CN117113102A - Electronic component life prediction method - Google Patents
Electronic component life prediction method Download PDFInfo
- Publication number
- CN117113102A CN117113102A CN202311133001.9A CN202311133001A CN117113102A CN 117113102 A CN117113102 A CN 117113102A CN 202311133001 A CN202311133001 A CN 202311133001A CN 117113102 A CN117113102 A CN 117113102A
- Authority
- CN
- China
- Prior art keywords
- standard
- electronic component
- electronic components
- abnormal
- appearance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000002159 abnormal effect Effects 0.000 claims abstract description 79
- 230000001133 acceleration Effects 0.000 claims abstract description 61
- 238000002474 experimental method Methods 0.000 claims abstract description 36
- 238000005070 sampling Methods 0.000 claims description 5
- 239000002131 composite material Substances 0.000 claims description 3
- 238000012795 verification Methods 0.000 abstract description 5
- 230000003044 adaptive effect Effects 0.000 abstract description 2
- 230000006872 improvement Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000012360 testing method Methods 0.000 description 4
- 238000005452 bending Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000006355 external stress Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 239000003985 ceramic capacitor Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to the technical field of electronic component life prediction, in particular to a method for predicting the life of an electronic component. The method comprises the steps of formulating acceleration experiment state data, calling and using various standard state data in a scene state database, and verifying the relation between the acceleration experiment state data and the standard state data. According to the method, the relation between the acceleration experiment state data and the standard state data is verified, only the ratio between the acceleration experiment state data and the standard state data is needed to be measured, the verification time is shortened through the acceleration experiment, the service lives of the standard electronic components and the abnormal electronic components in the standard state are predicted by combining the relation between the acceleration experiment state data and the standard state data, the service lives of the standard electronic components and the abnormal electronic components are divided according to the service lives, the service lives of the corresponding service scenes are predicted by combining the appearance of the electronic components, and the adaptive service scenes are planned for the standard electronic components and the abnormal electronic components.
Description
Technical Field
The invention relates to the technical field of electronic component life prediction, in particular to a method for predicting the life of an electronic component.
Background
The electronic component is a component part of an electronic element and a small machine or instrument, is often composed of a plurality of parts, and can be commonly used in similar products; some parts of the industry such as electric appliances, radios, meters and the like are commonly referred to as electronic devices such as capacitors, transistors, hairsprings, springs and the like. Diodes and the like are common.
When the electronic component is used for being assembled into various electronic devices and actually applied to the market, various external stress reactions, such as physical strain caused by falling of the electronic device, thermal strain caused by cold and hot temperature differences, electrical strain when being electrified and the like, are required to be faced, and the external strains are used as inducements.
In order to cope with the above problems, a method for predicting the lifetime of electronic components is needed.
Disclosure of Invention
The invention aims to provide a life prediction method for electronic components, which aims to solve the problems in the background technology.
In order to achieve the above object, a method for predicting lifetime of electronic components is provided, comprising the steps of:
s1, identifying appearance information of a current electronic component to be detected, collecting appearance data of standard electronic components in industry, and dividing the electronic component to be detected into standard electronic components and abnormal electronic components;
s2, collecting all standard state data of the current electronic components to be detected under different actual use scenes, and integrating the standard state data into a use scene state database;
s3, planning a sample selection mode, and sampling unit samples of the standard electronic components and the abnormal electronic components;
s4, formulating acceleration experiment state data, calling and using all standard state data in a scene state database, and verifying the relation between the acceleration experiment state data and the standard state data;
s5, determining service lives of the standard electronic components and the abnormal electronic components in the acceleration experiment, predicting the service lives of the standard electronic components and the abnormal electronic components in the actual use scene, and generating prediction data;
s6, combining the prediction data to deduce the service life ratio of the standard electronic component and the abnormal electronic component.
