CN109856483A - The Primary Component reliability estimation method and device of MMC power module - Google Patents
The Primary Component reliability estimation method and device of MMC power module Download PDFInfo
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Abstract
This application involves a kind of Primary Component reliability estimation method of MMC power module and devices.The described method includes: obtaining the corresponding performance parameter of each Primary Component；Performance parameter is to be tested to obtain to Primary Component at multiple time points；Model is estimated based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component, obtains the mean degradation rate and average diffusion coefficient of performance parameter；Using Reliability Model processing mean degradation rate, the average value of average diffusion coefficient and each performance initial value, reliability distribution map is obtained；The reliability of graph evaluation Primary Component, output reliability assessment result are distributed according to reliability.The application assesses the reliability of Primary Component using the performance parameter of the Primary Component operational process of test acquisition, instead of the reliability for assessing Primary Component in traditional technology using the fault data information of Primary Component, so can accurately, the reliability of the efficiently higher and higher Primary Component of quality of evaluation.
Description
Technical field
This application involves technical fields, more particularly to a kind of Primary Component reliability estimation method of MMC power module
And device.
Background technique
In recent years, modularization multi-level converter (Modular Multilevel Converter, MMC) technology become with
Flexible DC transmission is the research hotspot of the mesohigh of representative, large capacity electrical network field, compared to traditional switching tube series connection skill
Art, MMC technology are more easier to require to realize application under occasion in higher voltage grade and higher power.
Most basic unit and core component of the power module as MMC device, the quality of operation characteristic are directly related to
The function and performance of MMC device.And in power module include insulated gate bipolar transistor (Insulated Gate Bipolar
Transistor, IGBT), capacitor, thyristor, Primary Component including self-energizing power supply etc. high reliability become power mould
Block is normal, stablizes, the key of reliability service.
Thus, the reliability of the Primary Component in power module is accurately assessed, to the reliability water of hoisting power module
Flat or even MMC device reliability level all plays an important role, with the continuous development of MMC technology and processing technology,
The reliability level of Primary Component in power module is also being continuously improved, and therefore, during realization, inventor has found tradition
At least there are the following problems in technology: Traditional measurements method can not the higher and higher Primary Component of quality of evaluation reliability.
Summary of the invention
Based on this, it is necessary in view of the above technical problems, provide a kind of power module Primary Component performance estimating method,
Device and computer equipment.
A kind of Primary Component reliability estimation method of MMC power module, comprising the following steps:
Obtain the corresponding performance parameter of each Primary Component；Performance parameter is to test at multiple time points Primary Component
It obtains；
Model, obtaining property are estimated based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component
The mean degradation rate and average diffusion coefficient of energy parameter；
Using Reliability Model processing mean degradation rate, the average value of average diffusion coefficient and each performance initial value, obtain
To reliability distribution map；
The reliability of graph evaluation Primary Component, output reliability assessment result are distributed according to reliability.
In one of the embodiments, based on each performance parameter and the corresponding performance initial value operation of each Primary Component
Parameter estimation model further comprises the steps of: after the step of obtaining the mean degradation rate and average diffusion coefficient of performance parameter
Average value based on mean degradation rate, average diffusion coefficient and each performance initial value runs unreliable degree model,
Obtain unreliable degree distribution map；
According to the unreliable degree of unreliable degree distribution graph evaluation Primary Component, unreliable degree assessment result is exported.
In one of the embodiments, using Reliability Model processing mean degradation rate, average diffusion coefficient and each property
Can the average value of initial value further comprised the steps of: after the step of obtaining reliability distribution map
Average value operational failure probability density mould based on mean degradation rate, average diffusion coefficient and each performance initial value
Type obtains failure probability density profile；
According to failure probability density profile and reliability distribution map, crash rate distribution map is obtained；
It is distributed the crash rate of graph evaluation Primary Component according to crash rate, exports crash rate assessment result.
In one of the embodiments, using Reliability Model processing mean degradation rate, average diffusion coefficient and each property
Can the average value of initial value further comprised the steps of: after the step of obtaining reliability distribution map
According to reliability distribution map, the average life span of Primary Component is obtained.
In one of the embodiments, based on each performance parameter and the corresponding performance initial value operation of each Primary Component
Parameter estimation model, the step of obtaining the mean degradation rate and average diffusion coefficient of performance parameter include:
The corresponding performance initial value operating parameter of performance parameter and Primary Component based on Primary Component estimates model, obtains
The deterioration velocity and diffusion coefficient of Primary Component；
The average value of each deterioration velocity is obtained to be confirmed as putting down as mean degradation rate, and by the average value of each diffusion coefficient
Equal diffusion coefficient.
In one of the embodiments, based on each performance parameter and the corresponding performance initial value operation of each Primary Component
In the step of parameter estimation model, is got parms based on following formula and estimates model:
Wherein, μ indicates deterioration velocity；σ indicates diffusion coefficient；The quantity of N expression Primary Component；N indicates n-th of crucial device
Part；I indicates the performance parameter of i-th test Primary Component；tiIndicate the interval duration of i-th test Primary Component；Yn(ti) table
Show the performance parameter of i-th test；Y0nIndicate the performance initial value of n-th of Primary Component；μnIndicate moving back for n-th of Primary Component
Change rate；σnIndicate the diffusion coefficient of n-th of Primary Component.
