Electronic component fault monitoring module and method

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to an electronic component fault monitoring module and method. According to the method, the initial difference degree of each electronic component in each full life cycle stage is determined, and the cycle anomaly relevance is obtained by utilizing the initial difference degree volatility of the initial difference degree in different full life cycle stages. And further obtaining the monitoring abnormity degree of each electronic component in each full life cycle stage. A stage sensitivity coefficient of each category in each full life cycle stage can be determined based on a distribution result in the cluster. And obtaining the monitoring consistency of each full life cycle stage according to the difference of the overall sensitivity coefficients in the adjacent full life cycle stages and the abnormality of the periodic abnormality relevance of the electronic component, and judging whether the stage needs to be adjusted or not, thereby obtaining the result of the faulted electronic component. According to the method, the fault detection accuracy of the electronic component is ensured through early warning in the full life cycle stage in the monitoring process.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical variable measurement, and in particular to an electronic component fault monitoring module and method. Background Art

[0002] Electronic components are the core components of all kinds of electronic equipment, and their operating status directly affects the performance and life of the equipment. When developing full-cycle intelligent testing and maintenance technology for electronic components, it is necessary to focus on status monitoring, functional testing, performance optimization, and fault repair throughout the entire life cycle of components from design, production, operation to maintenance.

[0003] The full life cycle monitoring of electronic components includes four stages: Design verification stage: During the component design stage, verify whether its electrical characteristics, thermal performance, reliability, etc. meet the design requirements. Production test stage: Functional testing, screening testing and consistency testing are performed on components during the production process to ensure the quality of products before they leave the factory. Operation monitoring stage: During the actual operation of components, the working status is monitored in real time, abnormalities are found and diagnosed. Maintenance and repair stage: Maintenance and optimization are carried out through historical data and predictive models, and fault repair or replacement is carried out when necessary.

[0004] In the process of monitoring electronic components throughout their life cycle, there is a certain correlation between the failure manifestations of electronic components. The failure condition is affected by the monitoring methods or environmental factors at different stages of the life cycle. If the monitoring results of various types of electronic components at the life cycle stage are obviously unified, it means that the monitoring process of the life cycle stage is unreasonable, and the monitoring results of different categories of electronic components cannot be distinguished, resulting in the inability to timely determine the faulty components at each stage of the life cycle. Summary of the invention

[0005] In order to solve the technical problem in the prior art that the monitoring results of different electronic components are not distinguishable due to unreasonable monitoring settings at a certain stage of the whole life cycle, and thus the faulty components cannot be determined in time, the purpose of the present invention is to provide an electronic component fault monitoring module and method, and the technical solutions adopted are as follows: The present invention provides a method for monitoring electronic component failures, the method comprising: Obtain monitoring data of electronic components at each full life cycle stage in a monitoring scenario; the electronic components include multiple categories; For each full life cycle stage, the monitoring data deviation between each electronic component and similar electronic components in the full life cycle stage is used as the initial difference of each electronic component; according to the volatility of the initial difference of the electronic components at different full life cycle stages, the periodic anomaly correlation is obtained; according to the periodic anomaly correlation, the initial difference is adjusted to obtain the monitoring anomaly of each electronic component at each full life cycle stage; The time series of the whole life cycle stage is used as the horizontal axis, and the monitoring abnormality is used as the vertical axis to obtain the sample space, and all electronic components are mapped into the sample space to obtain the clustering results of the data points. According to the distribution concentration of each category of electronic components in the cluster cluster at each whole life cycle stage, the stage sensitivity coefficient of each category at each whole life cycle stage is obtained; Based on the stage sensitivity coefficients, the overall sensitivity coefficients of the electronic components in each full life cycle stage are obtained; for each full life cycle stage, based on the differences in the overall sensitivity coefficients in adjacent full life cycle stages and the abnormalities of the periodic abnormality correlations of the electronic components, the monitoring consistency of each full life cycle stage is obtained; based on the monitoring consistency, whether the full life cycle stage needs to be adjusted is judged; based on the adjusted full life cycle stage, whether there are faulty electronic components is judged.