As a further improvement of the present technical solution, the method for identifying appearance information of the current electronic component to be inspected in S1 includes the following steps:
s1.1, making an appearance identification key point of an electronic component;
s1.2, determining appearance identification key points of each electronic component in a standard state, and establishing a standard appearance identification key point database;
s1.3, comparing appearance information of the electronic components to be detected with a standard appearance recognition key point database, and determining abnormal points;
s1.4, an abnormal point threshold is established, and electronic components with abnormal points exceeding the abnormal point threshold are marked as abnormal electronic components, and electronic components with abnormal points exceeding the abnormal point threshold are marked as standard electronic components.
As a further improvement of the technical scheme, the appearance information for identifying the current electronic component to be detected in the S1 adopts a feature comparison algorithm, and the algorithm formula is as follows:
W μ ＝[a 1 ，a 2 ，…，a n ]；
M μ ＝[b 1 ，b 2 ，…，b m ]；
wherein W is μ Identifying a set of key points, a, for each appearance of each electronic component in a standard state 1 To a n
Identifying key points, M, for each appearance of each electronic component μ
Identifying a key point set for each appearance of the current electronic component to be inspected, b 1 To b m
Identifying key points for each appearance of the current electronic component to be inspected, and comparing F (N) characteristic points with a function, N
Key points are identified for the composite standard appearance,
when the threshold value of the key point is identified for the coincident standard appearance, the key point N is identified for the coincident standard appearance
Below the coincidence standard appearance identification key point threshold
When the electronic component to be inspected is an abnormal electronic component, the key point N is identified when the standard appearance is overlapped
Not lower than the threshold value of the coincidence standard appearance identification key pointAnd when the current electronic component to be detected is a standard electronic component.
As a further improvement of the technical scheme, the standard state data in S2 includes thermal strain caused by the temperature difference between cold and hot and electrical strain when energized.
As a further improvement of the present technical solution, the method for planning the sample selection manner in S3 includes the following steps:
s3.1, counting the total number of the current electronic components to be detected;
s3.2, determining the proportion between the number of standard electronic components and the number of abnormal electronic components of the current electronic components to be detected by combining a feature comparison algorithm;
s3.3, selecting the sample amounts of the standard electronic components and the abnormal electronic components according to the proportion.
As a further improvement of the present technical solution, the method for formulating acceleration experiment status data in S4 includes the following steps:
s4.1, determining the temperature and the voltage in an acceleration state;
s4.2, verifying the endurance time of the standard electronic component and the abnormal electronic component in the current acceleration state;
s4.3, determining the superposition relation of the acceleration state and the standard state, and deducing the durability of the endurance time in the standard state in the current acceleration state.
As a further improvement of the technical scheme, the superposition relation between the acceleration state and the standard state determined in S4.3 adopts a deduction empirical formula, and the formula algorithm is as follows:
wherein L is N For the service life of standard, L A To accelerate the service life in the experimental process, V A
Voltage at standard time, V N To accelerate the voltage during the experiment, z is the voltage acceleration constant, T A
To accelerate the temperature during the experiment, T N And θ is a temperature acceleration constant for the standard state.
As a further improvement of the present technical solution, the method for generating the prediction data in S5 includes the following steps:
s5.1, deducing the service lives of the standard electronic components and the abnormal electronic components of each sample by combining the deduced empirical formula in the S4.3;
s5.2, integrating the service lives of the standard electronic components and the abnormal electronic components of each sample, and taking the average value as prediction data.
