with data from the basic database, and integrating the working load characteristics of each core component (PCB board working voltage 24V, sensor working frequency 10 times / second), a material aging and degradation model is constructed. Inputting the basic data, the performance degradation coefficients of the core components at each aging stage are calculated. The degradation coefficients for new equipment are all 0; for the mid-aging stage, the degradation coefficients are 0.12 for the PCB board, 0.18 for the sealant, 0.10 for the capacitor, and 0.15 for the sensor; for the late-aging stage, they are 0.25 for the PCB board, 0.35 for the sealant, 0.22 for the capacitor, and 0.30 for the sensor; and for the final aging stage, they are 0.40 for the PCB board, 0.50 for the sealant, 0.38 for the capacitor, and 0.45 for the sensor. The loading parameters for each stage are adjusted based on the performance degradation coefficient: For the new equipment stage, the vibration frequency is 10Hz~2000Hz, amplitude is 2g, temperature and humidity are -40℃~85℃, 20%RH~95%, and electromagnetic interference amplitude is 2V / m~10V / m; for the mid-term aging stage, the vibration amplitude is adjusted to 1.8g, the temperature and humidity range is adjusted to -35℃~80℃, and the electromagnetic interference amplitude is adjusted to 2V / m~9V / m; for the late-stage aging stage, the vibration amplitude is 1.5g, the temperature and humidity are -30℃~75℃, and the electromagnetic interference amplitude is 2V / m~8V / m; for the final aging stage, the vibration amplitude is 1.2g, the temperature and humidity are -25℃~70℃, and the electromagnetic interference amplitude is 2V / m~7V / m, ensuring that the parameters are adapted to the aging characteristics of the components. Step 3: Test Area Division and Temperature and Humidity Gradient Standard Determination: Independent control areas are divided according to the actual installation location of the core components of the electronic equipment under test. Temperature and humidity sensors and regulating actuators are configured. Based on the corrected temperature and humidity parameters, the target temperature and humidity for each area are determined, forming a temperature and humidity gradient control standard. Based on the structural design of the vehicle signal controller, the test space is divided into an outer shell area, an internal circuit board area, a front-end sensor area, and a rear-end controller area. Each area is insulated with thermal insulation cotton to prevent heat and moisture. Each area is equipped with a high-precision temperature and humidity sensor (accuracy ±0.5℃, ±2%RH) and an independently adjustable actuator. The sensor is installed close to the surface of the component to complete calibration and debugging (sensor calibration error ±0.1℃, actuator response time 1.5s). Based on the corrected temperature and humidity parameters, the target temperature and humidity for each area are determined as follows: 85℃ for the outer casing area, 90℃ for the internal circuit board, 80℃ for the front-end sensor, and 88℃ for the back-end controller during the new equipment stage, forming a temperature gradient of 2℃ / cm and a humidity gradient of 8%RH / cm, with an allowable temperature and humidity fluctuation range of ±0.3℃ and ±1%RH. Step 4: Multi-stress Co-loading and Adaptive Adjustment: Apply vibration-temperature-humidity-electromagnetic multi-stress co-loading to the device under test, simultaneously achieving precise adjustment of temperature and humidity gradients in each region and adaptive adjustment to prevent condensation. The multi-stress loading system is activated, and vibration-temperature-humidity-electromagnetic multi-stress coordinated loading is applied according to the corrected parameters, with a loading synchronization error of ≤0.5s. Temperature and humidity gradient adjustment (using PID fuzzy control, temperature and humidity change rate of 0.8℃ / min) and anti-condensation adjustment are activated simultaneously. The dew point temperature is calculated in real time. When the temperature in the internal circuit board area is 88℃ and the humidity is 90%RH (dew point temperature 86.5℃, temperature difference 1.5℃), the anti-condensation command is triggered to reduce the humidity in this area by 3%RH and maintain the accuracy of the temperature and humidity gradient. Step 5: Data Acquisition and Model Calibration: Real-time acquisition of multi-dimensional data during the testing process, preprocessing, comparison with data predicted by the material aging and degradation model, calculation of deviation values, and completion of adaptive model calibration. A 32-channel data acquisition card (sampling rate 1kHz) was used to synchronously acquire temperature and humidity, dew point temperature, equipment response data (insulation resistance, signal error rate, etc.), and adjustment parameters for each area at a frequency of 100Hz. Wavelet transform denoising (signal-to-noise ratio 35dB), deduplication, and outlier removal (3σ criterion) were performed on the data to generate a standardized dataset. The measured data were compared with the model prediction data. During the mid-term aging stage, the PCB insulation resistance deviation was 8% (≤10%), and the signal stability deviation was 0.3% (≤0.5%), meeting the model accuracy requirements and requiring no parameter adjustment. During the late-term aging stage, the sealant performance deviation was 12% (>10%), requiring adjustment of the sealant aging coefficient in the model and re-correction of the parameters. In the model calibration phase, a hierarchical attention weight mechanism is introduced to accurately quantify the deviation between multi-dimensional measured data and predicted data, achieving adaptive calibration of model parameters. First, a hierarchical attention weight integration formula is constructed to dynamically allocate the weights of each core component and each test parameter in the deviation calculation. The formula is as follows:

[0018] Formula parameter definition: : Layered attention weight calibration coefficient (core output value); Variance of multidimensional data bias (calculated from preprocessed data, unit: ...) ; Single calibration cycle (data can be collected, unit: seconds); Quantity of core components (quantifiable, such as PCB boards, sealant, etc., without units); : No. Attention weights for each core component (calculated using the analytic hierarchy process, unitless). ; : Corrected linear unit function (optional, used to filter negative deviations, outputs non-negative values, unitless), expression is: ; : No. Each core component at time Measured performance parameters (collectible, unit: corresponding performance parameter unit, such as insulation resistance in MΩ, signal bit error rate in %). : No. Each core component at time Model prediction performance parameters (model output, unit: ...) Consistent); : The mean of the deviation of multi-dimensional data (which can be calculated from the preprocessed data, unit: performance parameter unit); Model parameter matrix determinant for matrix, (This refers to the number of model parameters; the elements are calibrable parameters such as material aging coefficient and stress coupling coefficient, and are unitless.) : Number of test parameter types (can be counted, such as temperature and humidity, vibration, electromagnetic interference, etc., without units); : No. The weighting coefficients of the class test parameters (which can be calculated using the entropy weighting method, and are unitless) ; : S-type activation function (optional, used to normalize the influence factor of test parameters, output range (0,1), unitless), expression is: ; : No. The deviation rate between the measured value and the standard value of a test parameter (can be collected and calculated, unitless). ; Meaning of the formula range: The meaning of the value range is as follows: when At the time: the hierarchical attention weight allocation is reasonable, the deviation between the measured data and the predicted data is very small (≤3%), and the model does not require calibration; when At this time: the hierarchical attention weight allocation is basically reasonable, and the deviation is within an acceptable range (3%~10%). The model needs to fine-tune the parameters. when If the hierarchical attention weight allocation is unreasonable and the deviation exceeds the allowable range (>10%), the model needs to be recalibrated for core parameters.

[0019] Calculated based on the above formula Then, the component performance data (focusing on core indicators such as insulation resistance and signal stability) in the standardized test dataset are compared one by one with the predicted data from the material aging and degradation model, and the performance deviation value is calculated (deviation value = |measured value - predicted value| / predicted value × 100%). The range of values ​​determines the model's prediction accuracy. If the deviation exceeds the preset threshold (insulation resistance deviation > 10% or signal stability deviation > 0.5%), the parameters in the material aging degradation model (including activation energy, fatigue cumulative damage coefficient, and stress coupling influence coefficients in the Arrhenius equation) are automatically adjusted, and the performance degradation coefficient is recalculated to achieve iterative optimization of the model. If the deviation does not exceed the preset threshold, the current model parameters are retained, and the test data is archived as model validation data for reference in subsequent model optimization. Step Six: Multi-Aging Stage Cyclic Test: Repeat Steps Two through Five to complete the comprehensive reliability test of the device under test at each aging stage in sequence. Repeat steps two through five according to the new equipment status, mid-term aging, late-term aging, and final aging stages, with test cycles of 72h, 96h, 96h, and 120h for each stage, respectively; update the initial equipment data, correct the loading parameters, and check the equipment status before each stage test; output a stage report after the test is completed, clearly stating that the sealing performance of the sealant in the final aging stage has degraded to IP64 and cannot meet the operating requirements. Step 7: Lifecycle Reliability Assessment: Summarize test data from each aging stage, comprehensively assess the equipment's lifecycle reliability, and output an assessment report. Based on the Weibull distribution model, the mean time between failures (MTBF) of the equipment was calculated to be 18,500 hours. The actual service life of the equipment was predicted to be about 80% of the design life. The key failure point was the rapid degradation of capacitor performance in the later part of the aging process. An evaluation report was output, which recommended optimizing the sealant material and strengthening the capacitor heat dissipation design.