In one of the embodiments, using Reliability Model processing mean degradation rate, average diffusion coefficient and each property
Can initial value average value, in the step of obtaining reliability distribution map, Reliability Model is obtained based on following formula:
Wherein, t indicates the moment；D indicates the threshold value of Primary Component failure；Y0Indicate performance initial value；μ indicates speed of degenerating
Rate；σ indicates diffusion coefficient.
A kind of Primary Component reliability assessment device of MMC power module, comprising:
Parameter acquisition module, for obtaining the corresponding performance parameter of each Primary Component；Performance parameter is at multiple time points
Primary Component is tested to obtain；
Model running module, for based on each performance parameter and the corresponding performance initial value operation ginseng of each Primary Component
Number estimation model, obtains the mean degradation rate and average diffusion coefficient of performance parameter；
Reliability distribution map obtains module, for handling mean degradation rate, average diffusion coefficient using Reliability Model
With the average value of each performance initial value, reliability distribution map is obtained；
As a result output module, for being distributed the reliability of graph evaluation Primary Component, output reliability assessment according to reliability
As a result.
A kind of computer equipment, including memory and processor, memory are stored with computer program, and processor executes meter
The step of above method is realized when calculation machine program.
A kind of computer readable storage medium is stored thereon with computer program, when computer program is executed by processor
The step of realizing the above method.
A technical solution in above-mentioned technical proposal is had the following advantages and beneficial effects:
Using following steps: obtaining the corresponding performance parameter of each Primary Component；Performance parameter is at multiple time points to pass
Key device is tested to obtain；Based on each performance parameter and the corresponding performance initial value operating parameter estimation of each Primary Component
Model obtains the mean degradation rate and average diffusion coefficient of performance parameter；Using Reliability Model processing mean degradation rate,
The average value of average diffusion coefficient and each performance initial value, obtains reliability distribution map；It is crucial that graph evaluation is distributed according to reliability
The reliability of device, output reliability assessment result, thus, the Primary Component reliability assessment side of the application MMC power module
Method assesses the reliability of Primary Component using the performance parameter of the Primary Component operational process of test acquisition, instead of traditional skill
The reliability of Primary Component is assessed in art using the fault data information of Primary Component, and then can accurately, efficiently be commented
Estimate the reliability of the higher and higher Primary Component of quality.
Detailed description of the invention
Fig. 1 is the flow diagram of the Primary Component performance estimating method of the application power module in one embodiment；
Fig. 2 is the flow diagram that mean degradation rate and average diffusion coefficient step are obtained in one embodiment；
Fig. 3 is the flow diagram that unreliable degree assessment result step is exported in one embodiment；
Fig. 4 is the flow diagram that crash rate assessment result step is exported in one embodiment；
Fig. 5 is the flow diagram that average life span step is obtained in one embodiment；
Fig. 6 is reliability distribution map in one embodiment；
Fig. 7 is unreliable degree distribution map in one embodiment；
Fig. 8 is the first structure block diagram of the Primary Component capability evaluating device of the application power module in one embodiment；
Fig. 9 is the second structural block diagram of the Primary Component capability evaluating device of the application power module in one embodiment；
Figure 10 is the third structural block diagram of the Primary Component capability evaluating device of the application power module in one embodiment；
Figure 11 is the 4th structural block diagram of the Primary Component capability evaluating device of the application power module in one embodiment；
Figure 12 is the structural block diagram of model running module in one embodiment；
Figure 13 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
In order to solve the problems, such as Traditional measurements method can not the higher and higher Primary Component of quality of evaluation performance, at one
In embodiment, as shown in Figure 1, providing a kind of Primary Component reliability estimation method of power module, comprising the following steps:
Step S110 obtains the corresponding performance parameter of each Primary Component；Performance parameter is at multiple time points to crucial device
Part is tested to obtain.
Wherein, Primary Component is the core devices in the power module of MMC device, and performance plays the performance of power module
To vital effect, so that directly affect the performance of MMC device.For example, Primary Component is the IGBT in power module
(Insulated Gate Bipolar Transistor, insulated gate bipolar transistor) module, thyristor, is asked for capacitor
Energy power supply etc..Each Primary Component refers to multiple identical Primary Components, that is, chooses multiple power module samples, and test same
The performance parameter of Primary Component.
Performance parameter is when power module operates normally, and an operation for carrying out test acquisition to wherein Primary Component is joined
Number.For example, performance parameter can be collection emitter-base bandgap grading conduction voltage drop, threshold voltage of the grid, thermal impedance when Primary Component is ICBT module
Or collector current；When Primary Component is capacitor, performance parameter can be capacitance or dissipation factor；When Primary Component is to take energy
When power supply, performance parameter is voltage or power, therefore, as long as can be measured that the performance of any kind of Primary Component in power module
The Primary Component performance estimating method of the application power module can be used all to realize Performance Evaluation in parameter.
Need to test the multiple values for obtaining same item performance parameter in test process, specifically, different time points can be chosen
Same item performance parameter is tested, to obtain multiple values of same item performance parameter.It in one example, can be according between preset time
Every, carry out test performance parameter, for example, can be spaced 720h (hour) test primary performance parameter.