Compared with the prior art, the invention has the beneficial effects that:
in the electronic component life prediction method, the relation between the acceleration experiment state data and the standard state data is verified, at the moment, only the ratio between the acceleration experiment state data and the standard state data is needed to be measured, the verification time is shortened through an acceleration experiment, the service lives of the standard electronic component and the abnormal electronic component in the standard state are predicted by combining the relation between the acceleration experiment state data and the standard state data, the service scenes of the standard electronic component and the abnormal electronic component are divided according to the service lives, the service lives corresponding to the service scenes are predicted by combining the appearance of the electronic component, and the adaptive service scenes are planned for the standard electronic component and the abnormal electronic component.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a flow chart of a method for detecting appearance information of other electronic components to be detected according to the present invention;
FIG. 3 is a flow chart of a method for planning sample selection in accordance with the present invention;
FIG. 4 is a flow chart of a method for formulating acceleration test status data in accordance with the present invention;
fig. 5 is a flowchart of a method for generating prediction data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-5, a method for predicting lifetime of an electronic component is provided, which includes the following steps:
s1, identifying appearance information of a current electronic component to be detected, collecting appearance data of standard electronic components in industry, and dividing the electronic component to be detected into standard electronic components and abnormal electronic components;
s2, collecting all standard state data of the current electronic components to be detected under different actual use scenes, and integrating the standard state data into a use scene state database;
s3, planning a sample selection mode, and sampling unit samples of the standard electronic components and the abnormal electronic components;
s4, formulating acceleration experiment state data, calling and using all standard state data in a scene state database, and verifying the relation between the acceleration experiment state data and the standard state data;
s5, determining service lives of the standard electronic components and the abnormal electronic components in the acceleration experiment, predicting the service lives of the standard electronic components and the abnormal electronic components in the actual use scene, and generating prediction data;
s6, combining the prediction data to deduce the service life ratio of the standard electronic component and the abnormal electronic component.
When the electronic component is used for being assembled into various electronic devices and actually applied to the market, various external stress reactions, such as physical strain caused by falling of the electronic device, thermal strain caused by cold and hot temperature difference, electrical strain when being electrified and the like, are required to be faced, the external strains are used as inducements, when the electronic component is used for a certain period of time, the electronic component can be out of order, and meanwhile, after the electronic component is produced, the electronic component can be normally used even though the appearance of the electronic component is different, and the service life of the electronic component is different due to the change of the contact state;
in order to predict the service lives of different appearance electronic components in different use scenes, identifying appearance information of the current electronic components to be detected, collecting appearance data of standard electronic components in the industry, dividing the electronic components to be detected into standard electronic components and abnormal electronic components, for example, bending pins, keeping the vertical state of each pin of the electronic components in the standard state, marking the electronic components with the bent pins as the abnormal electronic components, collecting all standard state data of the current electronic components to be detected under different actual use scenes, integrating the data into a use scene state database, for example, voltage during power-on and temperature of an installation area of the electric components, and in order to ensure standardization of sampling, planning a sample selection mode, and sampling unit samples of the standard electronic components and the abnormal electronic components;
because the service life of a general electronic component is generally thousands to tens of thousands of hours, if a direct detection mode is adopted, the verification time is overlong, acceleration experiment state data is required to be formulated, all standard state data in a scene state database are called for use, the relation between the acceleration experiment state data and the standard state data is verified, at the moment, only the ratio between the acceleration experiment state data and the standard state data is required to be measured, the verification time is shortened through the acceleration experiment, the service life of the standard electronic component and the service life of the abnormal electronic component in the standard state are predicted by combining the relation between the acceleration experiment data and the standard state data, the service life ratio of the standard electronic component and the abnormal electronic component is deduced by combining the prediction data, the service scene of the standard electronic component and the abnormal electronic component is divided according to the service life, for example, the standard electronic component is applied to an electric appliance which needs to be used for a long time, such as a television, a computer, a mobile phone and the like, the abnormal electronic component is applied to a short-term electric appliance, and the service life of a toy car is predicted by combining the appearance of the electronic component, and the service life of the corresponding service scene is planned and adapted to the electric appliance.
In addition, the method for identifying the appearance information of the current electronic component to be inspected in S1 comprises the following steps:
s1.1, making an appearance identification key point of an electronic component;
s1.2, determining appearance identification key points of each electronic component in a standard state, and establishing a standard appearance identification key point database;
s1.3, comparing appearance information of the electronic components to be detected with a standard appearance recognition key point database, and determining abnormal points;
s1.4, an abnormal point threshold is established, and electronic components with abnormal points exceeding the abnormal point threshold are marked as abnormal electronic components, and electronic components with abnormal points exceeding the abnormal point threshold are marked as standard electronic components.