[0020] In summary, the testing method and system of this invention can accurately simulate the actual operating environment of the vehicle signal controller throughout its entire life cycle, effectively identify weak links and fault risk points in the equipment, and provide real and reliable test results, thus providing strong support for equipment optimization design and operation and maintenance.

[0021] Example 2 This embodiment uses a rail transit vehicle-mounted signal controller as the test object. This device is widely used in rail transit signal transmission systems. Its core components include a PCB board (made of epoxy resin), sealant (made of silicone), aluminum electrolytic capacitors, and Hall sensors. Its reliability directly affects the safety of rail transit operation. In the test preparation stage, a comprehensive test platform that meets the requirements of the invention was first built, covering a basic data acquisition module (including thermal conductivity meter, insulation resistance tester, fatigue testing machine, electromagnetic interference tester, etc.), a comprehensive test control module (integrated material aging attenuation calculation unit, temperature and humidity gradient control unit), a multi-stress loading module (electromagnetic vibration table, zoned temperature and humidity loading unit, electromagnetic interference generator), a data acquisition feedback module (32-channel data acquisition card, sampling rate 1kHz), and a reliability assessment module (built-in Weibull distribution assessment model). At the same time, an existing technology test scheme (single stress loading + no gradient temperature and humidity control, no model adaptive calibration) was selected as a control group. The two groups of tests used three vehicle-mounted signal controllers of the same batch and specifications to ensure the uniqueness of the test variables.

[0022] The detailed process of the experiment strictly followed the seven steps of the invention: The first step involves basic data collection and database construction. This includes collecting material characteristic parameters of core components using specialized equipment (PCB board thermal conductivity 0.25 W / (m·K), insulation resistance baseline 1000 MΩ, sealant sealing performance baseline IP67, capacitor operating temperature range -40℃~85℃, sensor response time ≤10 ms), collecting data from typical rail transit application scenarios (track flatness deviation ≤0.5 mm / m, ambient temperature and humidity fluctuation -30℃~60℃ / 20%RH~95%, vibration frequency 10 Hz~2000 Hz, electromagnetic interference frequency band 800 MHz~2.4 GHz), and collecting historical fault data from similar equipment (sealant aging and leakage failure rate 32%, PCB board insulation attenuation failure rate 28%). After classification, organization, and outlier removal using the 3σ criterion, a multi-dimensional test database is constructed. The second step involves constructing a comprehensive test model and correcting parameters. Based on the Arrhenius temperature aging equation (k=Aexp(-Ea / RT)) and the fatigue cumulative damage theory (Miner linear cumulative damage criterion), a material aging and degradation model is constructed using database data. This model calculates the performance degradation coefficients of core components in four stages: new equipment (0 years), mid-term aging (5 years), late-term aging (10 years), and final aging (15 years). For example, the PCB insulation degradation coefficient is 0.12 and the sealant sealing performance degradation coefficient is 0.18 in the 5-year aging stage. Based on these degradation coefficients, the loading parameters for each stage are corrected (e.g., the vibration amplitude is corrected from 2g to 1.5g, the electromagnetic interference amplitude is corrected from 10V / m to 8V / m, and the target temperature of the internal circuit board area is corrected from 90℃ to 85℃ in the 10-year aging stage). The third step involves dividing the test area and determining the temperature and humidity gradient standards. Based on the installation location of the core components, the test area is divided into four independent control areas: the outer casing, the internal circuit board, the front-end sensor, and the back-end controller. Each area is equipped with a high-precision temperature and humidity sensor with an accuracy of ±0.5℃ / ±2%RH and an independent adjustment actuator. Based on the corrected temperature and humidity parameters, the target temperature and humidity for each area are determined (outer casing 85℃ / 60%RH, internal circuit board 90℃ / 55%RH, front-end sensor 80℃ / 65%RH, back-end controller 88℃ / 58%RH), forming a temperature gradient of 2℃ / cm and a humidity gradient control standard of 5%RH / cm. The fourth step involves multi-stress synergistic loading and adaptive adjustment. Vibration-temperature-humidity-electromagnetic synergistic stress is applied through the multi-stress loading module. During the loading process, precise temperature and humidity gradient adjustment is initiated simultaneously (using PID fuzzy control, with temperature and humidity change rates ≤1℃ / min and 5%RH / min). Temperature and humidity data for each area are collected in real time, and the dew point temperature is calculated. When the temperature in the internal circuit board area reaches 88℃ and the humidity reaches 90%RH (dew point temperature 86.5℃, temperature difference 1.5℃), anti-condensation adaptive adjustment is triggered, automatically reducing the humidity in that area by 3%RH to ensure no condensation and that the gradient accuracy meets the standard. The fifth step is data acquisition and model calibration. Temperature and humidity, dew point temperature, equipment response data (insulation resistance, signal error rate, sensor output accuracy) and adjustment parameters of each area are collected synchronously at a frequency of 100Hz. After wavelet transform noise reduction (signal-to-noise ratio ≥35dB), deduplication of duplicate data, and format standardization, the data is compared with the predicted data of the material aging and decay model to calculate the performance deviation value. When the PCB insulation resistance deviation reaches 12% (exceeding the 10% threshold), the aging activation energy parameter in the model is automatically adjusted to achieve adaptive calibration of the model. Step 6: Multi-aging stage cyclic testing. Repeat steps 2 to 5 to complete the comprehensive test of the four aging stages in sequence. The test cycle for each stage is 72h (0 years), 96h (5 years / 10 years), and 120h (15 years). After each stage, output a phase report to clarify the performance indicators of each core component. Step 7: Full life cycle reliability assessment. The test data of the four aging stages are summarized. The mean time between failures (MTBF) and reliability (R(t)) are calculated through the reliability assessment module. The full life cycle reliability assessment report is output. The control group test only uses single stress sequence loading, without regional division and temperature and humidity gradient control, without model calibration, and the loading parameters remain unchanged throughout the process.

