Storage chip high and low temperature aging test chamber fault self-diagnosis system and method and computer equipment

Abstract

The invention relates to the technical field of fault diagnosis, in particular to a fault self-diagnosis system and method for a high and low temperature aging test chamber of a storage chip and computer equipment. The temperature control abnormal index is obtained through the data of the temperature control system sensor, early warning can be performed when the temperature control system has abnormal fluctuation, the performance evaluation basis of the air circulation system is provided by the air system efficiency coefficient, the heating efficiency and the refrigeration coefficient are obtained through the data of the heating and refrigerating system sensor, and the performance of the heating and refrigerating system is evaluated. An electrical stability index is obtained through data of a test system sensor, the operation stability of an electrical system is evaluated, the stability of the electrical system is detected in time, and a fault probability index is obtained through a system entropy increase rate, the electrical stability index, a thermal coupling factor, a wind system efficiency coefficient and a temperature control abnormal index. Therefore, the stability and the reliability of the equipment can be improved, the maintenance cost can be reduced, the use efficiency of the equipment is improved, and the accuracy and the safety of test results are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and particularly to a fault self-diagnosis system, method and computer device for a high and low temperature aging test chamber of a storage chip. Background Art

[0003] A storage chip is an electronic device for storing data, which is widely used in various electronic products, such as computers, mobile phones, digital cameras, etc. These chips can be volatile, such as dynamic random access memory, which will lose the stored information when the power is off, or non-volatile, such as flash memory, that is, the data can be retained even when the power is off. An aging test chamber is a general term for a class of products in the environmental test industry. Among them, high and low temperature aging is an essential test equipment for electronic products, which is used to test the parameters and performance of samples after the environmental change of high and low temperature tests.

[0004] Semiconductor memories have a certain failure probability, and the relationship between the failure probability and the number of uses conforms to the characteristics of the bathtub curve. Generally, aging tests are used to accelerate the occurrence of the failure probability of memories, and directly let them enter the product stable period to solve this problem. The specific method is to make the chip under test work continuously for a set time under high and low temperature conditions to accelerate the failure of the semiconductor memory and screen out the good products. Due to the wide application, large quantity, high performance and wide operating temperature range of chip testing, the reliability and stability of the aging test chamber are required to be relatively high.

[0005] In the existing fault self-diagnosis methods for high and low temperature aging test chambers of storage chips, there are problems of the complexity of equipment and the diversity of fault modes. Existing diagnostic systems may be difficult to comprehensively cover all potential fault modes, especially the detection ability for some intermittent and low-probability faults is weak, and due to the variety of equipment involved in the test chamber, it is difficult to effectively achieve collaborative diagnosis between equipment in the existing technology, resulting in the problem that it may be difficult to accurately locate under the action of multiple factors. Summary of the Invention

[0006] The main object of the present invention is to provide a fault self-diagnosis method for a high and low temperature aging test chamber of a storage chip, aiming to solve the technical problems in the existing technology.

[0007] The present invention proposes a fault self-diagnosis method for a high and low temperature aging test chamber of a storage chip, including: Obtaining the sensor data of the equipment subsystems of the high and low temperature aging test chamber, where the equipment subsystem sensor data includes temperature control system sensor data, air circulation system sensor data, heating and cooling system sensor data, and test system sensor data; Obtain the temperature control anomaly index according to the temperature control system sensor data, and obtain the wind system efficiency coefficient according to the wind circulation system sensor data; Obtain the heating efficiency and refrigeration coefficient according to the heating and cooling system sensor data, and obtain the thermal coupling factor according to the heating efficiency and refrigeration coefficient; Obtain the electrical stability index according to the test system sensor data; Obtain the probability that multiple sensor data deviate from the normal distribution according to the device subsystem sensor data, and obtain the system entropy increase rate according to the probabilities that multiple said data deviate from the normal distribution; Obtain the failure probability index according to the system entropy increase rate, electrical stability index, thermal coupling factor, wind system efficiency coefficient and temperature control anomaly index, and determine the status of the test chamber according to the failure probability index.

[0008] Preferably, the steps of obtaining the temperature control anomaly index according to the temperature control system sensor data and obtaining the wind system efficiency coefficient according to the wind circulation system sensor data include: Obtain the chamber temperature, set temperature and temperature range within a preset time period according to the temperature control system sensor data, and obtain the temperature change acceleration according to the temperature range and preset time period; Obtain the temperature deviation according to the chamber temperature and set temperature; Obtain the maximum temperature and minimum temperature according to the temperature range, and obtain the temperature range according to the maximum temperature and minimum temperature; Obtain the temperature volatility according to the temperature range and set temperature, and obtain the temperature control anomaly index according to the temperature volatility, temperature deviation and temperature change acceleration; Obtain the wind speed, wind pressure, fan current and impeller speed data according to the wind circulation system sensor data, and obtain the basic efficiency factor according to the wind speed, wind pressure and fan current; Obtain the attenuation factor, actual speed and rated speed according to the impeller speed data, and obtain the attenuation index according to the attenuation factor, actual speed and rated speed; Obtain the wind system efficiency coefficient according to the attenuation index and basic efficiency factor.

[0009] Preferably, the steps of obtaining the heating efficiency and refrigeration coefficient according to the heating and cooling system sensor data and obtaining the thermal coupling factor according to the heating efficiency and refrigeration coefficient include: Obtain the heating data and refrigeration data according to the heating and cooling system sensor data, and obtain the heating power, heating resistance and heating current according to the heating data; Obtain the refrigeration capacity and compressor power according to the refrigeration data, and obtain the refrigeration coefficient according to the refrigeration capacity and compressor power; Obtain the heating efficiency based on the heating power, heating resistance, and heating current, and obtain the energy efficiency ratio based on the heating efficiency and the coefficient of performance; Obtain the actual pressure and rated pressure of the refrigerant based on the refrigeration data, and obtain the pressure ratio based on the actual pressure and the rated pressure; Obtain the thermo-mechanical coupling factor based on the pressure ratio and the energy efficiency ratio.

[0010] Preferably, the step of obtaining the electrical stability index according to the sensor data of the test system includes: Obtain the total number of transmitted bits and the number of error bits based on the sensor data of the test system, and obtain the bit error rate based on the number of error bits and the total number of transmitted bits; Obtain the real-time voltage and the nominal voltage based on the sensor data of the test system, and obtain the supply voltage volatility based on the real-time voltage and the nominal voltage; Obtain the cumulative stress of the test chamber during the test cycle based on the supply voltage volatility and the bit error rate; Obtain the instruction sending time and the instruction receiving time based on the sensor data of the test system, and obtain the response delay of the storage chip based on the instruction sending time and the instruction receiving time; Obtain the electrical stability index based on the response delay of the storage chip and the cumulative stress.

[0011] Preferably, the step of obtaining the probability of multiple sensor data deviating from the normal distribution according to the sensor data of the device subsystem and obtaining the system entropy increase rate according to the probabilities of multiple such deviations from the normal distribution includes: Obtain multiple sensor data sets respectively based on the sensor data of the device subsystem, and obtain the corresponding data mean for each sensor data set; Obtain the corresponding data standard deviation based on each data mean, and obtain the corresponding probability of deviation from the normal distribution based on each data standard deviation and the sensor data set; Obtain the entropy value of the corresponding sensor based on each probability of deviation from the normal distribution, and obtain the system entropy increase rate based on multiple such entropy values.

[0012] Preferably, the step of determining the state of the test chamber according to the failure probability index includes: Judge whether the failure probability index is greater than a preset threshold; If the failure probability index is greater than the preset threshold, determine that the test chamber is in a failed state at this time, and judge the relationship between the failure probability index and the preset threshold interval; If the failure probability index is greater than the upper limit value of the preset threshold interval, determine that the failure degree is serious; If the failure probability index is within the preset threshold interval, determine that the failure degree is general; If the failure probability index is less than the lower limit of the preset threshold range, it is determined that the degree of failure is minor; If the failure probability index is not greater than the preset threshold, it is determined that the test chamber is in a normal state at this time.

[0013] The present application also provides a high and low temperature aging test chamber fault self-diagnosis system for storage chips, including: A first acquisition module, configured to acquire device subsystem sensor data of the high and low temperature aging test chamber, where the device subsystem sensor data includes temperature control system sensor data, air circulation system sensor data, heating and cooling system sensor data, and test system sensor data; A second acquisition module, configured to acquire a temperature control anomaly index according to the temperature control system sensor data, and acquire an air system efficiency coefficient according to the air circulation system sensor data; A third acquisition module, configured to acquire a heating efficiency and a refrigeration coefficient according to the heating and cooling system sensor data, and acquire a thermal coupling factor according to the heating efficiency and the refrigeration coefficient; A fourth acquisition module, configured to acquire an electrical stability index according to the test system sensor data; A fifth acquisition module, configured to acquire probabilities of multiple sensor data deviating from a normal distribution according to the device subsystem sensor data, and acquire a system entropy increase rate according to the multiple probabilities of deviating from the normal distribution; A determination module, configured to acquire a failure probability index according to the system entropy increase rate, the electrical stability index, the thermal coupling factor, the air system efficiency coefficient, and the temperature control anomaly index, and determine the state of the test chamber according to the failure probability index.