In the process of identifying the appearance of the electronic component to be detected, firstly, setting an appearance identification key point of the electronic component to be detected, namely, the position of the electronic component to be detected, such as pin bending degree and connector shape, which can influence the use state of the electronic component in a use scene, is required to be established, the use state of the electronic component can be influenced to be used as the appearance identification key point, then, the appearance data of the internal standard electronic component is collected, the appearance identification key point of each electronic component in the standard state is determined, a standard appearance identification key point database is established, the appearance information of the electronic component to be detected is compared with the standard appearance identification key point database, the abnormal point is determined, namely, the position of the appearance identification key point, at last, an abnormal point threshold value is established, the electronic component with the abnormal point exceeding the abnormal point threshold value is marked as the abnormal electronic component, and the electronic component with the non-exceeding mark is marked as the standard electronic component.
Further, in the step S1, the appearance information for identifying the current electronic component to be detected adopts a feature comparison algorithm, and the algorithm formula is as follows:
W μ ＝[a 1 ，a 2 ，…，a n ]；
M μ ＝[b 1 ，b 2 ，…，b m ]；
wherein W is μ Identifying a set of key points, a, for each appearance of each electronic component in a standard state 1 To a n
Identifying key points, M, for each appearance of each electronic component μ
Identifying a key point set for each appearance of the current electronic component to be inspected, b 1 To b m
Identifying key points for each appearance of the current electronic component to be inspected, and comparing F (N) characteristic points with a function, N
Key points are identified for the composite standard appearance,
when the threshold value of the key point is identified for the coincident standard appearance, the key point N is identified for the coincident standard appearance
Below the coincidence standard appearance identification key point threshold
When the electronic component to be inspected is an abnormal electronic component, the key point N is identified when the standard appearance is overlapped
Not lower than the threshold value of the coincidence standard appearance identification key pointAnd when the current electronic component to be detected is a standard electronic component.
Still further, each item of standard state data in S2 includes thermal strain caused by a difference in temperature between cold and hot, and electrical strain when energized. Namely the temperature in the use scene of the electronic component and the voltage in the operation process, and the service life of the electronic component can be influenced by the two-state data.
Specifically, the method for planning the sample selection mode in S3 includes the following steps:
s3.1, counting the total number of the current electronic components to be detected;
s3.2, determining the proportion between the number of standard electronic components and the number of abnormal electronic components of the current electronic components to be detected by combining a feature comparison algorithm;
s3.3, selecting the sample amounts of the standard electronic components and the abnormal electronic components according to the proportion.
In the process of planning a sample selection mode, the number of standard electronic components is different from the number of abnormal electronic components, the selection is carried out according to the current number when a sample is selected, so that the total number of the current electronic components to be detected is firstly counted, then the standard electronic components and the abnormal electronic components are identified through a feature comparison algorithm, the number of different types of electronic components is counted, the ratio between the number of the standard electronic components and the number of the abnormal electronic components of the current electronic components to be detected is determined, and the sample quantity of the standard electronic components and the sample quantity of the abnormal electronic components are selected according to the ratio when the sample quantity is selected in the later period.
In addition, the method for preparing the acceleration experiment state data in the S4 comprises the following steps:
s4.1, determining the temperature and the voltage in an acceleration state;
s4.2, verifying the endurance time of the standard electronic component and the abnormal electronic component in the current acceleration state;
s4.3, determining the superposition relation of the acceleration state and the standard state, and deducing the durability of the endurance time in the standard state in the current acceleration state.
In the process of formulating acceleration experiment state data, firstly, determining the temperature and voltage in an acceleration state as acceleration data, then verifying the endurance time of a standard electronic component and an abnormal electronic component in the current acceleration state, finally determining the superposition relation of the acceleration state and the standard state, deducing the endurance time in the standard state in the current acceleration state, for example, in the acceleration test of a multilayer ceramic capacitor and the preset use environment of an actual product, predicting the 1000h endurance test carried out in the application environment of 85 ℃ and 20V, which is equivalent to 362039h in the application environment of 65 ℃ and 5V, and deducing the service life of the electronic component in the standard state in the short-term verification time by using the acceleration test.