It should be noted that the performance parameter of Primary Component meets Wiener-Hopf equation model, formula specific as follows:
Y(t0+ Δ t)=Y (t0)+μΔt+σB(Δt)
Wherein, t indicates the moment；Y(t0) indicate Primary Component in t0The performance parameter at moment；Y(t0+ Δ t) indicates crucial device
Part is in t0The performance parameter of+time Δt；The deterioration velocity of μ expression performance parameter；The diffusion coefficient of σ expression performance parameter；B(Δ
T) standard Brownian movement is indicated.
Step S120 estimates mould based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component
Type obtains the mean degradation rate and average diffusion coefficient of performance parameter.
Wherein, performance initial value is the performance parameter of Primary Component when leaving the factory, the i.e. property in Primary Component before use
Energy parameter, in one example, performance initial value can be searched from the product description of Primary Component and be obtained, in another example
In, it can be obtained by test.
In a specific embodiment, as shown in Fig. 2, being based on each performance parameter and the corresponding property of each Primary Component
Can initial value operating parameter estimate model, the step of obtaining the mean degradation rate and average diffusion coefficient of performance parameter includes:
Step S210, the corresponding performance initial value operating parameter estimation of performance parameter and Primary Component based on Primary Component
Model obtains the deterioration velocity and diffusion coefficient of Primary Component；
Step S220 obtains the average value of each deterioration velocity as mean degradation rate, and being averaged each diffusion coefficient
Value is confirmed as average diffusion coefficient.
It should be noted that handling the performance parameter of a Primary Component using parameter estimation model, the key device is obtained
The deterioration velocity and diffusion coefficient of part, the deterioration velocity and diffusion coefficient of other Primary Components are obtained using identical treatment process
It takes.
Further, in a specific embodiment, each performance parameter and the corresponding performance of each Primary Component are based on
Initial value operating parameter is estimated to get parms based on following formula in the step of model and estimate model:
Wherein, μ indicates deterioration velocity；σ indicates diffusion coefficient；The quantity of N expression Primary Component；N indicates n-th of crucial device
Part；I indicates the performance parameter of i-th test Primary Component；tiThe interval duration for indicating i-th test Primary Component (is span
Interval duration from first time test)；Yn(ti) indicate the performance parameter that n-th of Primary Component is tested in i-th；Y0nIndicate the
The performance initial value of n Primary Component；μnIndicate the deterioration velocity of n-th of Primary Component；σnIndicate the expansion of n-th of Primary Component
Dissipate coefficient.
Specifically, to above-mentioned formula respectively to μ n andSeek local derviation, solution obtain μ n andMaximum-likelihood estimation, specifically
It is as follows:
To the μ and σ of each Primary Component2It is averaging, obtains mean degradation rateAnd average diffusion coefficient
In one example, logarithm can be taken to get parms following formula and estimates model
Step S130, using Reliability Model processing mean degradation rate, average diffusion coefficient and each performance initial value
Average value obtains reliability distribution map.
Wherein, in one example, the average value of each performance initial value is obtained based on following formula
In a specific embodiment, using Reliability Model processing mean degradation rate, average diffusion coefficient and each
The average value of performance initial value in the step of obtaining reliability distribution map, obtains Reliability Model R (t) based on following formula:
Wherein, t indicates the moment；D indicates the threshold value of Primary Component failure；Y0Indicate performance initial value；μ indicates speed of degenerating
Rate；σ indicates diffusion coefficient.
In the average value using Reliability Model processing mean degradation rate, average diffusion coefficient and each performance initial value
When, i.e., by mean degradation rateAverage diffusion coefficientWith the average value of each performance initial valueRespectively replace deterioration velocity μ,
Diffusion coefficient σ and performance initial value Y0, it is specific as follows to state shown in formula:
Step S140 is distributed the reliability of graph evaluation Primary Component, output reliability assessment result according to reliability.
Wherein, reliability distribution map is the reliability of Primary Component with the distribution map for using time change and changing, and is being obtained
After getting reliability distribution map, using attached drawing find Primary Component it is current using duration under reliability.
In the embodiment of the Primary Component performance estimating method of the application power module, using following steps: obtaining each pass
The corresponding performance parameter of key device；Performance parameter is to be tested to obtain to Primary Component at multiple time points；Based on each performance
Parameter and the corresponding performance initial value operating parameter of each Primary Component estimate model, obtain the mean degradation speed of performance parameter
Rate and average diffusion coefficient；Using Reliability Model processing mean degradation rate, average diffusion coefficient and each performance initial value
Average value obtains reliability distribution map；The reliability of graph evaluation Primary Component, output reliability assessment knot are distributed according to reliability
Fruit, thus, the Primary Component reliability estimation method of the application MMC power module was run using the Primary Component that test obtains
The performance parameter of journey assesses the reliability of Primary Component, instead of the fault data information for utilizing Primary Component in traditional technology
Assess the reliability of Primary Component, and then can accurately, efficiently the higher and higher Primary Component of quality of evaluation is reliable
Property.
