CN111898236A - Acceleration factor analysis method for accelerated storage test of electronic complete machine based on failure big data - Google Patents

Acceleration factor analysis method for accelerated storage test of electronic complete machine based on failure big data Download PDF

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CN111898236A
CN111898236A CN202010448476.7A CN202010448476A CN111898236A CN 111898236 A CN111898236 A CN 111898236A CN 202010448476 A CN202010448476 A CN 202010448476A CN 111898236 A CN111898236 A CN 111898236A
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failure
component
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complete machine
acceleration factor
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CN111898236B (en
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蔡健平
陈凤熹
徐洪武
王伟
朱炜
徐福荣
张睿
宋汝宁
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CHINA AEROSPACE STANDARDIZATION INSTITUTE
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    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
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Abstract

The invention provides an acceleration factor analysis method for an accelerated storage test of an electronic complete machine based on failure big data, which utilizes the failure big data obtained in the storage process of different types of electronic complete machines to more accurately analyze the acceleration factor of the electronic complete machine, thereby improving the accuracy of storage period verification. The method specifically comprises the following steps: acquiring a component list of the electronic whole machine; inquiring storage failure information of each component type in the component list according to a quality database of the electronic complete machine to form a failure component list of the electronic complete machine; calculating or looking up a table to obtain the storage temperature T of each component in the failure component listuAnd accelerated storage test temperature TAForming a failure rate table of the components according to the failure rate; analyzing acceleration factor of accelerated storage test according to the failure rate table of the componentSeed Af。

Description

Acceleration factor analysis method for accelerated storage test of electronic complete machine based on failure big data
Technical Field
The invention belongs to the technical field of aerospace reliability, and relates to an acceleration factor analysis method for an acceleration storage test of an electronic complete machine based on failure big data.
Background
The storage period of an electronic complete machine in the national defense equipment is long, and the storage period of the electronic complete machine needs to be verified through an accelerated storage test technology in the later period of test identification or development. Generally, the accelerated storage test of electronic products adopts a constant high-temperature stress acceleration method, and the key parameter for designing the constant high-temperature stress acceleration test scheme is an acceleration factor. Usually, when the acceleration factor is calculated, the whole machine is often regarded as a series system, and each failure is regarded as an independent process, namely, the failure of any link can cause the failure of equipment, and the failure of one link does not influence the normal work of other parts. During specific calculation, the failure rate of each link under different environmental conditions is calculated according to GJB/Z108A, and the acceleration factor of the whole machine is calculated according to the ratio, wherein the specific calculation formula is as follows:
Figure BDA0002506807370000011
in the formula:
Afan acceleration factor;
λATfailure rate under accelerated conditions of the equipment;
λUTfailure rate under equipment storage conditions;
m, the number of link types contained in the equipment;
nithe number of the ith link;
λAifailure rate of the ith link under the condition of accelerated stress;
λUiunder storage conditionsFailure rate of the ith link.
The above formula assumes that the storage failure information of all links in the electronic complete machine cannot be obtained, or that all links in the electronic complete machine are newly designed, which is not an actual situation in engineering. The failure information in the storage of the existing electronic whole machine cannot be fully utilized, so that the accuracy of the calculated acceleration factor is not high. Therefore, the invention can more accurately analyze the acceleration factor of the whole machine by utilizing a large amount of failure information obtained by various electronic whole machines in the storage process, thereby improving the accuracy of the storage period verification.
Disclosure of Invention
The invention provides an acceleration factor analysis method for an accelerated storage test of an electronic complete machine based on failure big data, which utilizes the failure big data obtained in the storage process of various electronic complete machines to more accurately analyze the acceleration factor of the complete machine, thereby improving the accuracy of storage period verification.
An acceleration factor analysis method for an accelerated storage test of an electronic complete machine based on failure big data comprises the following steps:
acquiring a component list of the electronic whole machine;
inquiring storage failure information of each component type in the component list according to a quality database of the electronic complete machine to form a failure component list of the electronic complete machine;
calculating or looking up a table to obtain the storage temperature T of each component in the failure component listuAnd accelerated storage test temperature TAForming a failure rate table of the components according to the failure rate;
analyzing an acceleration factor A of an accelerated storage test according to the component failure rate tablef。
The invention has the beneficial effects that:
1. the invention can utilize big data formed by failure data of other electronic complete machines to more accurately calculate the acceleration factor of the electronic complete machine under certain acceleration conditions, thereby providing support for better developing a design accelerated storage test scheme and supporting more reliable test verification work of the storage period of the electronic complete machine.
2. The method can improve the accuracy of the storage period verification of the electronic complete machine, reduce the waste or risk caused by inaccurate storage period and have greater economic and social benefits. The method has important reference value for developing an accelerated storage test of aerospace electronic products, and can be popularized and applied to accelerated storage tests of complex electronic products in other fields.
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FIG. 1 is a flow chart of an acceleration factor analysis method of an acceleration storage test of an electronic complete machine based on failure big data.
Detailed Description
The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.
As shown in fig. 1, the method for analyzing acceleration factors of accelerated storage tests of electronic complete machines based on failure big data specifically includes the following steps:
step one, acquiring a component list of an electronic whole machine;
in specific implementation, a technician forms a component list of the electronic complete machine for storage period verification by counting selected components in the electronic complete machine, wherein the component list comprises each component model and the number n of the componentsi。
Secondly, inquiring storage failure information of each component type in the component list according to a quality database of the electronic complete machine to form a failure component list of the electronic complete machine;
the quality database referred to herein is generally a failure information list included in the electronic complete machine, and may also be a quality database that is collected and accumulated by an enterprise over the years and is specific to a certain electronic complete machine. Inquiring all the failure conditions of the components in the unitary component list in the step, eliminating the components without failure information in the component list, and counting the model specification of each component and the failure times L of each component in the quality databaseiAnd counting the total failure times L of the components to form a failure component list of the whole electronic machine.
Step three, calculating or looking up a table to obtain each failure component listComponent at storage temperature TuAnd accelerated storage test temperature TAForming a failure rate table of the components according to the failure rate;
in the embodiment, the table look-up refers to the storage temperature T by using GJB/Z108A reliability prediction handbook of non-working state of electronic equipmentuAnd accelerated storage test temperature TAFailure rate lambda of the componentAiAnd λUi。
Fourthly, analyzing an acceleration factor A of an accelerated storage test according to the failure rate table of the componentfThe concrete formula is as follows:
Figure BDA0002506807370000041
in the formula:
Afthe acceleration factor of the whole machine;
m, the number of types of failure components contained in the whole machine;
nithe number of the ith failure component;
Piweighting coefficients of the ith failed component; wherein the content of the first and second substances,
Figure BDA0002506807370000043
Lithe number of failures of each component model specification and the component model specification in the quality database is calculated; and L is the total failure times of the components.
λAiFailure rate of the ith component under the condition of accelerated stress;
λUifailure rate of the ith component under actual storage conditions.
Example 1:
1. and acquiring a component list of the whole electronic machine, wherein the list comprises the model specification of the components and the installed number, and is shown in the following table.
Figure BDA0002506807370000042
Figure BDA0002506807370000051
2. Inquiring the failure information stored in the large quality database accumulated in the enterprise database to form a large failure component data list
In this embodiment, the list includes the specification of each component model and the failure times of the component model in the high-quality database, as shown in the following table:
Figure BDA0002506807370000052
3. obtaining storage conditions and accelerated storage test conditions
In the present example, the storage temperature TuAt 25 ℃ and a temperature T for accelerated storage testAIs 100 ℃.
4. Calculating or looking up a table to obtain the storage temperature T of each component in the failure component big data listuAnd accelerated storage test temperature TAFailure rate of the device is formed into a failure rate table of the device
In the embodiment, the method of inquiring GJB/Z108A reliability prediction handbook of non-working state of electronic equipment is adopted to obtain the component with storage failure at TuAt 25 ℃ and a temperature T for accelerated storage testingAThe failure rate of the component at 100 ℃ is obtained as follows.
Figure BDA0002506807370000053
5. Analysis of acceleration factor for accelerated storage test
According to the failure times l of the ith component in the failure component big data list in the step (2)iAnd the total failure times L, and calculating the weighting coefficient P of each failed component according to the formula (2)iAnd (4) integrating the component list in the step (1) and the component failure rate table in the step (4) to obtain the following table.
Device name Model specification Number ni Weighting coefficient Pi λUiFailure rate (10)－6/h) λAiFailure rate (10)－6/h)
Capacitor with a capacitor element CT4L 23 0.325581 0.023296 1.985332
Resistor with a resistor element RJ24 12 0.023256 0.0087 0.532542
Electrical connector KS239 54 0.403101 0.06129036 3.393216
Relay with a movable contact JGX 21 0.248062 0.033614 0.412497
Substituting the data in the table into an acceleration factor formula to obtain the acceleration factor of the whole machine as follows:
Figure BDA0002506807370000061
the acceleration factor in this case is therefore 53.9.

Claims (2)

1. An acceleration factor analysis method for an accelerated storage test of an electronic complete machine based on failure big data is characterized by comprising the following steps:
acquiring a component list of the electronic whole machine;
inquiring storage failure information of each component type in the component list according to a quality database of the electronic complete machine to form a failure component list of the electronic complete machine;
calculating or looking up a table to obtain the storage temperature T of each component in the failure component listuAnd accelerated storage test temperature TAForming a failure rate table of the components according to the failure rate;
analyzing an acceleration factor A of an accelerated storage test according to the component failure rate tablefThe concrete formula is as follows:
Figure FDA0002506807360000011
in the formula:
Afthe acceleration factor of the whole machine;
m, the number of types of failure components contained in the whole machine;
nithe ith seed lossThe number of the effect components;
Piweighting coefficients of the ith failed component; wherein the content of the first and second substances,
Figure FDA0002506807360000012
Lithe number of failures of each component model specification and the component model specification in the quality database is calculated; and L is the total failure times of the components.
λAiFailure rate of the ith component under the condition of accelerated stress;
λUifailure rate of the ith component under actual storage conditions.
2. The method for analyzing the acceleration factor of the accelerated storage test of the electronic complete machine based on the failure big data as claimed in claim 1, wherein the list of the failure components of the electronic complete machine is obtained by the following method:
inquiring all the failure conditions of the components in the component list, eliminating the components without failure information in the component list, and counting the model specification of each component and the failure times L of each component in the quality databaseiAnd counting the total failure times L of the components to form a failure component list of the whole electronic machine.
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