[0014] Preferably, the second acquisition module includes: A first acquisition unit, configured to acquire a chamber temperature, a set temperature, and a temperature range within a preset time period according to the temperature control system sensor data, and acquire a temperature change acceleration according to the temperature range and the preset time period; A second acquisition unit, configured to acquire a temperature deviation degree according to the chamber temperature and the set temperature; A third acquisition unit, configured to acquire a maximum temperature and a minimum temperature according to the temperature range, and acquire a temperature range difference according to the maximum temperature and the minimum temperature; A fourth acquisition unit, configured to acquire a temperature volatility according to the temperature range difference and the set temperature, and acquire a temperature control anomaly index according to the temperature volatility, the temperature deviation degree, and the temperature change acceleration; A fifth acquisition unit, configured to acquire wind speed, wind pressure, fan current, and impeller rotation speed data according to the air circulation system sensor data, and acquire a basic efficiency factor according to the wind speed, the wind pressure, and the fan current; A sixth acquisition unit, configured to acquire a decay factor, an actual rotational speed, and a rated rotational speed according to the impeller rotational speed data, and acquire a decay exponent according to the decay factor, the actual rotational speed, and the rated rotational speed; A seventh acquisition unit, configured to acquire a wind system efficiency coefficient according to the decay exponent and a basic efficiency factor.

[0015] The present invention further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned fault self-diagnosis method for the high and low temperature aging test chamber of the storage chip are implemented.

[0016] The present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned fault self-diagnosis method for the high and low temperature aging test chamber of the storage chip are implemented.

[0017] The beneficial effects of the present invention are as follows: By acquiring sensor data from multiple subsystems, the present invention provides a data basis for subsequent diagnosis and fault analysis. By acquiring a temperature control anomaly index from the sensor data of the temperature control system, early warning can be given when the temperature control system has abnormal fluctuations, preventing more serious equipment damage or test errors caused by temperature control failures. By acquiring the wind system efficiency coefficient from the sensor data of the wind circulation system, the wind system efficiency coefficient provides a basis for evaluating the performance of the wind circulation system, helping to timely adjust the working state of the wind circulation and improve the overall thermal circulation efficiency of the system. By acquiring the heating efficiency and refrigeration coefficient from the sensor data of the heating and refrigeration system, the performance of the heating and refrigeration system is evaluated. By real-time monitoring the heating efficiency and refrigeration coefficient, it can be ensured that the equipment always maintains the optimal temperature control state and improves the energy use efficiency of the equipment. By calculating the thermal coupling factor based on the heating efficiency and refrigeration coefficient, the heat energy conversion efficiency of the heating and refrigeration system and the working state of the overall thermal system are reflected, which helps to understand the overall operation state of the thermal system. By acquiring the electrical stability index from the sensor data of the test system, the operation stability of the electrical system is evaluated, and the stability of the electrical system is detected in time. By acquiring the probability of multiple sensor data deviating from the normal distribution from the sensor data of the equipment subsystems and acquiring the system entropy increase rate according to the multiple probabilities of deviating from the normal distribution, the health state of the equipment can be evaluated from multiple dimensions, and potential fault modes in the system can be identified in time. The entropy increase rate provides a global monitoring tool for complex systems, helping to cover all potential fault modes. By acquiring the fault probability index based on the system entropy increase rate, the electrical stability index, the thermal coupling factor, the wind system efficiency coefficient, and the temperature control anomaly index, and determining the state of the test chamber according to the fault probability index, not only can the stability and reliability of the equipment be improved, but also the maintenance cost can be reduced, the use efficiency of the equipment can be improved, and the accuracy and safety of the test results can be ensured, solving the problems of insufficient fault diagnosis ability and difficulty in covering complex fault modes in the prior art. Description of the Drawings

[0018] Figure 1 Schematic diagram of the method flow according to an embodiment of the present invention.

[0019] Figure 2 Schematic diagram of the structure of a system according to an embodiment of the present invention.

[0020] Figure 3 Schematic diagram of the structure of another system according to an embodiment of the present invention.

[0021] Figure 4 Schematic diagram of the structure of the first diagnostic process according to an embodiment of the present invention.

[0022] Figure 5 Schematic diagram of the structure of the second diagnostic process according to an embodiment of the present invention.

[0023] Figure 6 Schematic diagram of the structure of the third diagnostic process according to an embodiment of the present invention.

[0024] Figure 7 Schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0025] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0026] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0027] As Figure 1 shown, the present application provides a method for self-diagnosing faults in a high and low temperature aging test chamber for storage chips, including: S1. Obtain the sensor data of the device subsystems of the high and low temperature aging test chamber, where the sensor data of the device subsystems includes the sensor data of the temperature control system, the sensor data of the air circulation system, the sensor data of the heating and cooling system, and the sensor data of the test system; S2. Obtain the temperature control anomaly index according to the sensor data of the temperature control system, and obtain the air system efficiency coefficient according to the sensor data of the air circulation system; S3. Obtain the heating efficiency and the refrigeration coefficient according to the sensor data of the heating and cooling system, and obtain the thermal coupling factor according to the heating efficiency and the refrigeration coefficient; S4. Obtain the electrical stability index according to the sensor data of the test system; S5. Obtain the probabilities of multiple sensor data deviating from the normal distribution according to the sensor data of the device subsystems, and obtain the system entropy increase rate according to the multiple probabilities of deviating from the normal distribution; S6. Calculate the fault probability index based on the system entropy increase rate, electrical stability index, thermal coupling factor, wind system efficiency coefficient, and temperature control anomaly index. The calculation formula is as follows: ; Wherein, represents the fault probability index, represents the system entropy increase rate, represents the weight of the system entropy increase rate, represents the electrical stability index, represents the weight of the electrical stability index, represents the thermal coupling factor, represents the weight of the thermal coupling factor, represents the wind system efficiency coefficient, represents the weight of the wind system efficiency coefficient, represents the temperature control anomaly index, represents the weight of the temperature control anomaly index; And determine the state of the test chamber according to the fault probability index.

[0028] As described in the above steps S1 - S6, in the calculation formula of the failure probability index, the respective parameters are normalized in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the temperature control system sensor data, air circulation system sensor data, heating and cooling system sensor data, and test system sensor data of the equipment subsystem sensors of the high and low temperature aging test chamber. By obtaining sensor data from multiple subsystems (temperature control system, air circulation system, heating and cooling system, test system), it provides a data basis for subsequent diagnosis and fault analysis. By obtaining a variety of sensor data, the operating state of the test chamber can be obtained from multiple dimensions, ensuring that all key equipment and systems are covered, avoiding any potential problems being overlooked, providing real-time and objective equipment operation data, reducing the influence of human factors on fault diagnosis, and enhancing the accuracy and timeliness of fault diagnosis. By obtaining the temperature control system sensor data, the temperature control anomaly index is obtained. Through the temperature control system sensor data, the temperature control anomaly index is calculated and obtained, reflecting the stability and working condition of the temperature control system, and can give an early warning when there are abnormal fluctuations in the temperature control system, preventing more serious equipment damage or test errors caused by temperature control failures. Through real-time monitoring and anomaly detection, the deviation of the temperature control system can be found in time, improving the overall stability of the system. According to the air circulation system sensor data, the air system efficiency coefficient is obtained. Through the air circulation system sensor data, the air system efficiency coefficient is calculated to evaluate the working efficiency of the air circulation system. The air system efficiency coefficient provides a basis for evaluating the performance of the air circulation system, helping to adjust the working state of the air circulation in time and improving the overall thermal circulation efficiency of the system. If the air system efficiency coefficient decreases, problems that may exist in the air system can be quickly identified, avoiding inaccurate tests or equipment damage caused by poor air flow or uneven temperature. By obtaining the heating efficiency and refrigeration coefficient through the heating and cooling system sensor data, through the sensor data of the heating and cooling system, the heating efficiency and refrigeration coefficient are calculated respectively to evaluate the performance of the heating and cooling system. By real-time monitoring the heating efficiency and refrigeration coefficient, it can ensure that the equipment always maintains the optimal temperature control state, improving the energy use efficiency of the equipment. When the performance of the heating and cooling system decreases, it can be found and adjusted in time, reducing the damage to the equipment caused by overloading, thereby extending the service life of the equipment. And the thermal coupling factor is obtained based on the heating efficiency and refrigeration coefficient. Based on the heating efficiency and refrigeration coefficient, the thermal coupling factor is calculated to reflect the heat energy conversion efficiency of the heating and cooling system and the working state of the overall thermal system. The calculation of the thermal coupling factor helps to understand the overall operating state of the thermal system and can be adjusted in time when there is poor heat energy transfer in the system, ensuring the stability of the temperature control system of the test chamber. By monitoring the thermal coupling factor, the anomalies of the heating and cooling system can be diagnosed more accurately, avoiding problems such as uneven high or low temperature. By obtaining the electrical stability index through the test system sensor data, through the sensor data of the test system,Obtain electrical stability indicators, evaluate the operational stability of the electrical system, timely detect the stability of the electrical system, avoid functional failure of the test box or damage to the equipment due to electrical system failures, ensure the stability of the electrical system, and prevent safety issues caused by electrical failures, such as short circuits and electrical fires. Obtain the probability of multiple sensor data deviating from the normal distribution through the sensor data of the equipment subsystem, and obtain the system entropy increase rate based on the multiple deviations from the normal distribution probability. Calculate the system entropy increase rate based on the deviations from the normal distribution probability of multiple sensor data to reflect the overall disorder and failure possibility of the system. Through the calculation of the entropy increase rate, the health status of the equipment can be evaluated from multiple dimensions, and potential failure modes in the system can be identified in a timely manner. The entropy increase rate provides a global monitoring tool for complex systems, which helps to cover all potential failure modes, especially those intermittent and low-probability failures. Obtain the failure probability index through the system entropy increase rate, electrical stability indicators, thermal coupling factors, wind system efficiency coefficients, and temperature control abnormality indexes, and determine based on the failure probability index The state of the test chamber is calculated by combining the above indicators to obtain the failure probability index, which provides a quantitative basis for the fault diagnosis of the equipment. By comprehensively calculating various indicators, the failure probability of the equipment can be comprehensively evaluated, reducing the limitation that a single indicator cannot fully reflect the problem. The failure probability index can be used to predict the type and severity of possible failures of the equipment in the early stage, providing decision support for repair and maintenance. According to the results of the failure probability index, the current state of the test chamber is determined to determine whether there is a potential failure or whether maintenance is required. It can quickly determine whether there is a potential failure in the test chamber, thereby reducing test interruptions or data errors caused by equipment failures. Through accurate fault prediction and state evaluation, preventive maintenance can be carried out in advance, equipment maintenance costs can be reduced, and downtime can be reduced, thereby not only improving the stability and reliability of the equipment, but also reducing maintenance costs, improving the efficiency of equipment use, ensuring the accuracy and safety of the test results, and solving the problems of insufficient fault diagnosis capabilities and difficulty in covering complex failure modes in the existing technology.

[0029] In one embodiment, the step S2 of acquiring the temperature control abnormality index according to the temperature control system sensor data and acquiring the wind system efficiency coefficient according to the wind circulation system sensor data comprises: S21, obtaining the cavity temperature, the set temperature and the temperature interval within a preset time period according to the temperature control system sensor data, and obtaining the temperature change acceleration according to the temperature interval and the preset time period; S22, obtaining a temperature deviation according to a ratio of a difference between the cavity temperature and a set temperature to the set temperature; S23, obtaining a maximum temperature and a minimum temperature according to the temperature interval, and obtaining a temperature range according to the difference between the maximum temperature and the minimum temperature; S24. Obtain the temperature volatility based on the temperature range difference and the set temperature, and obtain the temperature control anomaly index by calculating the weighted sum of the temperature volatility, temperature deviation degree, and temperature change acceleration; S25. Obtain the wind speed, wind pressure, fan current, and impeller speed data based on the wind circulation system sensor data, and obtain the basic efficiency factor based on the wind speed, wind pressure, and fan current; S26. Obtain the attenuation factor, actual speed, and rated speed based on the impeller speed data, and obtain the attenuation index based on the attenuation factor, actual speed, and rated speed; S27. Obtain the wind system efficiency coefficient based on the attenuation index and the basic efficiency factor. The calculation formula for the wind system efficiency coefficient is: ; where represents the wind system efficiency coefficient, represents the attenuation index, represents the attenuation factor, represents the actual speed, represents the rated speed, represents the basic efficiency factor, represents the wind speed, represents the wind pressure, represents the fan current.

[0030] As described in the above steps S21 - S27, in the calculation formulas of the temperature control anomaly index and the failure probability index, the respective parameters are normalized in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the cavity temperature, the set temperature, and the temperature range within a preset time period through the sensor data of the temperature control system. By collecting the data of the temperature control system, the actual cavity temperature, the set temperature, and the set temperature range can be obtained, which can help to judge the current temperature state of the test chamber and provide basic data for the subsequent abnormal diagnosis of temperature control. By obtaining the difference between the cavity temperature and the set temperature, the accuracy of temperature control can be evaluated in real time, thus effectively avoiding the influence of too high or too low temperature on the test results. The acquisition of the set temperature and the temperature range can help to evaluate whether the test chamber is within the normal working range, ensure operation under preset conditions, avoid abnormal fluctuations, and obtain the temperature change acceleration according to the temperature range and the preset time period. By the temperature range and the preset time period, the temperature change acceleration is calculated, that is, the rate of temperature change per unit time. This helps to monitor the timeliness of the temperature control system response. The temperature change acceleration can reflect the response speed and change trend of the temperature control system, and timely identify problems such as too slow or too fast system reaction. By monitoring the acceleration of temperature change, it helps to improve the system's response to temperature fluctuations and ensure that it reaches the set temperature within an appropriate time to avoid temperature oscillation. The temperature deviation is obtained through the cavity temperature and the set temperature. By calculating the difference between the cavity temperature and the set temperature, the temperature deviation is obtained, which reflects the current temperature control precision of the test chamber. The temperature deviation can accurately reflect the deviation between the actual temperature and the target set temperature, can help to detect possible faults or errors in the temperature control system, give early warning of system deviation, can monitor and optimize the temperature control system in real time, and make the temperature deviation controlled within the allowable range, thus ensuring the stability of the experimental environment and the accuracy of the test. The maximum temperature and the minimum temperature are obtained through the temperature range, and the temperature range is obtained according to the maximum temperature and the minimum temperature. By the temperature range data, the maximum value and the minimum value of the temperature are calculated, and further the temperature range is calculated, which reflects the amplitude of temperature fluctuation. As an amplitude index of temperature fluctuation, the temperature range can help to quickly judge whether there is too large temperature fluctuation in the temperature control system and timely discover the problem of unstable temperature control. The increase of the temperature range often indicates that some components of the temperature control system may fail. Timely identification of these abnormal fluctuations helps to improve the reliability of the equipment. The temperature fluctuation rate is obtained through the temperature range and the set temperature. By the temperature range and the set temperature, the temperature fluctuation rate is calculated to describe the temperature fluctuation situation of the system and further evaluate the stability of the temperature control system. The temperature fluctuation rate provides a quantitative index for the fluctuation situation of the temperature control system, can clearly reflect whether the temperature control system is stable, and timely discover abnormal states with large fluctuations. By analyzing the temperature fluctuation rate, the system can be adjusted before the occurrence of faults to improve the stability and precision of the temperature control system.Obtain the temperature control anomaly index based on the temperature volatility, temperature deviation, and temperature change acceleration. Combine the temperature volatility, temperature deviation, and temperature change acceleration to calculate the temperature control anomaly index, which is used to comprehensively evaluate the working state of the temperature control system, determine whether there is an anomaly. Through the combination of multiple indicators, the working state of the temperature control system can be evaluated more comprehensively and accurately, and potential failure risks can be identified. The temperature control anomaly index provides a quantitative anomaly evaluation standard for the temperature control system, which can issue early warnings when potential problems occur in the system and avoid large-scale failures. Obtain the wind speed, wind pressure, fan current, and impeller speed data through the wind circulation system sensor data, and obtain the basic efficiency factor based on the wind speed, wind pressure, and fan current. Collect the wind speed, wind pressure, fan current, and impeller speed of the wind circulation system to help comprehensively understand the working conditions of the wind circulation system and provide important data support for subsequent analysis of the wind system efficiency. Based on the wind speed, wind pressure, and fan current data, calculate the basic efficiency factor, which reflects the basic working efficiency of the wind circulation system. The basic efficiency factor can comprehensively reflect the working efficiency of the wind circulation system, help to detect problems of low wind system efficiency in a timely manner, and avoid affecting the temperature control system of the test chamber. Understanding the efficiency of the wind system in a timely manner helps to adjust and optimize the operating parameters of the wind system and improve the overall energy efficiency. Obtain the attenuation factor, actual speed, and rated speed through the impeller speed data, and obtain the attenuation index based on the attenuation factor, actual speed, and rated speed. Calculate the attenuation factor through the impeller speed data, and further calculate the attenuation index based on the attenuation factor, actual speed, and rated speed to evaluate the attenuation state of the wind system. The attenuation index can accurately reflect the performance degradation of the wind system over time, help to predict the life and efficiency decline of the wind system, and adjust the system state in a timely manner. By monitoring the attenuation of the wind system, preventive maintenance can be carried out before the fan efficiency decreases, and the service life of the wind system can be extended. Obtain the wind system efficiency coefficient based on the attenuation index and the basic efficiency factor. Combine the attenuation index and the basic efficiency factor to calculate the wind system efficiency coefficient, which comprehensively evaluates the overall efficiency of the wind circulation system. The wind system efficiency coefficient provides a comprehensive performance index for the wind circulation system, which can detect situations where the system does not meet the standards in a timely manner and avoid affecting the temperature control performance due to insufficient efficiency. Through the monitoring of the wind system efficiency coefficient, the working state of the wind system can be optimized, ensuring the stable operation of the temperature control system and avoiding temperature control problems caused by wind system failures.