In one embodiment, as shown in figure 3, it is initial based on each performance parameter and the corresponding performance of each Primary Component
It is worth after the step of operating parameter estimates model, obtains the mean degradation rate and average diffusion coefficient of performance parameter, further includes
Step:
Step S330, the average value operation based on mean degradation rate, average diffusion coefficient and each performance initial value can not
By spending model, unreliable degree distribution map is obtained；
Step S340 exports unreliable degree assessment knot according to the unreliable degree of unreliable degree distribution graph evaluation Primary Component
Fruit.
Wherein, the unreliable degree that unreliable degree model is used to assess Primary Component is based on following acquisition in one example
Unreliable degree model F (t):
In the average value using unreliable degree model treatment mean degradation rate, average diffusion coefficient and each performance initial value
When, i.e., by mean degradation rateAverage diffusion coefficientWith the average value of each performance initial valueRespectively replace deterioration velocity μ,
Diffusion coefficient σ and performance initial value Y0, it is specific as follows to state shown in formula:
In the embodiment of the Primary Component performance estimating method of the application power module, the performance parameter of Primary Component is handled
The unreliable degree distribution map of Primary Component is obtained, so that the unreliable degree of Primary Component is evaluated using unreliable degree distribution map,
So that assessing the reliability of Primary Component more fully hereinafter.
In one embodiment, as shown in figure 4, handling mean degradation rate, average diffusion coefficient using Reliability Model
With the average value of each performance initial value, after the step of obtaining reliability distribution map, further comprise the steps of:
Step S440, the average value operational failure based on mean degradation rate, average diffusion coefficient and each performance initial value
Pdf model obtains failure probability density profile；
Step S450 obtains crash rate distribution map according to failure probability density profile and reliability distribution map；
Step S460 is distributed the crash rate of graph evaluation Primary Component according to crash rate, exports crash rate assessment result.
Wherein, failure probability density model is used to assess the failure probability density of Primary Component, is specifically based on following formula
It obtains failure probability density model f (t):
Utilizing the flat of failure probability density model processing mean degradation rate, average diffusion coefficient and each performance initial value
When mean value, i.e., by mean degradation rateAverage diffusion coefficientWith the average value of each performance initial valueReplacement is degenerated fast respectively
Rate μ, diffusion coefficient σ and performance initial value Y0, it is specific as follows to state shown in formula:
In a specific embodiment, crash rate distribution map is obtained based on following formula:
In the embodiment of the Primary Component performance estimating method of the application power module, the performance parameter of Primary Component is handled
The crash rate distribution map of Primary Component is obtained, so that the crash rate of Primary Component is evaluated using crash rate distribution map, so that more
Add the reliability for comprehensively assessing Primary Component.
In one embodiment, as shown in figure 5, handling mean degradation rate, average diffusion coefficient using Reliability Model
With the average value of each performance initial value, after the step of obtaining reliability distribution map, further comprise the steps of:
Step S540 obtains the average life span of Primary Component according to reliability distribution map.
Specifically, obtaining average life span based on following formula:
In the embodiment of the Primary Component performance estimating method of the application power module, handled out using reliability distribution map
The average life span of Primary Component, to be conducive to the timely maintenance and replacement of device.
The Primary Component performance estimating method of the application power module in order to better understand, will be used for below with the application
It is illustrated for the performance of assessment IGBT module:
(720h) tests the collection emitter-base bandgap grading conduction voltage drop of its IGBT module every other month, failure threshold D=
0.5, the degradation trend of collection emitter-base bandgap grading conduction voltage drop meets Wiener-Hopf equation, and situation of change is as shown in table 1.
Table 1 collects emitter-base bandgap grading conduction voltage drop tables of data
| Sample | 0h | 720h | 1440h | 2160h | 2880h | 3600h | 4320h | 5040h | 5760h | 6480h | 7200h |
| A1 | 4.1267 | 4.1354 | 4.1469 | 4.1454 | 4.1528 | 4.1606 | 4.1572 | 4.1669 | 4.1734 | 4.1734 | 4.1792 |
| A2 | 4.1260 | 4.1355 | 4.1472 | 4.1466 | 4.1506 | 4.1600 | 4.1612 | 4.1678 | 4.1783 | 4.1711 | 4.1807 |
| A3 | 4.1271 | 4.1351 | 4.1461 | 4.1467 | 4.1524 | 4.1616 | 4.1534 | 4.1683 | 4.1800 | 4.1729 | 4.1880 |
| A4 | 4.1269 | 4.1347 | 4.1469 | 4.1461 | 4.1532 | 4.1615 | 4.1567 | 4.1681 | 4.1762 | 4.1745 | 4.1829 |
| A5 | 4.1269 | 4.1352 | 4.1467 | 4.1467 | 4.1539 | 4.1592 | 4.1564 | 4.1677 | 4.1695 | 4.1744 | 4.1792 |
| A6 | 4.1268 | 4.1347 | 4.1468 | 4.1470 | 4.1546 | 4.1631 | 4.1568 | 4.1669 | 4.1749 | 4.1725 | 4.1784 |
| A7 | 4.1270 | 4.1343 | 4.1465 | 4.1468 | 4.1537 | 4.1623 | 4.1583 | 4.1661 | 4.1695 | 4.1677 | 4.1697 |
| A8 | 4.1268 | 4.1355 | 4.1480 | 4.1468 | 4.1531 | 4.1601 | 4.1589 | 4.1640 | 4.1766 | 4.1694 | 4.1723 |
| A9 | 4.1269 | 4.1355 | 4.1464 | 4.1460 | 4.1540 | 4.1603 | 4.1587 | 4.1712 | 4.1762 | 4.1780 | 4.1752 |
| A10 | 4.1265 | 4.1349 | 4.1459 | 4.1437 | 4.1551 | 4.1588 | 4.1549 | 4.1708 | 4.1673 | 4.1756 | 4.1861 |
| A11 | 4.1271 | 4.1350 | 4.1465 | 4.1446 | 4.1537 | 4.1592 | 4.1579 | 4.1688 | 4.1683 | 4.1691 | 4.1687 |
| A12 | 4.1271 | 4.1349 | 4.1468 | 4.1456 | 4.1542 | 4.1615 | 4.1574 | 4.1663 | 4.1771 | 4.1759 | 4.1666 |
| A13 | 4.1266 | 4.1356 | 4.1479 | 4.1437 | 4.1532 | 4.1624 | 4.1572 | 4.1718 | 4.1740 | 4.1742 | 4.1816 |
| A14 | 4.1269 | 4.1349 | 4.1472 | 4.1452 | 4.1523 | 4.1604 | 4.1547 | 4.1657 | 4.1711 | 4.1739 | 4.1687 |
| A15 | 4.1264 | 4.1348 | 4.1470 | 4.1444 | 4.1531 | 4.1633 | 4.1551 | 4.1699 | 4.1733 | 4.1741 | 4.1855 |
| A16 | 4.1269 | 4.1349 | 4.1465 | 4.1474 | 4.1541 | 4.1592 | 4.1605 | 4.1630 | 4.1713 | 4.1771 | 4.1815 |
| A17 | 4.1267 | 4.1362 | 4.1474 | 4.1452 | 4.1548 | 4.1604 | 4.1562 | 4.1660 | 4.1745 | 4.1709 | 4.1828 |
| A18 | 4.1262 | 4.1350 | 4.1483 | 4.1460 | 4.1521 | 4.1622 | 4.1561 | 4.1640 | 4.1719 | 4.1741 | 4.1756 |
| A19 | 4.1262 | 4.1356 | 4.1475 | 4.1459 | 4.1525 | 4.1596 | 4.1590 | 4.1718 | 4.1704 | 4.1719 | 4.1865 |
| A20 | 4.1264 | 4.1353 | 4.1469 | 4.1462 | 4.1534 | 4.1625 | 4.1578 | 4.1646 | 4.1698 | 4.1690 | 4.1775 |
It should be noted that a kind of table, sample is IGBT module, and A1~A20 is the label of sample.
According to the Primary Component performance estimating method processing collection emitter-base bandgap grading conduction voltage drop of the application power module, what is obtained is reliable
Distribution map is spent as shown in fig. 6, obtained unreliable degree distribution map is as shown in Figure 7.
Average life span is
It should be understood that although each step in the flow chart of Fig. 1-5 is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, these steps can execute in other order.Moreover, at least one in Fig. 1-5
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, the execution sequence in these sub-steps or stage is also not necessarily successively
It carries out, but can be at least part of the sub-step or stage of other steps or other steps in turn or alternately
It executes.
In one embodiment, as shown in figure 8, additionally providing a kind of Primary Component reliability assessment of MMC power module
Device, comprising:
Parameter acquisition module 810, for obtaining the corresponding performance parameter of each Primary Component；Performance parameter is in multiple times
Point is tested to obtain to Primary Component；
Model running module 820, for based on each performance parameter and the corresponding performance initial value operation of each Primary Component
Parameter estimation model obtains the mean degradation rate and average diffusion coefficient of performance parameter；
Reliability distribution map obtains module 830, for handling mean degradation rate, average diffusion system using Reliability Model
Several and each performance initial value average value, obtains reliability distribution map；
As a result output module 840, for being distributed the reliability of graph evaluation Primary Component according to reliability, output reliability is commented
Estimate result.
In one embodiment, as shown in figure 9, the Primary Component reliability assessment device of the application MMC power module also
Include:
Unreliable degree distribution map obtains module 910, for based at the beginning of mean degradation rate, average diffusion coefficient and each performance
The average value of initial value runs unreliable degree model, obtains unreliable degree distribution map；
As a result output module 940 are also used to the unreliable degree according to unreliable degree distribution graph evaluation Primary Component, and output is not
Reliability assessment result.
In one embodiment, as shown in Figure 10, the Primary Component reliability assessment device of the application MMC power module is also
Include:
Failure probability density profile obtains module 1010, for based on mean degradation rate, average diffusion coefficient and each
The average value operational failure pdf model of performance initial value, obtains failure probability density profile；
Crash rate distribution map obtains module 1020, for obtaining according to failure probability density profile and reliability distribution map
To crash rate distribution map；
As a result output module 640 are also used to be distributed the crash rate of graph evaluation Primary Component according to crash rate, export crash rate
Assessment result.
In one embodiment, as shown in figure 11, the Primary Component reliability assessment device of the application MMC power module is also
Include:
Average life span obtains module 1110, for obtaining the average life span of Primary Component according to reliability distribution map.
In one embodiment, as shown in figure 12, model running module includes:
Model running unit 821, for performance parameter and the corresponding performance initial value of Primary Component based on Primary Component
Operating parameter estimates model, obtains the deterioration velocity and diffusion coefficient of Primary Component；
Mean value acquiring unit 823, for obtaining the average value of each deterioration velocity as mean degradation rate, and by each diffusion
The average value of coefficient is confirmed as average diffusion coefficient.
The specific restriction of Primary Component reliability assessment device about MMC power module may refer to above for
The restriction of the Primary Component reliability estimation method of MMC power module, details are not described herein.The key of above-mentioned MMC power module
Modules in device reliability assessment device can be realized fully or partially through software, hardware and combinations thereof.It is above-mentioned each
Module can be embedded in the form of hardware or independently of in the processor in computer equipment, can also be stored in meter in a software form
It calculates in the memory in machine equipment, executes the corresponding operation of the above modules in order to which processor calls.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junction
Composition can be as shown in figure 13.The computer equipment include by system bus connect processor, memory, network interface and
Database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipment
Include non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and data
Library.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The database of machine equipment is for data such as storage performance parameter, performance initial values.The network interface of the computer equipment be used for
External terminal passes through network connection communication.To realize a kind of MMC power module when the computer program is executed by processor
Primary Component reliability estimation method.
It will be understood by those skilled in the art that structure shown in Figure 13, only part relevant to application scheme
The block diagram of structure, does not constitute the restriction for the computer equipment being applied thereon to application scheme, and specific computer is set
Standby may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment, including memory and processor are provided, is stored in memory
Computer program, the processor perform the steps of when executing computer program
Obtain the corresponding performance parameter of each Primary Component；Performance parameter is to test at multiple time points Primary Component
It obtains；
Model, obtaining property are estimated based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component
The mean degradation rate and average diffusion coefficient of energy parameter；
Using Reliability Model processing mean degradation rate, the average value of average diffusion coefficient and each performance initial value, obtain
To reliability distribution map；
The reliability of graph evaluation Primary Component is distributed according to reliability, output reliability assesses assessment result
In one embodiment, it is also performed the steps of when processor executes computer program
Average value based on mean degradation rate, average diffusion coefficient and each performance initial value runs unreliable degree model,
Obtain unreliable degree distribution map；
According to the unreliable degree of unreliable degree distribution graph evaluation Primary Component, unreliable degree assessment result is exported.
In one embodiment, it is also performed the steps of when processor executes computer program
Average value operational failure probability density mould based on mean degradation rate, average diffusion coefficient and each performance initial value
Type obtains failure probability density profile；
According to failure probability density profile and reliability distribution map, crash rate distribution map is obtained；
It is distributed the crash rate of graph evaluation Primary Component according to crash rate, exports crash rate assessment result.
In one embodiment, it is also performed the steps of when processor executes computer program
According to reliability distribution map, the average life span of Primary Component is obtained.
In one embodiment, it is also performed the steps of when processor executes computer program
The corresponding performance initial value operating parameter of performance parameter and Primary Component based on Primary Component estimates model, obtains
The deterioration velocity and diffusion coefficient of Primary Component；
The average value of each deterioration velocity is obtained to be confirmed as putting down as mean degradation rate, and by the average value of each diffusion coefficient
Equal diffusion coefficient.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of when being executed by processor
Obtain the corresponding performance parameter of each Primary Component；Performance parameter is to test at multiple time points Primary Component
It obtains；
Model, obtaining property are estimated based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component
The mean degradation rate and average diffusion coefficient of energy parameter；
Using Reliability Model processing mean degradation rate, the average value of average diffusion coefficient and each performance initial value, obtain
To reliability distribution map；
The reliability of graph evaluation Primary Component, output reliability assessment result are distributed according to reliability
In one embodiment, it is also performed the steps of when computer program is executed by processor
Average value based on mean degradation rate, average diffusion coefficient and each performance initial value runs unreliable degree model,
Obtain unreliable degree distribution map；
According to the unreliable degree of unreliable degree distribution graph evaluation Primary Component, unreliable degree assessment result is exported.
In one embodiment, it is also performed the steps of when computer program is executed by processor
Average value operational failure probability density mould based on mean degradation rate, average diffusion coefficient and each performance initial value
Type obtains failure probability density profile；
According to failure probability density profile and reliability distribution map, crash rate distribution map is obtained；
It is distributed the crash rate of graph evaluation Primary Component according to crash rate, exports crash rate assessment result.
In one embodiment, it is also performed the steps of when computer program is executed by processor
According to reliability distribution map, the average life span of Primary Component is obtained.
In one embodiment, it is also performed the steps of when computer program is executed by processor
The corresponding performance initial value operating parameter of performance parameter and Primary Component based on Primary Component estimates model, obtains
The deterioration velocity and diffusion coefficient of Primary Component；
The average value of each deterioration velocity is obtained to be confirmed as putting down as mean degradation rate, and by the average value of each diffusion coefficient
Equal diffusion coefficient.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
The limitation to claim therefore cannot be interpreted as.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of Primary Component reliability estimation method of MMC power module, which comprises the following steps:
Obtain the corresponding performance parameter of each Primary Component；The performance parameter is to carry out at multiple time points to the Primary Component
Test obtains；
Model is estimated based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component, is obtained
To the mean degradation rate and average diffusion coefficient of the performance parameter；
The flat of the mean degradation rate, the average diffusion coefficient and each performance initial value is handled using Reliability Model
Mean value obtains reliability distribution map；
According to the reliability of Primary Component described in reliability distribution graph evaluation, output reliability assessment result.
2. the Primary Component reliability estimation method of MMC power module according to claim 1, which is characterized in that be based on
Each performance parameter and the corresponding performance initial value operating parameter of each Primary Component estimate model, obtain the property
After the step of mean degradation rate and average diffusion coefficient of energy parameter, further comprise the steps of:
Average value operation based on the mean degradation rate, the average diffusion coefficient and each performance initial value is unreliable
Model is spent, unreliable degree distribution map is obtained；
According to the unreliable degree of Primary Component described in the unreliable degree distribution graph evaluation, unreliable degree assessment result is exported.
3. the Primary Component reliability estimation method of MMC power module according to claim 1, which is characterized in that use
Reliability Model handles the average value of the mean degradation rate, the average diffusion coefficient and each performance initial value, obtains
After the step of to reliability distribution map, further comprise the steps of:
Average value operational failure based on the mean degradation rate, the average diffusion coefficient and each performance initial value is general
Rate density model obtains failure probability density profile；
According to the failure probability density profile and the reliability distribution map, crash rate distribution map is obtained；
According to the crash rate of Primary Component described in crash rate distribution graph evaluation, crash rate assessment result is exported.
4. the Primary Component reliability estimation method of MMC power module according to claim 1, which is characterized in that use
Reliability Model handles the average value of the mean degradation rate, the average diffusion coefficient and each performance initial value, obtains
After the step of to reliability distribution map, further comprise the steps of:
According to the reliability distribution map, the average life span of the Primary Component is obtained.
5. the Primary Component reliability estimation method of MMC power module according to any one of claims 1 to 4, feature
It is, model is estimated based on each performance parameter and the corresponding performance initial value operating parameter of each Primary Component, is obtained
To the performance parameter mean degradation rate and average diffusion coefficient the step of include:
The corresponding performance initial value of performance parameter and the Primary Component based on the Primary Component runs the parameter Estimation
Model obtains the deterioration velocity and diffusion coefficient of the Primary Component；
The average value of each deterioration velocity is obtained as the mean degradation rate, and by the average value of each diffusion coefficient
It is confirmed as the average diffusion coefficient.
6. the Primary Component reliability estimation method of MMC power module according to claim 5, which is characterized in that be based on
In the step of each performance parameter and the corresponding performance initial value operating parameter of each Primary Component estimate model, base
The parameter estimation model is obtained in following formula:
Wherein, μ indicates deterioration velocity；σ indicates diffusion coefficient；The quantity of N expression Primary Component；N indicates n-th of Primary Component；i
Indicate the performance parameter of i-th test Primary Component；tiIndicate the interval duration of i-th test Primary Component；Yn(ti) indicate the
The performance parameter that n Primary Component is tested in i-th；Y0nIndicate the performance initial value of n-th of Primary Component；μnIt indicates n-th
The deterioration velocity of Primary Component；σnIndicate the diffusion coefficient of n-th of Primary Component.
7. the Primary Component reliability estimation method of MMC power module according to claim 5, which is characterized in that use
Reliability Model handles the average value of the mean degradation rate, the average diffusion coefficient and each performance initial value, obtains
In the step of to reliability distribution map, Reliability Model is obtained based on following formula:
Wherein, t indicates the moment；D indicates the threshold value of Primary Component failure；Y0Indicate performance initial value；μ indicates deterioration velocity；σ table
Show diffusion coefficient.
8. a kind of Primary Component reliability assessment device of MMC power module characterized by comprising
Parameter acquisition module, for obtaining the corresponding performance parameter of each Primary Component；The performance parameter is at multiple time points
The Primary Component is tested to obtain；
Model running module, for based on each performance parameter and the corresponding performance initial value fortune of each Primary Component
Row parameter estimation model obtains the mean degradation rate and average diffusion coefficient of the performance parameter；
Reliability distribution map obtains module, for handling the mean degradation rate, the average diffusion using Reliability Model
The average value of coefficient and each performance initial value, obtains reliability distribution map；
As a result output module, for the reliability of the Primary Component according to reliability distribution graph evaluation, output reliability
Assessment result.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the step of processor realizes any one of claims 1 to 7 the method when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 7 is realized when being executed by processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111458617A (en) * | 2020-03-19 | 2020-07-28 | 深圳供电局有限公司 | Semiconductor device reliability detection method, semiconductor device reliability detection device, computer equipment and medium |
| CN113242570A (en) * | 2021-04-26 | 2021-08-10 | 深圳供电局有限公司 | Reliability evaluation method and device for uplink communication module and computer equipment |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101666662A (en) * | 2009-09-25 | 2010-03-10 | 北京航空航天大学 | Accelerated degradation test prediction method based on fuzzy theory |
| CN101710368A (en) * | 2009-12-21 | 2010-05-19 | 北京航空航天大学 | Bayesian reliability comprehensive estimation method based on multisource degraded data |
| CN103971024A (en) * | 2014-05-26 | 2014-08-06 | 华北电力大学（保定） | Method for evaluating reliability of relaying protection systems under small sample failure data |
| CN104463331A (en) * | 2014-12-29 | 2015-03-25 | 北京航空航天大学 | Accelerated degradation experiment modeling method based on fuzzy theory |
| CN107228926A (en) * | 2017-06-09 | 2017-10-03 | 电子科技大学 | The explosive logic network analysis method for reliability assessed based on accelerated aging |
| CN107515965A (en) * | 2017-07-27 | 2017-12-26 | 北京航空航天大学 | A kind of acceleration degeneration modelling evaluation method based on uncertain course |
| CN107766628A (en) * | 2017-09-29 | 2018-03-06 | 北京航空航天大学 | A kind of dynamic Degradation Reliability appraisal procedure based on life information fusion |
| CN107944090A (en) * | 2017-10-31 | 2018-04-20 | 中国船舶工业系统工程研究院 | Gas turbine engine systems performance prediction method based on critical component failure model |
| CN107958310A (en) * | 2017-12-07 | 2018-04-24 | 北京航空航天大学 | A kind of optimal Maintenance Design method of the existing structure based on interval model time-varying reliability for considering quiet dynamic uncertainty |
| CN108520152A (en) * | 2018-04-13 | 2018-09-11 | 中国人民解放军火箭军工程大学 | A kind of the service life distribution determination method and system of engineering equipment |
| CN109033710A (en) * | 2018-08-30 | 2018-12-18 | 电子科技大学 | A kind of momenttum wheel reliability estimation method based on more performance degradations |
| CN109149982A (en) * | 2018-08-21 | 2019-01-04 | 南方电网科学研究院有限责任公司 | Modularization level converter power Module Reliability appraisal procedure |
| CN109188232A (en) * | 2018-09-06 | 2019-01-11 | 河北工业大学 | A kind of construction method of IGBT module status assessment and predicting residual useful life model |
-
2019
- 2019-01-30 CN CN201910089275.XA patent/CN109856483B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101666662A (en) * | 2009-09-25 | 2010-03-10 | 北京航空航天大学 | Accelerated degradation test prediction method based on fuzzy theory |
| CN101710368A (en) * | 2009-12-21 | 2010-05-19 | 北京航空航天大学 | Bayesian reliability comprehensive estimation method based on multisource degraded data |
| CN103971024A (en) * | 2014-05-26 | 2014-08-06 | 华北电力大学（保定） | Method for evaluating reliability of relaying protection systems under small sample failure data |
| CN104463331A (en) * | 2014-12-29 | 2015-03-25 | 北京航空航天大学 | Accelerated degradation experiment modeling method based on fuzzy theory |
| CN107228926A (en) * | 2017-06-09 | 2017-10-03 | 电子科技大学 | The explosive logic network analysis method for reliability assessed based on accelerated aging |
| CN107515965A (en) * | 2017-07-27 | 2017-12-26 | 北京航空航天大学 | A kind of acceleration degeneration modelling evaluation method based on uncertain course |
| CN107766628A (en) * | 2017-09-29 | 2018-03-06 | 北京航空航天大学 | A kind of dynamic Degradation Reliability appraisal procedure based on life information fusion |
| CN107944090A (en) * | 2017-10-31 | 2018-04-20 | 中国船舶工业系统工程研究院 | Gas turbine engine systems performance prediction method based on critical component failure model |
| CN107958310A (en) * | 2017-12-07 | 2018-04-24 | 北京航空航天大学 | A kind of optimal Maintenance Design method of the existing structure based on interval model time-varying reliability for considering quiet dynamic uncertainty |
| CN108520152A (en) * | 2018-04-13 | 2018-09-11 | 中国人民解放军火箭军工程大学 | A kind of the service life distribution determination method and system of engineering equipment |
| CN109149982A (en) * | 2018-08-21 | 2019-01-04 | 南方电网科学研究院有限责任公司 | Modularization level converter power Module Reliability appraisal procedure |
| CN109033710A (en) * | 2018-08-30 | 2018-12-18 | 电子科技大学 | A kind of momenttum wheel reliability estimation method based on more performance degradations |
| CN109188232A (en) * | 2018-09-06 | 2019-01-11 | 河北工业大学 | A kind of construction method of IGBT module status assessment and predicting residual useful life model |
Non-Patent Citations (2)
| Title |
|---|
| GUANGZE PAN: "A Reliability Modeling and Evaluation Method of Modular Multilevel Converters", 《IEEE》 * |
| 潘广泽: "基于Wiener过程的高精度石英挠性加速度计贮存可靠性评估", 《环境技术增刊》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111458617A (en) * | 2020-03-19 | 2020-07-28 | 深圳供电局有限公司 | Semiconductor device reliability detection method, semiconductor device reliability detection device, computer equipment and medium |
| CN113242570A (en) * | 2021-04-26 | 2021-08-10 | 深圳供电局有限公司 | Reliability evaluation method and device for uplink communication module and computer equipment |
| CN113242570B (en) * | 2021-04-26 | 2023-08-11 | 深圳供电局有限公司 | Method, device and computer equipment for evaluating reliability of uplink communication module |
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