Further, in S4.3, the superposition relation between the acceleration state and the standard state is determined by adopting a derived empirical formula, and the formula algorithm is as follows:
wherein L is N For the service life of standard, L A To accelerate the service life in the experimental process, V A
Voltage at standard time, V N To accelerate the voltage during the experiment, z is the voltage acceleration constant, T A To accelerate the temperature during the experiment, T N Is the temperature in the standard state, theta isThe temperature acceleration constant, the voltage acceleration constant z and the temperature acceleration constant θ are related to the material of the current electronic component.
Still further, the generating method of the prediction data in S5 includes the steps of:
s5.1, deducing the service lives of the standard electronic components and the abnormal electronic components of each sample by combining the deduced empirical formula in S4.3;
s5.2, integrating the service lives of the standard electronic components and the abnormal electronic components of each sample, taking an average value as prediction data, and because of the difference of experimental results of different electronic components in the acceleration experiment process, in order to improve the prediction accuracy, firstly deriving an empirical formula in S4.3 to derive the service lives of the standard electronic components and the abnormal electronic components of each sample, then integrating the service lives of the standard electronic components and the abnormal electronic components of each sample, taking the average value as the prediction data, for example, selecting four standard electronic components for acceleration experiment, wherein the predicted actual life is L 1 ，L 2 ，L 3 L and 4 the final predicted value at this time is
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The electronic component life prediction method is characterized by comprising the following steps of:
s1, identifying appearance information of a current electronic component to be detected, collecting appearance data of standard electronic components in industry, and dividing the electronic component to be detected into standard electronic components and abnormal electronic components;
s2, collecting all standard state data of the current electronic components to be detected under different actual use scenes, and integrating the standard state data into a use scene state database;
s3, planning a sample selection mode, and sampling unit samples of the standard electronic components and the abnormal electronic components;
s4, formulating acceleration experiment state data, calling and using all standard state data in a scene state database, and verifying the relation between the acceleration experiment state data and the standard state data;
s5, determining service lives of the standard electronic components and the abnormal electronic components in the acceleration experiment, predicting the service lives of the standard electronic components and the abnormal electronic components in the actual use scene, and generating prediction data;
s6, combining the prediction data to deduce the service life ratio of the standard electronic component and the abnormal electronic component.
2. The electronic component lifetime prediction method according to claim 1, wherein: the method for identifying the appearance information of the current electronic component to be inspected in the S1 comprises the following steps:
s1.1, making an appearance identification key point of an electronic component;
s1.2, determining appearance identification key points of each electronic component in a standard state, and establishing a standard appearance identification key point database;
s1.3, comparing appearance information of the electronic components to be detected with a standard appearance recognition key point database, and determining abnormal points;
s1.4, an abnormal point threshold is established, and electronic components with abnormal points exceeding the abnormal point threshold are marked as abnormal electronic components, and electronic components with abnormal points exceeding the abnormal point threshold are marked as standard electronic components.
3. The electronic component lifetime prediction method according to claim 2, characterized in that: the appearance information for identifying the current electronic component to be detected in the S1 adopts a characteristic comparison algorithm, and the algorithm formula is as follows:
W μ ＝[a 1 ，a 2 ，…，a n ]；
M μ ＝[b 1 ，b 2 ，…b m ]；
wherein W is μ Identifying a set of key points, alpha, for each appearance of each electronic component in a standard state 1 To a n
Identifying key points, M, for each appearance of each electronic component μ
Identifying a key point set for each appearance of the current electronic component to be inspected, b 1 To b m
Identifying key points for each appearance of the current electronic component to be inspected, and comparing F (N) characteristic points with a function, N
Key points are identified for the composite standard appearance,
when the threshold value of the key point is identified for the coincident standard appearance, the key point N is identified for the coincident standard appearance
Below the coincidence standard appearance identification key point threshold
When the electronic component to be inspected is an abnormal electronic component, the key point N is identified when the standard appearance is overlapped
Not lower than the threshold value of the coincidence standard appearance identification key pointAnd when the current electronic component to be detected is a standard electronic component.
4. The electronic component lifetime prediction method according to claim 1, wherein: and the standard state data in the step S2 comprise thermal strain caused by cold and hot temperature difference and electric strain when the power is on.