[0031] In one embodiment, step S3 of obtaining the heating efficiency and refrigeration coefficient according to the heating and cooling system sensor data and obtaining the thermal coupling factor according to the heating efficiency and refrigeration coefficient includes: S31. Heat the data and cooling data according to the heating and cooling system sensor data, and obtain the heating power, heating resistance, and heating current according to the heating data; S32. Obtain the refrigerating capacity and the compressor power according to the refrigeration data, and obtain the coefficient of performance based on the ratio of the refrigerating capacity to the compressor power; S33. Obtain the heating efficiency according to the heating power, the heating resistance and the heating current, and obtain the energy efficiency ratio based on the heating efficiency and the coefficient of performance; S34. Obtain the actual pressure and the rated pressure of the refrigerant according to the refrigeration data, and obtain the pressure ratio based on the ratio of the actual pressure to the rated pressure; S35. Obtain the thermo - coupling factor according to the pressure ratio and the energy efficiency ratio, where the calculation formula of the thermo - coupling factor is: ; where, represents the thermo - coupling factor, represents the energy efficiency ratio, represents the heating efficiency, represents the heating power, represents the heating current, represents the heating resistance, represents the coefficient of performance, represents the pressure ratio.

[0032] As described in the above steps S31 - S35, in the calculation formula of the thermo - coupling factor, normalization processing is performed on their respective parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. In the present invention, the heating and cooling system sensor data, heating data, and cooling data are used. The heating power, heating resistance, and heating current are obtained from the heating data. These data reflect the working state of the heating system and directly affect the heating performance of the system. Traditional heating system monitoring methods mostly rely on static parameters or conventional temperature data, which may not accurately reflect the actual heating efficiency. By obtaining the heating power, resistance, and current data, the working state of the heating system can be monitored more precisely in real - time, and abnormalities in the heating circuit, such as problems like resistance changes and power fluctuations, can be detected in a timely manner, avoiding overheating or energy efficiency losses, and improving the stability and energy efficiency of the system. The cooling capacity and compressor power are obtained from the cooling data. By obtaining the cooling capacity and compressor power from the cooling data, this can accurately characterize the operation effect of the cooling system. In current technologies, the performance evaluation of the cooling system is mostly based on simple temperature differences or compressor speeds, which may ignore the non - linear relationship between the cooling capacity and the compressor power. By accurately obtaining the cooling capacity and compressor power, the working efficiency of the cooling system can be evaluated more comprehensively, especially under complex load change conditions, providing effective support for diagnosing system abnormalities and inefficiencies. The coefficient of performance (COP) is obtained based on the cooling capacity and compressor power. Calculating the coefficient of performance according to the cooling capacity and compressor power is a key parameter for evaluating the efficiency of the cooling system. The coefficient of performance is the core index for measuring the efficiency of the cooling system. In existing technologies, the calculation of the coefficient of performance may rely on static reference data, ignoring the dynamic changes in actual operations. By calculating the coefficient of performance in real - time, the fluctuations in system efficiency can be detected more flexibly, providing a more accurate fault warning than traditional methods and providing a strong basis for system optimization. The heating efficiency is obtained from the heating power, heating resistance, and heating current. Calculating the heating efficiency from the heating power, heating resistance, and heating current data, the heating efficiency is a direct indicator for judging the energy efficiency of the heating system. In existing technologies, when calculating the heating efficiency, overly simple formulas or static parameters are usually used, which cannot accurately reflect the actual heating effect during operation. By combining real - time heating power, resistance, and current data, the energy efficiency loss during the heating process can be accurately evaluated, faults can be detected and repaired in a timely manner, thereby reducing unnecessary energy consumption and improving the energy efficiency and sustainability of the system. The energy efficiency ratio is obtained based on the heating efficiency and the coefficient of performance. The energy efficiency ratio is an important indicator for evaluating the comprehensive performance of the entire device (heating and cooling systems). In existing technologies, the calculation of the energy efficiency ratio often relies on the energy efficiency data of a single system, ignoring the synergistic effect between heating and cooling. By calculating the energy efficiency ratio by combining the heating efficiency and the coefficient of performance, a comprehensive evaluation of the overall performance of the device can be provided, revealing possible design or operation bottlenecks.It helps with overall optimization. By obtaining the actual pressure and rated pressure of the refrigerant through refrigeration data and getting the pressure ratio based on the actual pressure and rated pressure. In the existing technology, the pressure ratio is usually ignored or evaluated through theoretical preset, lacking accurate measurement under actual operating conditions. By obtaining the actual pressure and rated pressure data in real time, it can dynamically monitor the operating status of the refrigeration system, timely identify potential risks of abnormal pressure (for example, too high or too low pressure may cause equipment damage or reduced efficiency), greatly improving the monitoring and maintenance efficiency of the refrigeration system. By obtaining the thermal coupling factor through the pressure ratio and energy efficiency ratio, the thermal coupling factor is a key parameter for comprehensively evaluating the collaborative work of heating and refrigeration systems. The coupling of heating and refrigeration systems in the existing technology is often not deep enough, making it difficult to effectively evaluate in the case of multiple variables. By calculating the thermal coupling factor by combining the pressure ratio and energy efficiency ratio, it can quantify the interaction between the two, help locate the cause of the fault, provide a more accurate system debugging and optimization plan, improve the accuracy of fault diagnosis, be able to give timely warnings for potential faults, especially improve the detection ability for low-probability faults or intermittent faults, optimize the collaborative diagnosis of equipment, break through the limitation of independent operation of each device in traditional technology, be able to comprehensively reflect the interaction and status between devices, and by dynamically calculating the energy efficiency ratio and thermal coupling factor, enhance the ability of equipment operation optimization, help the equipment achieve higher operating efficiency and lower energy consumption, solve the challenges brought by the equipment complexity and diverse fault modes in the existing technology, improve the efficiency of fault location and repair, and extend the service life of the equipment.

[0033] In one embodiment, the step S4 of obtaining the electrical stability index according to the sensor data of the test system includes: S41. Obtain the total number of transmitted bits and the number of error bits according to the sensor data of the test system, and obtain the bit error rate according to the ratio of the number of error bits to the total number of transmitted bits; S42. Obtain the real-time voltage and the nominal voltage according to the sensor data of the test system, and obtain the power supply voltage volatility according to the ratio of the real-time voltage to the nominal voltage; S43. Obtain the cumulative stress of the test chamber during the test cycle according to the power supply voltage volatility and the bit error rate; S44. Obtain the instruction sending time and the instruction receiving time according to the sensor data of the test system, and obtain the response delay of the storage chip according to the difference between the instruction sending time and the instruction receiving time; S45. Obtain the electrical stability index according to the response delay of the storage chip and the cumulative stress, where the calculation formula of the electrical stability index is: ; Wherein, represents the electrical stability index, Indicates the response delay of the memory chip, Indicates the accumulated stress, Indicates the power supply voltage fluctuation rate, Indicates the bit error rate.

[0034] As described in the above steps S41 - S45, in the calculation formula of the electrical stability index, the respective parameters are normalized in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the total number of transmitted bits and the number of error bits through the sensor data of the test system. Obtaining the total number of transmitted bits and the number of error bits through the sensor data of the test system helps to accurately calculate the bit error rate, thereby evaluating the data transmission quality of the storage chip under the actual working condition, being able to monitor the communication stability of the chip in real time, providing effective data support for subsequent fault diagnosis, avoiding the wrong judgment or omission that may occur in the traditional method due to the lack of real - time monitoring, and obtaining the bit error rate based on the number of error bits and the total number of transmitted bits. Calculating the bit error rate can objectively reflect the proportion of error bits generated during the transmission of the storage chip, can quickly identify the reliability and performance of the storage chip, evaluate the transmission quality of the chip immediately during the test process, and discover potential transmission faults or performance degradation in advance, effectively avoiding the disadvantages of relying on manual intervention or having a long time lag in the traditional method, improving the timeliness of fault diagnosis. By obtaining the real - time voltage and the nominal voltage through the sensor data of the test system, and by obtaining the difference between the real - time voltage and the nominal voltage, the fluctuation of the supply voltage can be monitored in real time. The fluctuation of the supply voltage is one of the common reasons for the failure of the storage chip. Obtaining this data in real time helps to troubleshoot the faults caused by the unstable power supply voltage, improving the sensitivity to the power supply voltage fluctuation faults, avoiding the limitation of the traditional method that fails to capture the power supply problem in time, and obtaining the supply voltage volatility based on the real - time voltage and the nominal voltage. Obtaining the supply voltage volatility can further quantify the stability of the power supply. The volatility of the supply voltage has a direct impact on the stability of the storage chip. Therefore, it can accurately evaluate the impact of the power supply fluctuation on the chip, and thus provide a reliable basis for the fault diagnosis of the device, avoiding the defect of the traditional method that cannot accurately quantify the power supply problem and improving the accuracy of fault diagnosis. By obtaining the cumulative stress of the test chamber during the test cycle through the supply voltage volatility and the bit error rate, and by comprehensively considering the power supply fluctuation and the bit error rate to evaluate the cumulative stress of the test chamber during the test cycle, it can accurately reflect the working condition of the device in a long - time and high - stress environment, can help to judge the durability of the device under high voltage and long - time operation, can predict in advance the possible faults of the device in an extreme environment, avoiding the limitation of only relying on a single index judgment in the traditional fault detection. By obtaining the instruction sending time and the instruction receiving time through the sensor data of the test system, and obtaining the response delay of the storage chip based on the instruction sending time and the instruction receiving time, by measuring the response delay of the storage chip, the efficiency and real - time performance of the chip in processing instructions can be quantified, which helps to evaluate whether there is an excessive response delay in the chip during actual application, thus affecting the overall performance of the system. Compared with the traditional method that only relies on hardware detection, this step can deeply analyze the working state of the chip.It can more effectively capture low-probability and intermittent faults, especially for the detection of response speed, and has significant advantages in diagnosing the performance and response ability of the device. By obtaining the electrical stability index through the response delay and cumulative stress of the storage chip, and combining the response delay and cumulative stress to evaluate the electrical stability, it can comprehensively evaluate the electrical performance of the storage chip, identify the chip performance decline caused by long-term operation and electrical stress, and discover potential faults that may lead to system crashes or data loss in advance. It is more comprehensive and in-depth than traditional single-fault mode detection, and can provide a strong diagnostic basis for the electrical stability of the device. Through the real-time acquisition and comprehensive analysis of multiple key parameters, the present invention can more accurately diagnose the fault mode of the storage chip in the high and low temperature aging test chamber, overcome the problems of device complexity and fault mode diversity in the prior art, especially for the detection of intermittent and low-probability faults. The present invention provides a more accurate means. Through real-time monitoring and multi-dimensional data analysis, it can comprehensively cover potential fault modes, and due to the adoption of multiple sensors and data fusion technology, it can effectively achieve collaborative diagnosis between devices, accurately locate the fault source, thereby improving the reliability and efficiency of the entire test system.

[0035] In one embodiment, step S5 of obtaining the probability of multiple sensor data deviating from the normal distribution according to the sensor data of the device subsystem and obtaining the system entropy increase rate according to the multiple probabilities of deviating from the normal distribution includes: S51. Respectively obtain multiple sensor data sets according to the sensor data of the device subsystem, and obtain the corresponding data mean according to each sensor data set; S52. Obtain the corresponding data standard deviation according to each data mean, and obtain the corresponding probability of deviating from the normal distribution according to each data standard deviation and the sensor data set; S53. Calculate the entropy value of the corresponding sensor according to each probability of deviating from the normal distribution, where the calculation formula is: ; Wherein, represents the entropy value, represents the probability of deviating from the normal distribution; And sum the multiple entropy values to obtain the system entropy increase rate.

[0036] As described in the above steps S51 - S53, the present invention obtains multiple sensor data sets through the sensor data of the device subsystem. By obtaining multiple sensor data sets respectively, it can comprehensively monitor different components and working states of the device. In the prior art, it may only rely on single - sensor data, which is likely to overlook the complexity and diversity of the device. The solution using multiple sensors can obtain device information from multiple perspectives, which helps to comprehensively evaluate the device, improve the accuracy and reliability of fault diagnosis, and obtain the corresponding data mean according to each sensor data set. The data mean, as an index describing the central tendency of sensor data, can help eliminate the influence of outliers or noise in single - sampling data. By calculating the mean, the "normal state" of the device at a certain moment can be obtained, making subsequent anomaly detection more accurate, helping to improve the detection ability of low - probability faults, and reducing misjudgment caused by data fluctuations. By obtaining the corresponding data standard deviation for each data mean, the standard deviation is an effective index to measure the volatility and dispersion degree of data. By calculating the standard deviation of each sensor data set, the stability of the device working state can be identified. If the device state is relatively stable, the standard deviation is small, while if there are anomalies or faults, the standard deviation will increase. The introduction of the standard deviation can effectively distinguish the normal fluctuations of the device from the drastic changes caused by faults, enhancing the accuracy - positioning ability of faults, and obtaining the corresponding probability of deviating from the normal distribution according to each data standard deviation and sensor data set. In device fault detection, many normal data conform to the normal distribution. By calculating the probability of each sensor data set deviating from the normal distribution, abnormal situations can be identified. If the output value of a certain sensor significantly deviates from the normal distribution, it may imply that the device has a fault or abnormal state. The calculation of this probability can capture intermittent and low - probability faults that cannot be timely identified by traditional methods, thus enhancing the coverage ability for various potential faults. By obtaining the entropy value of the corresponding sensor for each probability of deviating from the normal distribution, the entropy value is an important concept in information theory, representing the uncertainty or chaos degree of the system. The entropy value can reflect the complexity of the device state. By calculating the entropy value, the change law of the device under different operating states can be quantified. For complex systems, the entropy value can help identify irregular patterns, further improving the sensitivity of fault diagnosis. Especially when dealing with complex devices and fault modes, the introduction of the entropy value can greatly enhance the system's monitoring ability of the device operating state, and obtaining the system entropy increase rate according to multiple entropy values. The calculation of the entropy increase rate can reflect the change trend of the entire device system, which is an important index for evaluating the health status of the system. When the system entropy increase rate is relatively high, it may indicate an increase in the chaos degree of the device system, with faults or potential fault risks. Through the monitoring of the entropy increase rate, multiple sensor data can be comprehensively considered, the operating state of the device can be comprehensively evaluated, the ability of multi - device collaborative diagnosis can be improved, and it helps to accurately locate the area and cause of the fault. Compared with the prior art, the method of the present invention can more comprehensively cover potential fault modesEspecially, it can provide more accurate fault diagnosis under the action of multiple factors. Through data collection and analysis of multiple sensors, the present invention helps to comprehensively cover various fault modes that may occur in the equipment. By combining multiple indicators such as data mean, standard deviation, and entropy value, it can accurately analyze the equipment from multiple dimensions. In particular, the detection ability for low-probability and intermittent faults is significantly improved. It can combine the working states of multiple devices to perform collaborative fault diagnosis, avoiding the problem of precise positioning that cannot be achieved by a single device in traditional technologies. It can handle the situation of a large variety of equipment types and complex working states, is applicable to multiple different types of equipment, and has strong application scenarios and wide practical value.

[0037] In one embodiment, step S6 of determining the state of the test chamber according to the fault probability index includes: S61. Determine whether the fault probability index is greater than a preset threshold; S62. If the fault probability index is greater than the preset threshold, it is determined that the test chamber is in a faulty state at this time, and the relationship between the fault probability index and the preset threshold interval is judged; S63. If the fault probability index is greater than the upper limit value of the preset threshold interval, it is determined that the fault degree is serious; S64. If the fault probability index is within the preset threshold interval, it is determined that the fault degree is general; S65. If the fault probability index is less than the lower limit value of the preset threshold interval, it is determined that the fault degree is minor; S66. If the fault probability index is not greater than the preset threshold, it is determined that the test chamber is in a normal state at this time.

[0038] As described in the above steps S61 - S66, the present invention determines whether the failure probability index is greater than a preset threshold. If the failure probability index is greater than the preset threshold, it is determined that the test chamber is in a failure state at this time. By setting a preset threshold, if the failure probability index of the test chamber is greater than this threshold, it indicates that the test chamber is in a potential failure state and further failure assessment is required. Through the setting of the preset threshold, a large number of equipment states can be quickly screened out, reducing unnecessary diagnostic steps. The comparison between the failure probability index and the threshold can quickly determine whether the equipment has entered a failure state, improving the response speed of failure detection and reducing the possible losses of the test chamber. Compared with the traditional complex failure diagnosis system, this step can quickly determine whether there is a potential failure, thus reducing the resources consumed in complex system analysis, and determining the relationship between the failure probability index and the preset threshold interval. If the failure probability index is greater than the upper limit value of the preset threshold interval, it is determined that the failure degree is serious; if the failure probability index is within the preset threshold interval, it is determined that the failure degree is general; if the failure probability index is less than the lower limit value of the preset threshold interval, it is determined that the failure degree is minor; if the failure probability index is not greater than the preset threshold, it is determined that the test chamber is in a normal state at this time. By judging the relationship (upper limit, lower limit) between the failure probability index and the preset threshold interval, the severity of the failure is further evaluated. Through the classification of different failure probabilities, targeted treatment solutions can be provided for different degrees of failures, which plays an important role in improving the accuracy of failure diagnosis. By real-time tracking of the failure probability index, potential minor failures and more serious failures can be detected in a timely manner, so as to take maintenance measures in advance to avoid major failures of the equipment. According to the relationship between the failure probability index and the threshold interval, maintenance personnel can clearly understand the specific situation of the equipment failure, make more accurate maintenance decisions, and improve the use efficiency and service life of the equipment. The present invention can accurately, real-time, and hierarchically evaluate the state of the test chamber by using the method of comparing the failure probability index with the threshold, effectively overcoming the problems of diagnostic blind spots, insufficient low-probability failure detection ability, and difficult equipment collaborative diagnosis in the prior art. Through refined failure judgment, the system can quickly respond, ensure the normal operation of the equipment, reduce the failure downtime, and improve the reliability and safety of the test chamber. In addition, through resource optimization and accurate failure location, it helps to save the diagnostic cost and provides strong data support for maintenance decisions.

[0039] Embodiment 1: As Figure 2 shown, the present application provides a self-diagnosis system for the high and low temperature aging test chamber of a storage chip, including: A first acquisition module, configured to acquire the sensor data of the equipment subsystems of the high and low temperature aging test chamber, wherein the sensor data of the equipment subsystems includes the sensor data of the temperature control system, the sensor data of the air circulation system, the sensor data of the heating and cooling system, and the sensor data of the test system; A second acquisition module, configured to obtain a temperature control anomaly index according to the temperature control system sensor data, and obtain a wind system efficiency coefficient according to the wind circulation system sensor data; A third acquisition module, configured to obtain a heating efficiency and a refrigeration coefficient according to the heating and refrigeration system sensor data, and obtain a thermal coupling factor according to the heating efficiency and the refrigeration coefficient; A fourth acquisition module, configured to obtain an electrical stability index according to the test system sensor data; A fifth acquisition module, configured to obtain probabilities of multiple sensor data deviating from a normal distribution according to the device subsystem sensor data, and obtain a system entropy increase rate according to the probabilities of multiple deviations from the normal distribution; A determination module, configured to obtain a failure probability index according to the system entropy increase rate, the electrical stability index, the thermal coupling factor, the wind system efficiency coefficient, and the temperature control anomaly index, and determine the state of the test chamber according to the failure probability index.

[0040] In one embodiment, the second acquisition module includes: A first acquisition unit, configured to obtain a chamber temperature, a set temperature, and a temperature range within a preset time period according to the temperature control system sensor data, and obtain a temperature change acceleration according to the temperature range and the preset time period; A second acquisition unit, configured to obtain a temperature deviation degree according to the chamber temperature and the set temperature; A third acquisition unit, configured to obtain a maximum temperature and a minimum temperature according to the temperature range, and obtain a temperature range difference according to the maximum temperature and the minimum temperature; A fourth acquisition unit, configured to obtain a temperature volatility according to the temperature range difference and the set temperature, and obtain a temperature control anomaly index according to the temperature volatility, the temperature deviation degree, and the temperature change acceleration; A fifth acquisition unit, configured to obtain wind speed, wind pressure, fan current, and impeller rotation speed data according to the wind circulation system sensor data, and obtain a basic efficiency factor according to the wind speed, the wind pressure, and the fan current; A sixth acquisition unit, configured to obtain an attenuation factor, an actual rotation speed, and a rated rotation speed according to the impeller rotation speed data, and obtain an attenuation index according to the attenuation factor, the actual rotation speed, and the rated rotation speed; A seventh acquisition unit, configured to obtain a wind system efficiency coefficient according to the attenuation index and the basic efficiency factor.

[0041] Embodiment 2: As Figure 3As shown in the figure, the present application also provides a fault self-diagnosis system for a high and low temperature aging test chamber of a storage chip, which includes an input module, a monitoring and diagnosis module, a recording and display module, and an output module; the main function of the input module is to sense, and collect the operation status data of the device in real time through multiple sensors distributed inside the device and transmit it to the monitoring and diagnosis module; the main function of the monitoring and diagnosis module is to analyze and make decisions, analyze and process the data through the built-in intelligent algorithm, determine the dynamic threshold based on historical data and empirical values, detect abnormal data and cross-verify the data of different sensors, and finally give accurate fault diagnosis information; the main function of the recording and display module is to store and indicate the operation status and fault information of the device, archive the operation status data of the device before and after the fault into the system log, and display the operation status, real-time / historical data curve and fault diagnosis information of the device, etc.; the main function of the output module is to execute, receive the control instructions sent by the monitoring and diagnosis module, and trigger the corresponding electrical components to perform physical protection actions, such as shutting off the power supply / gas source / water source, etc.

[0042] After a storage chip high and low temperature aging test chamber fault self-diagnosis system completes data sampling through the input module, it transmits the data to the monitoring and diagnosis module to analyze and judge the authenticity of the data, data anomalies, sensor failures, protection device actions, the working conditions of key components, and whether the system is operating normally, etc., and obtains a diagnosis result: fault precursor, general fault, serious fault. The monitoring and diagnosis module issues an action instruction to the output module to execute the alarm protection action. At the same time, the recording and display module archives the device operation data before and after the alarm and displays the alarm information and maintenance suggestions.

[0043] In one embodiment, as Figure 4 shown, it is a schematic diagram of the first diagnostic process. After the high and low temperature test chamber starts to run, the input module collects the operation status data of the machine and transmits it to the monitoring and diagnosis module. The monitoring and diagnosis module checks the authenticity of the data (for example, the sensor data exceeds the actual measurement range). If the data is not true, it transfers to the second diagnostic process. If the data is true, it compares and analyzes the current data with the historical big data. If abnormal data is detected (for example, there is a certain deviation between the motor running current data and the historical data, but it does not exceed the alarm threshold), it continues to comprehensively analyze the operation status of each subsystem. If this anomaly affects the system operation, it transfers to the third diagnostic process. If it does not affect the system operation, it outputs a diagnostic result: fault precursor (which belongs to data anomaly and cannot be called a fault); the processing strategy for the fault precursor is that the recording and display module archives and marks the abnormal data, and at the same time increases the detection frequency of the abnormal data by the input module, improves the system response speed, displays the alarm information to prompt the operator to pay attention, and gives maintenance and maintenance suggestions (for example, after the current round of high and low temperature aging test is completed, check in time whether the motor output shaft needs to be cleaned of stains and lubricated).

[0044] AsFigure 5 As shown, it is a schematic diagram of Diagnostic Process 2. After the high and low temperature test chamber starts and runs, the input module collects the operating status data of the machine tool and transmits it to the monitoring and diagnostic module. If the data is not true, it will be matched and analyzed with the sensor fault model, and whether the spare sensor is available will be detected. If it is not available, it will transfer to Diagnostic Process 3. If it is available, the system will automatically switch to the spare sensor, collect new data and monitor whether the system returns to normal. If it does not return to normal, it will transfer to Diagnostic Process 3. If the system returns to normal, the diagnostic result will be output: general fault (belonging to non-stop machine fault, which needs to be closely monitored); the processing strategy for general faults is that the recording and display module archives and marks the fault data, updates the sensor data to ignore the faulty sensor at the same time, displays an alarm message to prompt the operator to pay attention, and gives maintenance and repair suggestions (for example, after the current high and low temperature aging test is completed, promptly repair and replace the faulty sensor).

[0045] As Figure 6 As shown, it is a schematic diagram of Diagnostic Process 3. After the high and low temperature test chamber starts and runs, the input module collects the operating status data of the machine tool and transmits it to the monitoring and diagnostic module. If the data is not true, it will transfer to Diagnostic Process 1. If there is a sensor fault, it will transfer to Diagnostic Process 2. If the data is true and there is no sensor fault, the following will be detected in sequence: 1. Whether the protection device acts (for example, the leakage protector / overtemperature protector / overheat protector, etc. are triggered); 2. Whether the key components are faulty (for example, electrical components in the air circulation system / refrigeration system / heating system are detected to be faulty or multiple sensors, including the spare sensor, are faulty); 3. Whether the system operation is abnormal (for example, the temperature deviation is too large, unable to heat up or cool down, etc.). If any of the above three conditions is met, the diagnostic result will be output: serious fault (belonging to a stop machine fault, which requires the execution of a protection action); the processing strategy for serious faults is that the output module executes a stop protection action, and the monitoring and diagnostic module synchronously detects whether the equipment has stopped running. If it has not stopped yet, the spare actuator will be switched (for example, triggering the shunt trip mechanism of the main power circuit breaker to cut off the main power supply emergently), and the main power supply will be forcibly cut off to ensure the safety of the equipment. The recording and display module will display the alarm message in a prominent form, archive the fault information and the running data before and after it into the system log, and the output module triggers the alarm light and the alarm bell to give an audible and visual prompt.

[0046] In the prior art, the operating data of a device is collected in real time by multiple sensors distributed in a high and low temperature test chamber, such as pressure sensors and temperature sensors. When it is detected that a certain data exceeds the threshold range (including the first / second / third threshold, etc.), the system executes a protection action and outputs an alarm signal to prevent the further expansion of equipment failures. In the prior art, although various sensors are arranged in the high and low temperature aging test chamber for storage chips, due to the lack of linkage and redundancy, there is a risk of false alarms or missed alarms due to incorrect data of a single sensor. The prior art is limited to the emergency disposal after a failure, which is a passive defense and does not have the ability to predict potential failures in advance, reducing the working efficiency of the device. When the device alarms and shuts down, the system only records the alarm information and some operating data, etc. Due to the limited amount of information, it is time-consuming and laborious to trace the cause of the failure, and it is difficult to discover and make up for design defects in a timely manner, hindering the equipment iteration and upgrade process. The prior art realizes the disposal of failures through the action of a single actuator. Due to the lack of redundancy, there is a risk that the protection action fails due to actuator failure, thus triggering serious accidents and threatening the safety of the device and personnel. Specifically, the input module consists of various sensors, signal transmitters, and communication cards (communicating with electrical components to read status data) distributed in the high and low temperature aging test chamber for storage chips. Its main function is to collect the operating status data of the device in real time and transmit it to the monitoring and diagnosis module for processing. To avoid the impact of a single sensor failure or the common cause failure of the same type of sensors on the closed-loop system, the sensors in the present invention adopt redundant (multiple sensors detect the same physical quantity) and heterogeneous (different sensor detection principles) designs. In the temperature control system, the input module includes an inner chamber temperature sensor for detecting the temperature of the inner chamber and serving as the PV value (actual temperature measurement value) of the temperature control system; an inner chamber over-temperature protector for independently detecting whether the inner chamber is over-temperature; a main power supply monitoring sensor for monitoring whether the plant power supply is abnormal (phase loss / phase sequence error / overvoltage / undervoltage, etc.); a cooling water temperature / pressure sensor for detecting the flow rate and temperature of the plant water supply; a liquid leakage detection sensor for detecting whether there is water leakage in the compressor compartment; a CDA (dry compressed air) pressure / flow sensor for detecting the CDA supply pressure and flow rate of the plant. In the air circulation system, the input module includes a fan power supply closing detection sensor for detecting the status of the fan power circuit breaker (closing / tripping); an air pressure difference detection sensor for detecting whether the air pressure difference inside and outside the air duct is normal; a frequency converter communication card for reading data such as the running current / voltage / frequency of the motor and transmitting it to the monitoring and diagnosis module. In the heating system, the input module includes a main heating wire leakage protector for detecting whether the insulation of the heating wire is abnormal (ground short circuit will trigger leakage tripping); a main heating wire power supply closing detection sensor for detecting the status of the main heating wire power circuit breaker; a main heating wire current sensor for detecting the working current of the main heating wire;The main heating wire overtemperature protector is used to independently detect whether the main heating wire is overheated. In the refrigeration system, the input module includes a compressor power supply closing detection sensor for detecting the status of the compressor power circuit breaker; temperature / pressure sensors at the compressor suction and discharge ports for detecting the temperature and pressure data at the compressor suction and discharge ports; a mechanical overpressure protector for independently detecting whether the compressor suction / discharge ports are overpressured; a compressor overheat protector for independently detecting whether the compressor is overheated; a compressor / refrigerant solenoid valve current sensor for detecting the operating current of the compressor / refrigerant solenoid valve. In the test system, the input module includes a test power supply closing detection sensor for detecting the status of the test power circuit breaker; a cooling fan speed measurement sensor for detecting the rotation speed of the cooling fan; a test power communication card for reading data such as current / voltage of the input and output; a test motherboard temperature sensor for detecting the surface temperature of the test motherboard.

[0047] The function of the recording and display module is to collect, store, and display device-related data transmitted by the monitoring and diagnosis module, provide a graphical interface to display the device operation status, real-time data curves, historical data curves, and fault diagnosis information, etc., support querying historical records and client remote access, facilitating maintenance personnel to monitor and respond in real time. The recording and display module can be an independent display with storage function or a touch screen including display storage control function, or it can be integrated in the touch screen of the high and low temperature aging test chamber. The specific implementation method is not specified here. The output module consists of various actuators configured in the high and low temperature aging test chamber of the storage chip, and its function is to receive the control instructions of the monitoring and diagnosis module and execute entity control actions, such as cutting off the power / gas source / water source, etc.

[0048] In the temperature control system, the output module includes the shunt trip mechanism of the main power circuit breaker, the electric control valve for cooling water, and the CDA solenoid valve. The shunt trip mechanism of the main power circuit breaker can cut off the main power supply in case of an emergency. The electric control valve for cooling water is used to control the flow rate of the cooling water. The CDA solenoid valve is used to control the on / off of the CDA. In the air circulation system, the output module includes the fan power contactor, the frequency converter, and the control relay (abbreviation of the intermediate relay). The fan power contactor is used to control the power on and off of the fan frequency converter (connect and disconnect the power supply). The fan frequency converter is used to control the operation and frequency adjustment (speed control) of the fan. The fan control relay is used to control the start and stop of the fan. In the heating system, the output module includes the main heating wire power contactor and the solid state relay. The former is used to control the power on and off of the main heating wire power supply, and the latter is used to frequently start and stop the main heating wire to achieve fine control of the heating amount. In the refrigeration system, the output module is the compressor power contactor, the refrigerant solenoid valve, and the solid state relay. The compressor power contactor is used to control the start and stop of the compressor. The refrigerant solenoid valve is used to control the flow rate and flow direction of the refrigerant. The solid state relay is used to frequently start and stop the refrigerant solenoid valve to achieve fine control of the refrigeration amount. In the test system, the output module is the test power contactor and the cooling fan. The former is used to control the power on and off of the test power supply, and the test power supply provides DC power for the test motherboard. The latter is used to cool down the test motherboard.

[0049] The operation mode of a fault self-diagnosis system for a high and low temperature aging test chamber of a storage chip: After the equipment starts high and low temperature operation, the monitoring and diagnosis module detects the closed-loop operation status of each subsystem. The monitoring and diagnosis module polls and collects the real-time status data of the equipment transmitted by the input module. If data anomalies are found after processing and judgment, such as the fan has not started running but the air pressure difference signal is detected, the fault information and alarm prompt information will be transmitted to the recording and display module. The recording and display module indicates the fault information to prompt the operator to handle it in time and saves the fault data to the system log. At the same time, the monitoring and diagnosis module independently judges whether to switch to the standby sensor (no fault in data verification) or ignore the data of the faulty sensor to ensure that the machine can start high and low temperature operation normally.

[0050] The temperature control system starts running. Multiple temperature sensors distributed in the high and low temperature aging test chamber of the storage chip collect temperature data in real time. The power supply monitoring relay detects the power supply parameters at the factory end (such as phase voltage, phase sequence, etc.). The water supply temperature and pressure sensors detect the water supply temperature and pressure data at the factory end. The air supply pressure and flow sensors detect the pressure and flow data of the dry compressed air transported from the factory end to the equipment. The sensor data of the temperature control system is summarized and transmitted to the monitoring and diagnosis module; the fan in the air circulation system starts running. The air pressure difference sensor monitors the air pressure difference between the inner box and the outer box. The frequency converter real-time feedbacks the operating current, voltage, frequency and other data of the fan and sends them to the monitoring and diagnosis module; the heater in the heating system starts running. The heating current sensor detects the real-time operating current of the heater. The leakage protector and over-temperature protector detect whether the heater is leaking or overheating. The operating data of the heating system is summarized and sent to the monitoring and diagnosis module; the compressor and refrigerant solenoid valve in the refrigeration system start running. The temperature sensors and pressure sensors at the suction and discharge ports of the compressor detect the temperature and pressure data of the compressor's suction / discharge. The mechanical overpressure protector and the compressor overheat protector detect whether there is overpressure or overheating during the operation of the compressor. The current sensors of the compressor and the refrigerant solenoid valve detect the operating current in real time. The operating data of the refrigeration system is summarized and sent to the monitoring and diagnosis module.

[0051] The test power supply and the cooling fan in the test system start running. The test power supply and the cooling fan real-time feedback the status signals. The test power supply communication card transmits and feedbacks the output voltage and current data of the test power supply. The test motherboard temperature sensor detects the surface temperature of the test motherboard.

[0052] After receiving the operating data transmitted by the input modules of each subsystem of the equipment, the monitoring and diagnosis module analyzes and processes the data by the built-in intelligent algorithm, determines the dynamic threshold based on the historical data and empirical values, detects the abnormal data and compares the data of different sensors for cross-verification. Finally, it gives accurate fault diagnosis information, saves the equipment operating status data before and after the fault through the recording and display module, indicates the fault content, synchronously sends an action instruction to the output module for execution. If the system does not receive the feedback signal of the completion of the action of the output module, it switches to the redundant actuator to act to ensure that the fault handling action has been executed and completed.

[0053] It should be noted that each module and unit in the fault self-diagnosis system of the high and low temperature aging test chamber of the storage chip corresponds one by one to the steps in the fault self-diagnosis method of the high and low temperature aging test chamber of the storage chip.

[0054] As Figure 7 shown, the present application also provides a computer device, which can be a server, and its internal structure can be as Figure 7As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store all the data required for the process of the fault self-diagnosis method of the high and low temperature aging test chamber for storage chips. The network interface of the computer device is used to communicate with an external terminal via a network connection. The computer program, when executed by the processor, implements the fault self-diagnosis method of the high and low temperature aging test chamber for storage chips.

[0055] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.

[0056] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the above-mentioned fault self-diagnosis methods of the high and low temperature aging test chamber for storage chips.

[0057] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0058] It should be noted that in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, apparatus, article or method comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, apparatus, article or method comprising such element.

[0059] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for self-diagnosis of faults in a high and low temperature aging test chamber for storage chips, characterized in that, Including: Obtaining the sensor data of the equipment subsystem of the high and low temperature aging test chamber, wherein the equipment subsystem sensor data includes temperature control system sensor data, air circulation system sensor data, heating and cooling system sensor data, and test system sensor data; Obtaining a temperature control anomaly index according to the temperature control system sensor data, and obtaining an air system efficiency coefficient according to the air circulation system sensor data; Obtaining a heating efficiency and a refrigeration coefficient according to the heating and cooling system sensor data, and obtaining a thermal coupling factor according to the heating efficiency and the refrigeration coefficient; Obtaining an electrical stability index according to the test system sensor data; Obtaining the probabilities of multiple sensor data deviating from the normal distribution according to the equipment subsystem sensor data, and obtaining a system entropy increase rate according to the multiple probabilities of deviating from the normal distribution; Obtaining a failure probability index according to the system entropy increase rate, the electrical stability index, the thermal coupling factor, the air system efficiency coefficient, and the temperature control anomaly index, and determining the state of the test chamber according to the failure probability index.

2. The method for self-diagnosing faults of a high and low temperature aging test chamber for storage chips according to claim 1, characterized in that, The steps of obtaining a temperature control anomaly index according to the temperature control system sensor data and obtaining an air system efficiency coefficient according to the air circulation system sensor data include: Obtaining the chamber temperature, the set temperature, and the temperature range within a preset time period according to the temperature control system sensor data, and obtaining a temperature change acceleration according to the temperature range and the preset time period; Obtaining a temperature deviation degree according to the chamber temperature and the set temperature; Obtaining the maximum temperature and the minimum temperature according to the temperature range, and obtaining a temperature range difference according to the maximum temperature and the minimum temperature; Obtaining a temperature volatility according to the temperature range difference and the set temperature, and obtaining a temperature control anomaly index according to the temperature volatility, the temperature deviation degree, and the temperature change acceleration; Obtaining wind speed, wind pressure, fan current, and impeller rotation speed data according to the air circulation system sensor data, and obtaining a basic efficiency factor according to the wind speed, the wind pressure, and the fan current; Obtaining a decay factor, an actual rotation speed, and a rated rotation speed according to the impeller rotation speed data, and obtaining a decay index according to the decay factor, the actual rotation speed, and the rated rotation speed; Obtaining an air system efficiency coefficient according to the decay index and the basic efficiency factor.

3. The fault self-diagnosis method of the high and low temperature aging test chamber for storage chips according to claim 1, wherein The steps of obtaining a heating efficiency and a refrigeration coefficient according to the heating and cooling system sensor data, and obtaining a thermal coupling factor according to the heating efficiency and the refrigeration coefficient include: Heating data and refrigeration data according to the heating and cooling system sensor data, and obtaining a heating power, a heating resistance, and a heating current according to the heating data; Obtaining a refrigeration capacity and a compressor power according to the refrigeration data, and obtaining a refrigeration coefficient according to the refrigeration capacity and the compressor power; Obtaining a heating efficiency according to the heating power, the heating resistance, and the heating current, and obtaining an energy efficiency ratio according to the heating efficiency and the refrigeration coefficient; Obtaining the actual pressure and the rated pressure of the refrigerant according to the refrigeration data, and obtaining a pressure ratio according to the actual pressure and the rated pressure; Obtaining a thermal coupling factor according to the pressure ratio and the energy efficiency ratio.

4. The fault self-diagnosis method of the high and low temperature aging test chamber for storage chips according to claim 1, characterized in that, The steps of obtaining an electrical stability index according to the test system sensor data include: Obtain the total number of transmitted bits and the number of error bits based on the sensor data of the test system, and obtain the bit error rate based on the number of error bits and the total number of transmitted bits; Obtain the real-time voltage and the nominal voltage based on the sensor data of the test system, and obtain the power supply voltage volatility based on the real-time voltage and the nominal voltage; Obtain the cumulative stress of the test chamber during the test cycle based on the power supply voltage volatility and the bit error rate; Obtain the sending instruction time and the receiving instruction time based on the sensor data of the test system, and obtain the response delay of the storage chip based on the sending instruction time and the receiving instruction time; Obtain the electrical stability index based on the response delay of the storage chip and the cumulative stress.

5. The method for self-diagnosis of faults of a high and low temperature aging test chamber for storage chips according to claim 1, wherein The step of obtaining the probability of multiple sensor data deviating from the normal distribution based on the sensor data of the device subsystem and obtaining the system entropy increase rate based on multiple probabilities of deviation from the normal distribution includes: Respectively obtain multiple sensor data sets based on the sensor data of the device subsystem, and obtain the corresponding data mean value based on each sensor data set; Obtain the corresponding data standard deviation based on each data mean value, and obtain the corresponding probability of deviation from the normal distribution based on each data standard deviation and the sensor data set; Obtain the entropy value of the corresponding sensor based on each probability of deviation from the normal distribution, and obtain the system entropy increase rate based on multiple entropy values.

6. The fault self-diagnosis method of the high and low temperature aging test chamber for storage chips according to claim 1, characterized in that, The step of determining the state of the test chamber according to the failure probability index includes: Judge whether the failure probability index is greater than a preset threshold; If the failure probability index is greater than the preset threshold, it is determined that the test chamber is in a failure state at this time, and the relationship between the failure probability index and the preset threshold interval is judged; If the failure probability index is greater than the upper limit value of the preset threshold interval, it is determined that the failure degree is serious; If the failure probability index is within the preset threshold interval, it is determined that the failure degree is general; If the failure probability index is less than the lower limit value of the preset threshold interval, it is determined that the failure degree is slight; If the failure probability index is not greater than the preset threshold, it is determined that the test chamber is in a normal state at this time.

7. A self-diagnosis system for high and low temperature aging test chamber of a storage chip, characterized in that, Including: A first acquisition module for acquiring the sensor data of the device subsystem of the high and low temperature aging test chamber, wherein the sensor data of the device subsystem includes the sensor data of the temperature control system, the sensor data of the air circulation system, the sensor data of the heating and cooling system, and the sensor data of the test system; A second acquisition module for obtaining the temperature control abnormality index based on the sensor data of the temperature control system and obtaining the air system efficiency coefficient based on the sensor data of the air circulation system; A third acquisition module for obtaining the heating efficiency and the refrigeration coefficient based on the sensor data of the heating and cooling system, and obtaining the thermal coupling factor based on the heating efficiency and the refrigeration coefficient; A fourth acquisition module for obtaining the electrical stability index based on the sensor data of the test system; A fifth acquisition module for obtaining the probability of multiple sensor data deviating from the normal distribution based on the sensor data of the device subsystem and obtaining the system entropy increase rate based on multiple probabilities of deviation from the normal distribution; A determination module, configured to obtain a fault probability index according to the system entropy increase rate, electrical stability index, thermal coupling factor, wind system efficiency coefficient, and temperature control anomaly index, and determine the state of the test chamber according to the fault probability index.

8. The fault self-diagnosis system of the high and low temperature aging test chamber for storage chips according to claim 7, wherein, The second acquisition module includes: A first acquisition unit, configured to obtain the cavity temperature, set temperature, and temperature range within a preset time period according to the temperature control system sensor data, and obtain the temperature change acceleration according to the temperature range and the preset time period; A second acquisition unit, configured to obtain the temperature deviation degree according to the cavity temperature and the set temperature; A third acquisition unit, configured to obtain the maximum temperature and the minimum temperature according to the temperature range, and obtain the temperature range difference according to the maximum temperature and the minimum temperature; A fourth acquisition unit, configured to obtain the temperature volatility according to the temperature range difference and the set temperature, and obtain the temperature control anomaly index according to the temperature volatility, temperature deviation degree, and temperature change acceleration; A fifth acquisition unit, configured to obtain the wind speed, wind pressure, fan current, and impeller rotation speed data according to the wind circulation system sensor data, and obtain the basic efficiency factor according to the wind speed, wind pressure, and fan current; A sixth acquisition unit, configured to obtain the attenuation factor, actual rotation speed, and rated rotation speed according to the impeller rotation speed data, and obtain the attenuation index according to the attenuation factor, actual rotation speed, and rated rotation speed; A seventh acquisition unit, configured to obtain the wind system efficiency coefficient according to the attenuation index and the basic efficiency factor.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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