[0006] Furthermore, the method for obtaining the initial difference degree includes: Obtain the average monitoring data of each electronic component in a full life cycle stage; For any electronic component, obtain the mean difference of monitoring data between the electronic component and each other electronic component of the same type; and use the average value of the mean difference of the monitoring data as the initial difference degree of the electronic component in a full life cycle stage.

[0007] Furthermore, the method for obtaining the period anomaly correlation includes: For each electronic component, the initial difference sequences in all life cycle stages are arranged into an initial difference sequence; a differential sequence of the initial difference sequence is obtained; and the ratio of the element mean in the initial difference sequence to the element mean in the differential sequence is normalized to obtain the periodic anomaly correlation.

[0008] Furthermore, the method for acquiring the monitoring abnormality degree includes: The periodic anomaly correlation is multiplied by the initial difference to obtain the monitoring anomaly degree of each electronic component at each full life cycle stage.

[0009] Furthermore, a density clustering algorithm is used in the sample space to obtain each cluster in the clustering result.

[0010] Furthermore, the method for obtaining the stage sensitivity coefficient includes: Choose any category of electronic components as the target category and one life cycle stage as the target stage; In each cluster, the proportion of sample points of electronic components of the target category in the target stage is obtained; and the standard deviation of the proportion of sample points of the target category in all clusters in the target stage is calculated; In each cluster, the maximum difference distance between sample points of electronic components of the target category is obtained; the average maximum difference distance of the target category in all clusters is obtained; The ratio of the average maximum difference distance to the standard deviation is normalized to obtain the stage sensitivity coefficient of the target category at the target stage.

[0011] Furthermore, the method for obtaining the overall sensitivity coefficient includes: The average value of the stage sensitivity coefficients of all categories in each life cycle stage is taken as the overall sensitivity coefficient.

[0012] Furthermore, the method for acquiring the monitoring consistency includes: For any full life cycle stage, obtain the overall sensitivity coefficient difference between the full life cycle stage and the previous full life cycle stage; obtain the average periodic anomaly correlation of all electronic components in each category, and obtain the data deviation of all average anomaly correlations relative to the average value; normalize the ratio of the overall sensitivity coefficient difference to the data deviation to obtain the monitoring consistency.

[0013] Furthermore, judging whether the whole life cycle stage needs to be adjusted according to the monitoring consistency includes: If the monitoring consistency is greater than a preset consistency threshold, it is determined that the full life cycle stage needs to be adjusted.

[0014] The present invention also proposes an electronic component fault monitoring module, the module comprising: A monitoring data acquisition module, used to obtain monitoring data of electronic components in each full life cycle stage in the monitoring scenario; the electronic components include multiple categories; The monitoring anomaly acquisition module, for a full life cycle stage, takes the monitoring data deviation between each electronic component and similar electronic components in the full life cycle stage as the initial difference of each electronic component; obtains the periodic anomaly correlation according to the volatility of the initial difference of the electronic component at different full life cycle stages; adjusts the initial difference according to the periodic anomaly correlation to obtain the monitoring anomaly of each electronic component at each full life cycle stage; The stage sensitivity coefficient analysis module is used to obtain the sample space by taking the time sequence of the whole life cycle stage as the horizontal coordinate and the monitoring abnormality as the vertical coordinate, mapping all electronic components into the sample space and obtaining the clustering result of the data points, and obtaining the stage sensitivity coefficient of each category in each whole life cycle stage according to the distribution concentration of each category of electronic components in the clustering cluster at each whole life cycle stage; The whole life cycle stage judgment module is used to obtain the overall sensitivity coefficient of the electronic components in each whole life cycle stage according to the stage sensitivity coefficient; for each whole life cycle stage, according to the difference in the overall sensitivity coefficients in adjacent whole life cycle stages and the abnormality of the monitoring abnormality in the whole life cycle stage, obtain the monitoring consistency of each whole life cycle stage; according to the monitoring consistency, judge whether the whole life cycle stage needs to be adjusted.

[0015] The present invention has the following beneficial effects: The present invention first determines the initial difference of each electronic component at each full life cycle stage, and uses the volatility of the initial difference at different full life cycle stages to characterize the abnormal state characterization degree of the electronic components at different full life cycle stages. The greater the obtained periodic abnormal correlation, the more abnormal the abnormal stage is, and the greater the abnormal quantification result should be, so the monitoring abnormality of each electronic component at each full life cycle stage can be further obtained. All data can be classified based on the monitoring abnormality, and the stage sensitivity coefficient of each category at each full life cycle stage can be determined based on the distribution results in the clustering clusters. The stage sensitivity coefficient can reflect the monitoring distinction of each category of electronic components at the current full life cycle stage. If there are electronic components between categories with a unified periodic abnormal correlation, and adjacent full life cycle stages have a large overall sensitivity coefficient difference, it means that there are major problems in the full life cycle stage at this time, and targeted adjustments need to be made, so the adjustment signal can be fed back to achieve effective monitoring of electronic components by adjusting the parameters of the full life cycle stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for monitoring electronic component failures provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the electronic component fault monitoring module and method proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of an electronic component fault monitoring module and method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of an electronic component fault monitoring method provided by an embodiment of the present invention, the method comprising: Step S1: obtaining monitoring data of electronic components in each full life cycle stage in a monitoring scenario; the electronic components include multiple categories.

[0022] Common electronic components on circuit boards include chip capacitors, overload protectors, resistors, diodes, transistors, inductors, and integrated circuits, which together undertake key functions such as filtering, overload protection, signal conditioning, rectification, amplification, energy storage, and logic control. In actual operation, these components may degrade in performance or fail due to environmental changes, long-term use, or electrical stress. Chip capacitors are core components for filtering and energy storage. Common problems include capacity attenuation, short circuit or open circuit, and increased leakage current caused by high temperature. Overload protectors protect circuits by limiting current, and may experience faults such as premature triggering, untimely triggering, or thermal characteristic drift. Resistors may drift in resistance, overheat and burn out, or have poor welding due to aging, temperature changes, or overload during long-term use. Diodes are mainly used for unidirectional conduction and rectification. Common problems include increased leakage current, abnormal forward voltage drop, and thermal breakdown. Transistors are core components for signal amplification and switch control, and may experience switch failure, gain reduction, or overheating failure. Inductors are used for energy storage and filtering, and may experience performance degradation due to coil breakage, inductance drift, or core damage. Integrated circuits (ICs) are the core of complex logic and control functions, and common faults include internal circuit failure, overheating damage, and poor pin welding.

[0023] In order to ensure the safety of various electronic components on circuit boards, it is necessary to monitor the data of various electronic components at different stages of the full life cycle, evaluate the status of electronic components and conduct quality management by monitoring key parameters. The main monitoring contents may include: the capacitance and leakage current of chip capacitors, the current characteristics and temperature response of overload protectors, the resistance change and power load of resistors, the forward voltage drop and reverse leakage current of diodes, the current gain and switching performance of transistors, the inductance stability and core state of inductors, and the power consumption, logic functions and thermal management of integrated circuits. By combining intelligent diagnostic algorithms and fault prediction technologies, the operating status of electronic components can be deeply analyzed, their operating parameters can be optimized, potential problems can be warned in time, the risk of equipment failure can be reduced, and the overall reliability and safety of the system can be improved.

[0024] As described in the background technology, the full life cycle stage has multiple stages, and there is a certain correlation between the failure performance of electronic components in different full life cycle stages. If the fault correlation is strong, there will not be a large difference in monitoring sensitivity in adjacent full life cycle stages, and there will be consistent monitoring characteristics in both stages. However, if the monitoring data at this time shows that there is a difference in monitoring sensitivity in adjacent full life cycle stages, it means that the test settings of a certain full life cycle stage are unreasonable, and adjustments need to be made to determine the correct test settings to avoid missed detection of electronic components.

[0025] Therefore, the embodiment of the present invention first obtains the monitoring data of electronic components in each full life cycle stage in the monitoring scenario, and further analyzes different categories of electronic components and different full life cycle stages in subsequent steps.

[0026] It should be noted that the parameter types monitored by different electronic components in the embodiment of the present invention are different, so the value range and dimension of the obtained monitoring data are different. In order to facilitate subsequent data processing, the monitoring data needs to be further normalized after it is obtained to eliminate the influence of the dimension and value range. The monitoring system of the embodiment of the present invention includes: (1) Sensor module: including temperature sensors for real-time monitoring of component operating temperature; electrical sensors for monitoring electrical parameters such as voltage, current, and power; vibration and stress sensors for detecting the impact of mechanical stress or environmental vibration on components; and environmental sensors for detecting the potential impact of external environmental factors such as humidity and air pressure on components.

[0027] (2) Data acquisition and processing module: An integrated high-precision analog-to-digital converter is used to acquire and quantify sensor data. The data preprocessing unit performs filtering, denoising and normalization on the acquired data. An embedded AI chip or microprocessor is used to execute the electronic component fault monitoring method in the embodiment of the present invention.

[0028] (3) Communication module: supports multiple communication protocols (such as CAN, I2C, SPI, wireless communication, etc.), which is convenient for interaction with the host computer or cloud platform. It has real-time alarm function and can push alarm information through buzzer, indicator light or remotely.

[0029] (4) Power management module: adopts low power consumption design, supports external power supply or self-powered mode, and ensures long-term stable operation.

[0030] (5) Storage and interface module: Built-in non-volatile memory to record component operation history data and support offline analysis; provides standard interfaces (such as USB, UART) to facilitate debugging and expansion.

[0031] Step S2: For each full life cycle stage, the monitoring data deviation between each electronic component and similar electronic components in the full life cycle stage is used as the initial difference of each electronic component; according to the volatility of the initial difference of the electronic components in different full life cycle stages, the periodic anomaly correlation is obtained; according to the periodic anomaly correlation, the initial difference is adjusted to obtain the monitoring anomaly of each electronic component in each full life cycle stage.

[0032] For the same full life cycle stage, different types of electronic components will be tested and monitored to a certain extent. The content of monitoring required at different life cycle stages is different, and the monitoring equipment and monitoring methods used are different. For each electronic component, the smaller the deviation from similar electronic components in a full life cycle stage, the less likely the electronic component is to fail. Therefore, firstly, in the full life cycle stage, the deviation of the detection data between each electronic component and similar electronic components is obtained to obtain the initial difference of the electronic component.

[0033] Preferably, in one embodiment of the present invention, the method for obtaining the initial difference degree includes: Obtain the average monitoring data of each electronic component in a full life cycle stage; For any electronic component, obtain the mean difference of monitoring data between the electronic component and every other electronic component of the same type; and take the average value of the mean difference of monitoring data as the initial difference degree of the electronic component in a whole life cycle stage.

[0034] When electronic components have quality problems, abnormal monitoring correlation will occur in the whole life cycle stage, that is, certain abnormalities will also occur in other whole life cycle stages. Therefore, the periodic abnormal correlation can be obtained according to the volatility of the initial difference of electronic components in different whole life cycle stages. That is, the smaller the volatility, the greater the periodic abnormal correlation.

[0035] Preferably, in one embodiment of the present invention, the method for obtaining period anomaly correlation includes: For each electronic component, the initial difference sequences in all life cycle stages are arranged into an initial difference sequence; the difference sequence of the initial difference sequence is obtained; the ratio of the element mean in the initial difference sequence to the element mean in the difference sequence is normalized to obtain the periodic anomaly correlation.

[0036] The element mean in the initial difference sequence represents the fault performance of an electronic component in all life cycle stages, that is, the larger the element mean, the more uniform and larger abnormal performance the electronic component has in multiple life cycle stages; the differential sequence represents the rate of change of the initial difference between consecutive life cycle stages. The smaller the differential sequence, the smaller the fluctuation of the initial difference of the electronic component in different life cycle stages, and the smaller the difference sequence, the smaller the fluctuation of the initial difference of the electronic component in different life cycle stages, and the smaller the difference sequence, the smaller the fluctuation of the initial difference of the electronic component in different life cycle stages, and the smaller the difference sequence, the smaller the fluctuation of the initial difference of the electronic component in different life cycle stages, and the smaller the difference sequence, the smaller the correlation of the periodic anomaly, which means that the electronic component has a more consistent initial difference in different life cycle stages.

[0037] It should be noted that the normalization method in the embodiment of the present invention adopts range normalization, and other normalization methods may also be selected in other embodiments of the present invention, which will not be described in detail here.

[0038] The periodic anomaly correlation characterizes the degree of anomaly correlation of an electronic component in all life cycle stages. It shows the characteristics of the electronic component in the entire monitoring process. Therefore, it can be used to adjust the initial difference and obtain the monitoring anomaly degree of each electronic component in each life cycle stage.

[0039] In the embodiment of the present invention, the periodic anomaly correlation is multiplied by the initial difference to obtain the monitoring anomaly degree of each electronic component in each full life cycle stage.

[0040] Step S3: Use the time series of the whole life cycle stage as the horizontal axis and the monitoring abnormality as the vertical axis to obtain the sample space, map all electronic components into the sample space and obtain the clustering results of the data points, and obtain the stage sensitivity coefficient of each category in each life cycle stage according to the distribution concentration of each category of electronic components in the clustering cluster at each life cycle stage.

[0041] The difference in performance of different electronic components at different stages of the cycle is more affected by the setting of the whole life cycle stage. Ideally, different types of electronic components will extract different abnormal conditions at different stages due to their own uses and technical difficulties. For example, ordinary simple components: such as resistors, capacitors, diodes, transistors, etc. have simple structures and are not prone to failures in the design and testing stages. Their fault monitoring is more concentrated in the actual use feedback stage. Therefore, the corresponding design of the monitoring stage should be able to monitor the faults of different electronic components as much as possible within this stage, and be distinguishable.

[0042] Therefore, for any full life cycle stage, the monitoring abnormality of electronic components between all categories in this stage should have obvious distinction, and the greater the distinction, the stronger the stage sensitivity coefficient under this stage. Therefore, the embodiment of the present invention uniformly analyzes the data of all electronic components under all full life cycle stages, takes the time series of the full life cycle stage as the horizontal coordinate, and the monitoring abnormality as the vertical coordinate, obtains the sample space, maps all electronic components into the sample space, and obtains the clustering result of the data points. That is, there are sample points formed by the data of each electronic component in the sample space. For the clustering result, the more uniform the distribution of sample points of electronic components of a category under a certain life cycle stage in the cluster cluster, and the more dispersed the distribution of sample points of the category, the stronger the stage sensitivity under the life cycle stage, and a large number of electronic components of the same type in the same cluster cluster can be effectively distinguished. Therefore, the embodiment of the present invention can obtain the stage sensitivity coefficient of each category under each full life cycle stage according to the distribution concentration of each category of electronic components in the cluster cluster under each full life cycle stage.

[0043] In the embodiment of the present invention, a density clustering algorithm is used to obtain each clustering cluster in the clustering result.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining the stage sensitivity coefficient includes: Choose any category of electronic components as the target category and one life cycle stage as the target stage; In each cluster, the sample point ratio of the target category of electronic components in the target stage is obtained; the standard deviation of the sample point ratio of the target category in all clusters in the target stage is calculated. The larger the standard deviation, the greater the volatility of the sample point ratio distribution, which means that the discrimination of the target category in the target stage is lower.

[0045] In each cluster, the maximum difference distance between the sample points of the target category of electronic components is obtained; the average maximum difference distance of the target category in all clusters is obtained. Because the larger the average maximum difference distance of the maximum difference distance, the greater the difference distance, indicating that the electronic components of this category show obvious distinguishing characteristics within multiple clusters.

[0046] Therefore, the larger the average maximum difference distance and the smaller the standard deviation, the greater the discrimination of the target category at the target stage. Therefore, the ratio of the average maximum difference distance to the standard deviation is normalized to obtain the stage sensitivity coefficient of the target category at the target stage. In the embodiment of the present invention, the normalization method of the ratio is the hyperbolic tangent function mapping method.

[0047] Step S4: Based on the stage sensitivity coefficient, obtain the overall sensitivity coefficient of the electronic components in each full life cycle stage; for each full life cycle stage, according to the difference in the overall sensitivity coefficients in adjacent full life cycle stages and the abnormality of the periodic abnormal correlation of the electronic components, obtain the monitoring consistency of each full life cycle stage; determine whether the full life cycle stage needs to be adjusted based on the monitoring consistency; determine whether there are faulty electronic components based on the adjusted full life cycle stage.

[0048] For a whole life cycle stage, if the stage sensitivity coefficient of each category in the whole life cycle stage is large, it means that the overall sensitivity coefficient in the whole life cycle stage is large.

[0049] When the fault characteristics of multiple categories in the whole life cycle stage are relatively similar, it means that from the perspective of monitoring stage setting, this stage does not have the significance of improving monitoring accuracy, and the detection consistency is relatively high. For a certain category of electronic components, because the fault performance has a tendency to be stage-related, that is, there will be obvious abnormal state correlation between adjacent stages. If there is a significant difference in the overall sensitivity coefficient between adjacent stages at this time, it means that the monitoring setting in this stage is unreasonable, with a strong consistency of monitoring characteristics, and it needs to be adjusted in this stage. Therefore, for each whole life cycle stage, the embodiment of the present invention obtains the monitoring consistency of each whole life cycle stage based on the difference in the overall sensitivity coefficients in adjacent whole life cycle stages and the abnormality of the periodic abnormal correlation of electronic components.

[0050] According to the monitoring consistency, it is judged whether the whole life cycle stage needs to be adjusted. According to the adjusted whole life cycle stage, it is judged whether there are faulty electronic components. By adjusting the monitoring settings in the stage, the accuracy of fault monitoring can be significantly improved, the reliability of components can be improved, and intelligent monitoring and maintenance can be realized. On the basis of eliminating the influence of quality problems, by distinguishing between conventional abnormal performance and potential faults, combining big data and artificial intelligence technology, and dynamically adjusting the monitoring frequency, it can not only accurately capture potential risks, but also effectively predict fault trends and achieve early warning, thereby avoiding sudden failures. In the design verification stage, potential design defects are discovered by optimizing the test plan, and the weak links of components are identified by using the penetration of the whole life cycle data, providing data support for reliability improvement. At the same time, the Internet of Things and digital twin technology are introduced to monitor the operating status of components in real time, and the predictive maintenance model is combined to quickly respond to abnormalities, and components that may fail are repaired or replaced in advance, thereby reducing the probability of sudden failures and comprehensively improving the performance and operation stability of components. It should be noted that the specific method for determining faulty electronic components is a technical means well known to those skilled in the art, and will not be repeated here.

[0051] Preferably, in the embodiment of the present invention, the method for acquiring monitoring consistency includes: For any full life cycle stage, obtain the overall sensitivity coefficient difference between the full life cycle stage and the previous full life cycle stage. Obtain the average period anomaly correlation of all electronic components in each category, and obtain the data deviation of all average anomaly correlations relative to the average value. The smaller the data deviation, the more uniform the period anomaly correlations between all categories and the greater the monitoring consistency. If the period anomaly correlations are relatively uniform and have a larger overall sensitivity coefficient difference, it means that the monitoring consistency of this life cycle stage is greater. Therefore, the ratio of the overall sensitivity coefficient difference to the data deviation is normalized to obtain monitoring consistency.

[0052] In one embodiment of the present invention, the method for obtaining data deviation is: obtaining the average value of all average abnormal correlations, taking the absolute value of the difference between the average abnormal correlation of each category and the average value as the initial data deviation, and taking the average value of the initial data deviations of all categories as the data deviation.

[0053] In an embodiment of the present invention, if the monitoring consistency is greater than a preset consistency threshold, it is determined that the full life cycle stage needs to be adjusted. Because the monitoring consistency is a normalized value, the consistency threshold can be set to 0.8. When the monitoring consistency of a stage is greater than the consistency threshold, it means that different types of electronic components cannot be effectively monitored and distinguished at this stage, and a warning signal needs to be fed back to remind the staff to adjust the settings at this stage.

[0054] In summary, the embodiment of the present invention determines the initial difference of each electronic component at each full life cycle stage, and uses the volatility of the initial difference at different full life cycle stages to obtain the periodic anomaly correlation. The monitoring anomaly of each electronic component at each full life cycle stage is further obtained. Based on the distribution results in the clustering clusters, the stage sensitivity coefficient of each category at each full life cycle stage can be determined. According to the difference in the overall sensitivity coefficients at adjacent full life cycle stages, and the abnormality of the periodic anomaly correlation of electronic components, the monitoring consistency of each full life cycle stage is obtained and it is determined whether the stage needs to be adjusted, thereby obtaining the results of faulty electronic components.

[0055] Based on the same inventive concept, the present invention also proposes an electronic component fault monitoring module, the module comprising: A monitoring data acquisition module, used to obtain monitoring data of electronic components in each full life cycle stage in the monitoring scenario; the electronic components include multiple categories; The monitoring anomaly acquisition module, for a full life cycle stage, takes the monitoring data deviation between each electronic component and similar electronic components in the full life cycle stage as the initial difference of each electronic component; obtains the periodic anomaly correlation according to the volatility of the initial difference of the electronic component at different full life cycle stages; adjusts the initial difference according to the periodic anomaly correlation to obtain the monitoring anomaly of each electronic component at each full life cycle stage; The stage sensitivity coefficient analysis module is used to obtain the sample space by taking the time sequence of the whole life cycle stage as the horizontal coordinate and the monitoring abnormality as the vertical coordinate, mapping all electronic components into the sample space and obtaining the clustering result of the data points, and obtaining the stage sensitivity coefficient of each category in each whole life cycle stage according to the distribution concentration of each category of electronic components in the clustering cluster at each whole life cycle stage; The whole life cycle stage judgment module is used to obtain the overall sensitivity coefficient of the electronic components in each whole life cycle stage according to the stage sensitivity coefficient; for each whole life cycle stage, according to the difference in the overall sensitivity coefficients in adjacent whole life cycle stages and the abnormality of the monitoring abnormality in the whole life cycle stage, obtain the monitoring consistency of each whole life cycle stage; according to the monitoring consistency, judge whether the whole life cycle stage needs to be adjusted.

[0056] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring electronic component failure, characterized in that: The method comprises: Obtain monitoring data of electronic components at each full life cycle stage in a monitoring scenario; the electronic components include multiple categories; For each full life cycle stage, the monitoring data deviation between each electronic component and similar electronic components in the full life cycle stage is used as the initial difference of each electronic component; according to the volatility of the initial difference of the electronic components at different full life cycle stages, the periodic anomaly correlation is obtained; according to the periodic anomaly correlation, the initial difference is adjusted to obtain the monitoring anomaly of each electronic component at each full life cycle stage; The time series of the whole life cycle stage is used as the horizontal axis, and the monitoring abnormality is used as the vertical axis to obtain the sample space, and all electronic components are mapped into the sample space to obtain the clustering results of the data points. According to the distribution concentration of each category of electronic components in the cluster cluster at each whole life cycle stage, the stage sensitivity coefficient of each category at each whole life cycle stage is obtained; Based on the stage sensitivity coefficients, the overall sensitivity coefficients of the electronic components in each full life cycle stage are obtained; for each full life cycle stage, based on the differences in the overall sensitivity coefficients in adjacent full life cycle stages and the abnormalities of the periodic abnormality correlations of the electronic components, the monitoring consistency of each full life cycle stage is obtained; based on the monitoring consistency, whether the full life cycle stage needs to be adjusted is judged; based on the adjusted full life cycle stage, whether there are faulty electronic components is judged.

2. The electronic component fault monitoring method according to claim 1, characterized in that: The method for obtaining the initial difference degree includes: Obtain the average monitoring data of each electronic component in a full life cycle stage; For any electronic component, obtain the mean difference of monitoring data between the electronic component and each other electronic component of the same type; and use the average value of the mean difference of the monitoring data as the initial difference degree of the electronic component in a full life cycle stage.

3. The electronic component fault monitoring method according to claim 1, characterized in that: The method for obtaining the period anomaly correlation includes: For each electronic component, the initial difference sequences in all life cycle stages are arranged into an initial difference sequence; a differential sequence of the initial difference sequence is obtained; and the ratio of the element mean in the initial difference sequence to the element mean in the differential sequence is normalized to obtain the periodic anomaly correlation.

4. The electronic component fault monitoring method according to claim 1, characterized in that: The method for obtaining the monitoring abnormality degree includes: The periodic anomaly correlation is multiplied by the initial difference to obtain the monitoring anomaly degree of each electronic component at each full life cycle stage.

5. The electronic component fault monitoring method according to claim 1, characterized in that: A density clustering algorithm is used in the sample space to obtain each clustering cluster in the clustering result.

6. The electronic component fault monitoring method according to claim 1, characterized in that: The method for obtaining the stage sensitivity coefficient includes: Choose any category of electronic components as the target category and one life cycle stage as the target stage; In each cluster, the proportion of sample points of electronic components of the target category in the target stage is obtained; and the standard deviation of the proportion of sample points of the target category in all clusters in the target stage is calculated; In each cluster, the maximum difference distance between sample points of electronic components of the target category is obtained; the average maximum difference distance of the target category in all clusters is obtained; The ratio of the average maximum difference distance to the standard deviation is normalized to obtain the stage sensitivity coefficient of the target category at the target stage.

7. The electronic component fault monitoring method according to claim 1, characterized in that: The method for obtaining the overall sensitivity coefficient includes: The average value of the stage sensitivity coefficients of all categories in each life cycle stage is taken as the overall sensitivity coefficient.

8. The electronic component fault monitoring method according to claim 1, characterized in that: The method for acquiring the monitoring consistency includes: For any full life cycle stage, obtain the overall sensitivity coefficient difference between the full life cycle stage and the previous full life cycle stage; obtain the average periodic anomaly correlation of all electronic components in each category, and obtain the data deviation of all average anomaly correlations relative to the average value; normalize the ratio of the overall sensitivity coefficient difference to the data deviation to obtain the monitoring consistency.

9. The electronic component fault monitoring method according to claim 1, characterized in that: The determining whether the whole life cycle stage needs to be adjusted according to the monitoring consistency includes: If the monitoring consistency is greater than a preset consistency threshold, it is determined that the full life cycle stage needs to be adjusted.

10. An electronic component fault monitoring module, characterized in that: The module includes: A monitoring data acquisition module, used to obtain monitoring data of electronic components in each full life cycle stage in the monitoring scenario; the electronic components include multiple categories; The monitoring anomaly acquisition module, for a full life cycle stage, takes the monitoring data deviation between each electronic component and similar electronic components in the full life cycle stage as the initial difference of each electronic component; obtains the periodic anomaly correlation according to the volatility of the initial difference of the electronic component at different full life cycle stages; adjusts the initial difference according to the periodic anomaly correlation to obtain the monitoring anomaly of each electronic component at each full life cycle stage; The stage sensitivity coefficient analysis module is used to obtain the sample space by taking the time sequence of the whole life cycle stage as the horizontal coordinate and the monitoring abnormality as the vertical coordinate, mapping all electronic components into the sample space and obtaining the clustering result of the data points, and obtaining the stage sensitivity coefficient of each category in each whole life cycle stage according to the distribution concentration of each category of electronic components in the clustering cluster at each whole life cycle stage; The whole life cycle stage judgment module is used to obtain the overall sensitivity coefficient of the electronic components in each whole life cycle stage according to the stage sensitivity coefficient; for each whole life cycle stage, according to the difference in the overall sensitivity coefficients in adjacent whole life cycle stages and the abnormality of the monitoring abnormality in the whole life cycle stage, obtain the monitoring consistency of each whole life cycle stage; according to the monitoring consistency, judge whether the whole life cycle stage needs to be adjusted.

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