5. The electronic component lifetime prediction method according to claim 1, wherein: the method for planning the sample selection mode in the S3 comprises the following steps:
s3.1, counting the total number of the current electronic components to be detected;
s3.2, determining the proportion between the number of standard electronic components and the number of abnormal electronic components of the current electronic components to be detected by combining a feature comparison algorithm;
s3.3, selecting the sample amounts of the standard electronic components and the abnormal electronic components according to the proportion.
6. The electronic component lifetime prediction method according to claim 1, wherein: the method for preparing the acceleration experiment state data in the S4 comprises the following steps:
s4.1, determining the temperature and the voltage in an acceleration state;
s4.2, verifying the endurance time of the standard electronic component and the abnormal electronic component in the current acceleration state;
s4.3, determining the superposition relation of the acceleration state and the standard state, and deducing the durability of the endurance time in the standard state in the current acceleration state.
7. The electronic component lifetime prediction method according to claim 6, wherein: the superposition relation between the acceleration state and the standard state determined in the step S4.3 adopts a deduction empirical formula, and the formula algorithm is as follows:
wherein L is N For the service life of standard, L A To accelerate the use in the experimental processLife, V A Voltage at standard time, V N To accelerate the voltage during the experiment, z is the voltage acceleration constant, T A To accelerate the temperature during the experiment, T N And θ is a temperature acceleration constant for the standard state.
8. The electronic component lifetime prediction method according to claim 7, wherein: the generation method of the prediction data in the S5 comprises the following steps:
s5.1, deducing the service lives of the standard electronic components and the abnormal electronic components of each sample by combining the deduced empirical formula in the S4.3;
s5.2, integrating the service lives of the standard electronic components and the abnormal electronic components of each sample, and taking the average value as prediction data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311133001.9A CN117113102B (en) | 2023-09-04 | 2023-09-04 | Electronic component life prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311133001.9A CN117113102B (en) | 2023-09-04 | 2023-09-04 | Electronic component life prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117113102A true CN117113102A (en) | 2023-11-24 |
| CN117113102B CN117113102B (en) | 2024-04-16 |
Family
ID=88801943
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311133001.9A Active CN117113102B (en) | 2023-09-04 | 2023-09-04 | Electronic component life prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117113102B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117744013A (en) * | 2024-02-20 | 2024-03-22 | 深圳市联明电源股份有限公司 | Detection system and detection method for power supply electronic components |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102589891A (en) * | 2012-03-02 | 2012-07-18 | 北京理工大学 | Method for estimating endurance life of vehicle motor driving system |
| KR20210123729A (en) * | 2020-04-03 | 2021-10-14 | 에너지인 주식회사 | Acceleration Life Prediction Method of Inverter Stack in Railway Electric Train |
| WO2021208079A1 (en) * | 2020-04-17 | 2021-10-21 | 中国科学院深圳先进技术研究院 | Method and apparatus for obtaining power battery life data, computer device, and medium |
| CN113721157A (en) * | 2021-08-31 | 2021-11-30 | 星恒电源股份有限公司 | Method, device, terminal and storage medium for prolonging service life of battery pack |
| CN113901675A (en) * | 2021-12-09 | 2022-01-07 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Electronic component service life prediction method and device, computer equipment and storage medium |
| CN114114049A (en) * | 2021-09-30 | 2022-03-01 | 北京交通大学 | Lithium ion battery life prediction method based on sample migration |
| CN114565008A (en) * | 2022-01-12 | 2022-05-31 | 西安理工大学 | Lithium battery abnormity detection method based on long-time and short-time memory self-encoder |
| CN115659812A (en) * | 2022-10-29 | 2023-01-31 | 思维实创(哈尔滨)科技有限公司 | Escalator service life prediction method, system, equipment and medium based on urban rail ISCS |
| CN116309470A (en) * | 2023-03-22 | 2023-06-23 | 业成科技（成都）有限公司 | Lamp bead plane fault monitoring method and device, computer equipment and storage medium |