[0023] ; ; ; A deep analysis of the data in the three tables above clearly demonstrates the significant advantages, inventiveness, and novelty of this invention compared to existing technologies. The specific analysis is as follows: Looking at the performance degradation comparison of core components (Tables 1 and 2), the two sets of test data are basically consistent under new equipment conditions, indicating unified initial test conditions. However, as the aging process progresses, the differences between the two sets of data gradually widen. The performance degradation rate of the core components in the test group of this invention is significantly lower than that of the control group. For example, during the 15-year aging stage, the insulation resistance of the PCB board in the test group of this invention remains at 598 MΩ, a 32.9% improvement compared to the 450 MΩ of the control group; the sealing performance of the sealant remains at IP64, a 100% improvement compared to the IP60 of the control group; and the signal error rate is only 0.09%, a 59.1% reduction compared to the 0.22% of the control group. This difference stems from the core innovation of this invention—the material aging degradation model and dynamic parameter correction. By accurately calculating the performance degradation coefficient at each aging stage and adjusting the loading parameters accordingly, the excessive wear or insufficient testing caused by the "one-size-fits-all" loading of existing technologies is avoided. This achieves precise adaptation between the test and the aging characteristics of the components, a creative design not addressed in existing technologies.

[0024] From the perspective of overall system performance comparison (third table), this invention has achieved breakthrough optimizations in temperature and humidity gradient control accuracy, condensation avoidance, model prediction accuracy, and equipment reliability. The 70% / 60% improvement in temperature and humidity gradient control accuracy is primarily due to the invention's adoption of zoned independent control and gradient standard design. This solves the technical pain point of existing technologies where single temperature and humidity control cannot simulate the actual temperature and humidity distribution inside the equipment, ensuring a high degree of consistency between the test environment and the actual service environment, demonstrating the novelty of the test scenario construction. The condensation rate is reduced to 0%, thanks to the collaborative control mechanism of precise temperature and humidity gradient adjustment and anti-condensation adaptive adjustment. By calculating the dew point temperature in real time and dynamically fine-tuning temperature and humidity parameters, it avoids interference with test data and damage to the equipment caused by condensation without affecting gradient control accuracy, thus overcoming the shortcomings of existing technologies that lack anti-condensation design or where anti-condensation and temperature and humidity control conflict. Model prediction... The deviation reduction of over 60% stems from the closed-loop logic of data acquisition and model calibration in this invention. By comparing multi-dimensional data in real time, preprocessing, and model prediction data, the model parameters are automatically optimized, solving the problem of prediction accuracy decay caused by the lack of adaptive calibration mechanism and fixed models in existing technologies. This achieves dynamic optimization of the testing process. The mean time between failures (MTBF) is increased by 54.2%, and the total failure rate of core components is reduced by 73.5%. This is the result of the synergistic effect of the above-mentioned innovative designs, fully demonstrating that the testing method of this invention can more accurately and comprehensively assess the reliability of equipment throughout its entire life cycle, providing more reliable technical support for equipment optimization design and operation and maintenance, and significantly outperforming the testing effects of existing technologies.

[0025] In summary, this invention, through its innovative end-to-end design encompassing basic data, model construction, parameter correction, partitioned control, collaborative loading, closed-loop calibration, cyclic testing, and comprehensive evaluation, not only addresses numerous shortcomings of existing technologies such as single stress loading, lack of gradient control, lack of adaptive calibration, and inaccurate reliability assessment, but also achieves qualitative improvements in test adaptability, scenario realism, data accuracy, and system reliability. Its technical solution demonstrates significant creativity and novelty, and possesses extremely high application value.

[0026] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive reliability testing method for rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields, characterized in that: Includes the following steps: Step 1: Basic Data Acquisition and Database Construction: Collect material property parameters, actual application scenario data, and historical test data of the core components of the tested rail transit electronic equipment to construct a multi-dimensional basic test database; Step 2: Comprehensive test model construction and parameter correction: Based on the basic database, a material aging and degradation model is constructed, the performance degradation coefficient of each core component at different aging stages is calculated, and the vibration, temperature and humidity, and electromagnetic loading parameters at the corresponding stages are corrected. Step 3: Test area division and temperature and humidity gradient standard determination: Divide the test area into independent control areas according to the actual installation location of the core components of the electronic equipment under test, configure temperature and humidity sensors and adjustment actuators, and determine the target temperature and humidity of each area based on the corrected temperature and humidity parameters to form a temperature and humidity gradient control standard. Step 4: Multi-stress synergistic loading and adaptive adjustment: Apply vibration-temperature-humidity-electromagnetic multi-stress synergistic loading to the device under test, and simultaneously achieve precise adjustment of temperature and humidity gradients in each area and adaptive adjustment to prevent condensation. Step 5: Data Acquisition and Model Calibration: Collect multi-dimensional data in real time during the test process, preprocess the data, compare it with the predicted data of the material aging and decay model, calculate the deviation value, and complete the adaptive calibration of the model. Step Six: Multi-Aging Stage Cyclic Test: Repeat Steps Two through Five to complete the comprehensive reliability test of each aging stage of the device under test in sequence; Step 7: Full life cycle reliability assessment: Summarize the test data of each aging stage, comprehensively assess the reliability of the equipment throughout its entire life cycle, and output an assessment report.

2. The method for comprehensive reliability testing of rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields according to claim 1, characterized in that: The process of constructing and correcting the material aging degradation model in step two specifically includes: Based on the Arrhenius temperature aging equation and fatigue cumulative damage theory, and combined with the component material property parameters, equipment preset service life and environmental exposure time in the basic database, a material aging and degradation model is constructed. The material aging degradation model calculates the performance degradation coefficient of each core component at different aging stages (including 0 years, 5 years, 10 years, and 15 years). The core components include PCB boards, sealants, capacitors, and sensors. The performance degradation coefficient is used to correct the temperature and humidity loading parameters, vibration loading parameters, and electromagnetic loading parameters corresponding to the aging stages, respectively, to ensure that the loading parameters at each stage are adapted to the aging characteristics of the components.

3. The method for comprehensive reliability testing of rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields according to claim 1, characterized in that: The process of determining the temperature and humidity gradient standard in step three specifically includes: The test space is divided into four independent control areas: the outer shell area, the internal circuit board area, the front-end sensor area, and the back-end controller area. Each independent control area is equipped with a dedicated high-precision temperature and humidity sensor and an independent adjustment actuator. The high-precision temperature and humidity sensor has a temperature measurement accuracy of ±0.5℃ and a humidity measurement accuracy of ±2%RH. Based on the corrected temperature and humidity loading parameters, the target temperature and humidity of each independent control area are determined, forming a temperature and humidity gradient control standard with a temperature gradient of 0~5℃ / cm and a humidity gradient of 0~10%RH / cm.

4. The method for comprehensive reliability testing of rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields according to claim 1, characterized in that: The coordinated control process of precise temperature and humidity gradient adjustment and anti-condensation adaptive adjustment in step four is as follows: The temperature and humidity of each independent control zone are adjusted in real time to ensure that the temperature and humidity gradient control standards are met. The system synchronously collects temperature and humidity data of each area and calculates the dew point temperature, and presets an anti-condensation judgment threshold: when the difference between the actual temperature of the area and the dew point temperature is ≤2℃, it is judged to be close to the condensation state. When condensation is detected, the system automatically fine-tunes the temperature and humidity parameters of the corresponding area. It avoids the risk of condensation by reducing the RH humidity of the corresponding area by 2%~5% or increasing the temperature by 0.5℃~1℃, and the fine-tuning process does not affect the accuracy of temperature and humidity gradient control.

5. The method for comprehensive reliability testing of rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields according to claim 1, characterized in that: The multi-stress synergistic loading in step four specifically includes: The vibration loading unit applies vibration stress in the frequency range of 10Hz to 2000Hz, and the vibration amplitude can be precisely adjusted based on the correction parameters. The temperature and humidity loading unit provides precise temperature and humidity output for each independent control area, adapting to the requirements of temperature and humidity gradient control. The electromagnetic loading unit applies electromagnetic stress covering the typical interference frequency band of rail transit, with an electromagnetic interference amplitude adjustment range of 2V / m to 10V / m, and the loading parameters are adapted to the characteristics of components at different aging stages.

6. The method for comprehensive reliability testing of rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields according to claim 1, characterized in that: The data acquisition and model calibration process in step five specifically includes: The system synchronously collects temperature and humidity data, dew point temperature data, equipment response data, and adjustment parameters of each module in each independent control area at a sampling frequency of not less than 100Hz. The collected data is processed to reduce noise, remove duplicates, and standardize the format to generate a standardized test dataset; The component performance data in the standardized test dataset is compared with the predicted data of the material aging and degradation model to calculate the performance deviation value. When the deviation value exceeds the preset threshold (insulation resistance deviation > 10% or signal stability deviation > 0.5%), the parameters of the material aging and degradation model are automatically adjusted to achieve iterative optimization of the model.

7. The method for comprehensive reliability testing of rail transit electronic equipment based on vibration, temperature and humidity, and electromagnetic fields according to claim 1, characterized in that: In step six, a multi-aging stage cyclic test is conducted. After each aging stage test is completed, a stage test report is output, which clarifies the temperature and humidity resistance, vibration and electromagnetic interference adaptability of each core component in the corresponding aging stage.

8. The integrated reliability testing system for vibration-temperature-humidity-electromagnetic fields of rail transit electronic equipment according to any one of claims 1-7, characterized in that: It includes a basic data acquisition module, a comprehensive test control module, a multi-stress loading module, a data acquisition feedback module, and a reliability assessment module; each module realizes data interaction and command transmission through an industrial communication bus, and works together to complete the comprehensive reliability test of vibration, temperature and humidity, and electromagnetic fields throughout the entire life cycle of rail transit electronic equipment; Basic data acquisition module: used to collect material property parameters, actual application scenario data and historical test data of core components of rail transit electronic equipment, to build a multi-dimensional test basic database and provide data support for step one; Integrated Test Control Module: As the core control unit of the system, it includes a material aging and degradation control submodule, a temperature and humidity gradient control submodule, and a dew point adaptive adjustment submodule, which are used to realize the dynamic correction of parameters in step two, the determination of temperature and humidity gradient standards in step three, and the adaptive adjustment in step four. Multi-stress loading module: used to perform vibration-temperature-humidity-electromagnetic multi-stress coordinated loading in step four, and the loading parameters can be dynamically adjusted by receiving instructions from the integrated test control module; Data acquisition and feedback module: used to perform multi-dimensional real-time data acquisition, preprocessing and model calibration in step five, and feed the data back to the comprehensive test control module to realize model iteration; Reliability assessment module: Used to perform the full life cycle reliability assessment in step seven, and outputs an assessment report based on test data and the calibrated model.

9. A comprehensive reliability testing system for vibration-temperature-humidity-electromagnetic fields in rail transit electronic equipment according to claim 8, characterized in that: The material aging and degradation control submodule is specifically used for: A material aging degradation model was constructed based on the Arrhenius temperature aging equation and fatigue cumulative damage theory. Calculate the performance degradation coefficient of each core component at different aging stages; The loading parameters at each stage are adjusted based on the performance degradation coefficient to ensure that the loading parameters are adapted to the aging characteristics of the components.

10. A comprehensive reliability testing method for vibration-temperature-humidity-electromagnetic systems of rail transit electronic equipment according to claim 8, characterized in that: The temperature and humidity gradient control submodule is specifically used for: The system is divided into four independent control areas: the outer casing area, the internal circuit board area, the front-end sensor area, and the rear-end controller area. Equipped with a high-precision temperature and humidity sensor and an independent regulating actuator; It receives the corrected temperature and humidity loading parameters, determines the target temperature and humidity of each area, and achieves precise control of the temperature and humidity gradient.

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