-
2023
- 2023-09-04 CN CN202311133001.9A patent/CN117113102B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102589891A (en) * | 2012-03-02 | 2012-07-18 | 北京理工大学 | Method for estimating endurance life of vehicle motor driving system |
| KR20210123729A (en) * | 2020-04-03 | 2021-10-14 | 에너지인 주식회사 | Acceleration Life Prediction Method of Inverter Stack in Railway Electric Train |
| WO2021208079A1 (en) * | 2020-04-17 | 2021-10-21 | 中国科学院深圳先进技术研究院 | Method and apparatus for obtaining power battery life data, computer device, and medium |
| CN113721157A (en) * | 2021-08-31 | 2021-11-30 | 星恒电源股份有限公司 | Method, device, terminal and storage medium for prolonging service life of battery pack |
| CN114114049A (en) * | 2021-09-30 | 2022-03-01 | 北京交通大学 | Lithium ion battery life prediction method based on sample migration |
| CN113901675A (en) * | 2021-12-09 | 2022-01-07 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Electronic component service life prediction method and device, computer equipment and storage medium |
| CN114565008A (en) * | 2022-01-12 | 2022-05-31 | 西安理工大学 | Lithium battery abnormity detection method based on long-time and short-time memory self-encoder |
| CN115659812A (en) * | 2022-10-29 | 2023-01-31 | 思维实创(哈尔滨)科技有限公司 | Escalator service life prediction method, system, equipment and medium based on urban rail ISCS |
| CN116309470A (en) * | 2023-03-22 | 2023-06-23 | 业成科技（成都）有限公司 | Lamp bead plane fault monitoring method and device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 卢 立 丽 等: "Ｌ ｉ Ｆ ｅ Ｐ ０ ４ 动 力 电 池 加 速 循 环 寿 命 实 验研 究", 电源技术 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117744013A (en) * | 2024-02-20 | 2024-03-22 | 深圳市联明电源股份有限公司 | Detection system and detection method for power supply electronic components |
| CN117744013B (en) * | 2024-02-20 | 2024-04-19 | 深圳市联明电源股份有限公司 | Detection system and detection method for power supply electronic components |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117113102B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117113102B (en) | Electronic component life prediction method | |
| US20180120384A1 (en) | Tester for equipment, apparatus or component with distributed processing function | |
| US9619612B2 (en) | Tester for equipment, apparatus, or component with distributed processing function | |
| CN110824270A (en) | Electricity stealing user identification method and device combining transformer area line loss and abnormal events | |
| CN102117730B (en) | Method for processing parameter data of machine station in manufacturing process of semiconductor and device thereof | |
| CN102449645B (en) | Product inspection device and product inspection method | |
| US11327734B2 (en) | Download system of app, determination method of installation type and storage medium | |
| CN105912437B (en) | The failure prediction system of control device | |
| CN106449296B (en) | Component with monitoring latch assembly monitors system | |
| US9965841B2 (en) | Monitoring system based on image analysis of photos | |
| CN102262191A (en) | Method for forecasting service life of solid tantalum electrolytic capacitor | |
| US20100010708A1 (en) | Methods of Identifying Flight Profiles in Aircraft Maintenance Operations | |
| CN115080555A (en) | Method, system and medium for distinguishing outliers by acquiring population data based on signaling | |
| CN108846239A (en) | A kind of elastic epoxy resin accelerated storage test and appraisal procedure based on temperature and humidity | |
| CN115128438A (en) | Chip internal fault monitoring method and device | |
| Wang et al. | Equivalence analysis of accelerated degradation mechanism based on stochastic degradation models | |
| US7667450B2 (en) | Electrostatic discharge device testing system and method | |
| KR102168737B1 (en) | Quality analysis device and quality analysis method | |
| CN116559619A (en) | Method and related apparatus for testing semiconductor device | |
| US20050184741A1 (en) | Multi-function probe card | |
| CN114913600A (en) | Electricity stealing detection method and device for electricity meter, electronic equipment and storage medium | |
| CN112352165B (en) | Method for monitoring circuit breaker, device using method and Internet of things | |
| CN116075798A (en) | Method and system for predictive analysis and/or process control | |
| KR20210016980A (en) | Server and method for estimating used amount of missed data in advanced metering infrastructure system | |
| WO2023171010A1 (en) | Battery pack management system and battery pack management method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |