CN107918704A - Charge amplifier Storage Life Prediction method, apparatus, storage medium and computer equipment - Google Patents
Charge amplifier Storage Life Prediction method, apparatus, storage medium and computer equipment Download PDFInfo
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- CN107918704A CN107918704A CN201711108210.2A CN201711108210A CN107918704A CN 107918704 A CN107918704 A CN 107918704A CN 201711108210 A CN201711108210 A CN 201711108210A CN 107918704 A CN107918704 A CN 107918704A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- 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
Abstract
The present invention relates to a kind of charge amplifier Storage Life Prediction method, apparatus, storage medium and computer equipment.The characteristic parameter and the corresponding characteristic sequence of characteristic parameter of charge amplifier storage life are obtained first,When the characteristic data value in characteristic sequence monotone decreasing and characteristic sequence is more than the default failure threshold of characteristic parameter,Using characteristic sequence as Modelling feature data sequence,According to Modelling feature data sequence,Solved based on time response function,Obtain the prediction model of charge amplifier storage life,By the characteristic parameter for characterizing charge amplifier life characteristics,Obtain Modelling feature data sequence,Based on time response function,And then obtain the prediction model of charge amplifier storage life,With calculated charge amplifier storage life,So solve the problems, such as that charge amplifier storage life is unpredictable,Establish the Storage Life Prediction model of charge amplifier,It can realize and the storage life of charge amplifier is predicted.
Description
Technical field
The present invention relates to electronic device detection technique field, more particularly to a kind of charge amplifier Storage Life Prediction side
Method, device, storage medium and computer equipment.
Background technology
With the development of science and technology, the popularity rate of electronic equipment is higher and higher, and storage life is one of electronic equipment
Important indicator.Charge amplifier fits tune device as essential signal, the faint charge signal that it can export sensor
The voltage signal of amplification is converted into, while the high impedance output of sensor can be converted into Low ESR output again, moreover it is possible to is prevented
Power supply short circuit, thus it is widely used in Detection of Weak Signals field.
Charge amplifier is a series connection link of test system, its failure directly affects the failure of whole system, because
This, carries out research to the storage life of charge amplifier and is very important.Traditional, product is obtained by accelerated life test
Burn-out life data, and then Life Prediction Model is established, but within the limited accelerated test time, it is difficult to obtain electric charge amplification
The fail data of device, often without fail data, thus can not establish Life Prediction Model to charge amplifier.
The content of the invention
Based on this, it is necessary in view of the above-mentioned problems, it is pre- to provide a kind of storage life progress that can be achieved to charge amplifier
Charge amplifier Storage Life Prediction method, apparatus, storage medium and the computer equipment of survey.
A kind of Forecasting Methodology of charge amplifier storage life, including：
Obtain the characteristic parameter and the corresponding characteristic sequence of characteristic parameter of charge amplifier storage life；
When the characteristic data value in characteristic sequence monotone decreasing and characteristic sequence is more than the default of characteristic parameter
During failure threshold, using characteristic sequence as Modelling feature data sequence；
According to Modelling feature data sequence, solved based on time response function, obtain the pre- of charge amplifier storage life
Survey model；
According to the prediction model of charge amplifier storage life, charge amplifier storage life is obtained.
A kind of prediction meanss of charge amplifier storage life, including：
Characteristic parameter acquisition module, characteristic parameter and characteristic parameter for obtaining charge amplifier storage life correspond to
Characteristic sequence；
Retrieval module is modeled, for when the characteristic in characteristic sequence monotone decreasing and characteristic sequence
When value is more than the default failure threshold of characteristic parameter, using characteristic sequence as Modelling feature data sequence；
Prediction model acquisition module, for according to Modelling feature data sequence, being solved based on time response function, obtaining electricity
The prediction model of lotus amplifier storage life；
Storage life computing module, for the prediction model according to charge amplifier storage life, obtains charge amplifier
Storage life.
A kind of storage medium, is stored thereon with computer program, which realizes the above method when being executed by processor
Step.
A kind of computer equipment, including memory, processor and storage can be run on a memory and on a processor
The step of computer program, processor realizes the above method when performing the program.
Above-mentioned charge amplifier Storage Life Prediction method, apparatus, storage medium and computer equipment, it is first determined electric charge
The characteristic parameter of amplifier storage life, and the corresponding characteristic sequence of characteristic parameter is obtained, when characteristic sequence is dull
Successively decrease and when characteristic data value in characteristic sequence is more than the default failure threshold of characteristic parameter, characteristic sequence is made
For Modelling feature data sequence, according to Modelling feature data sequence, solved based on time response function, obtain charge amplifier storage
The prediction model in service life is deposited, according to the prediction model of charge amplifier storage life, charge amplifier storage life is obtained, passes through
The characteristic parameter of charge amplifier life characteristics is characterized, obtains Modelling feature data sequence, based on time response function, and then
To the prediction model of charge amplifier storage life, with calculated charge amplifier storage life, so solves charge amplifier
The problem of storage life is unpredictable, establishes the Storage Life Prediction model of charge amplifier, can realize to charge amplifier
Storage life be predicted.
Brief description of the drawings
Fig. 1 is the flow diagram of the Forecasting Methodology of charge amplifier storage life in one embodiment；
Fig. 2 is the flow diagram of the Forecasting Methodology of charge amplifier storage life in one embodiment；
Fig. 3 is the flow diagram of the Forecasting Methodology of charge amplifier storage life in one embodiment；
Fig. 4 is the structure diagram of the prediction meanss of charge amplifier storage life in one embodiment；
Fig. 5 is the structure diagram of the prediction meanss of charge amplifier storage life in one embodiment；
Fig. 6 is that the data of charge amplifier gain in one embodiment move towards figure.
Embodiment
As shown in Figure 1, a kind of Forecasting Methodology of charge amplifier storage life, including：
S100, obtains the characteristic parameter and the corresponding characteristic sequence of characteristic parameter of charge amplifier storage life.
Charge amplifier include electric charge conversion stage, it is suitable adjust a wage scale, low-pass filter, high-pass filter, final stage power amplifier, power supply etc.
Several parts, the performance parameter of charge amplifier storage life include frequency, noiseproof feature, frequency response and gain.
In one embodiment, as shown in Fig. 2, the step of obtaining the characteristic parameter of charge amplifier storage life specifically may be used
With including：S120, obtains the corresponding monitoring data sequence of each performance parameter of charge amplifier storage life；S140, to each prison
The unbiased esti-mator that data sequence carries out average and variance is surveyed, obtains the corresponding statistic of each performance parameter；And S160, by each property
The corresponding statistic of energy parameter compared with default statistic, obtains the characteristic parameter of charge amplifier storage life respectively.
Each performance parameter of charge amplifier storage life includes frequency, noiseproof feature, frequency response and gain, obtains
The corresponding monitoring data of each performance parameter, respectively correspondingly form monitoring data sequence.Average is carried out to each monitoring data sequence
With the unbiased esti-mator of variance, obtain the corresponding statistic of each performance parameter, by the corresponding statistic of each performance parameter respectively with advance
If statistic is compared, specifically, when the statistic of certain performance parameter is more than the default statistic of the performance parameter, by this
Characteristic parameter of the performance parameter as charge amplifier storage life.By taking gain monitoring data sequence as an example, number is monitored to gain
The unbiased esti-mator of average and variance is asked for according to sequence, and then asks for obtaining the corresponding statistic of gain of charge amplifier, will be counted
Obtained statistic is compared with default statistic, when the statistic being calculated is greater than or equal to default statistic,
Illustrate that gain has significant degeneration, carry out same processing to other performance parameter respectively, there will be the performance significantly degenerated to join
Characteristic parameter of the number as charge amplifier storage life.
In one embodiment, as shown in Fig. 2, the step of obtaining characteristic parameter corresponding characteristic sequence specifically can be with
Including：S180, obtains the corresponding characteristic monitoring data sequent of characteristic parameter；And S190, characteristic is supervised based on interpolation method
Sequencing row are handled, and obtain characteristic sequence.
After the characteristic parameter for determining charge amplifier, the corresponding characteristic monitoring data sequent of characteristic parameter is obtained, such as,
The gain monitoring data sequence of charge amplifier is：1.989,1.98,1.98,1.98,1.98,1.94,1.97,1.93,
1.93 }, it can be seen that, which handles the sequence based on interpolation method, obtains characteristic sequence there are catastrophe point 1.97
It is classified as：{ 1.989,1.98,1.98,1.98,1.98,1.94,1.935,1.93,1.93 }.Based on interpolation method to sequence at
Reason is exactly to be aware of sequence overall trend, and by previous data and the latter data, mutation value is replaced with an estimated value.
S200, when the characteristic data value in characteristic sequence monotone decreasing and characteristic sequence is more than characteristic parameter
During default failure threshold, using characteristic sequence as Modelling feature data sequence.
To characteristic sequence carry out monotonicity inspection, such as charge amplifier characteristic sequence 1.989,1.98,
1.98,1.98,1.98,1.94,1.935,1.93,1.93 }, it can be seen that this feature data sequence monotone decreasing, and each monitoring
Data value is more than the default failure threshold 1.89 of gain, and therefore, which can be used as Modelling feature data sequence.
S300, according to Modelling feature data sequence, is solved based on time response function, obtains charge amplifier storage life
Prediction model.
Time response function has the advantages that to be suitable for a small number of evidences, poor information modeling, according to Modelling feature data sequence, base
Solved in time response function, the step of obtaining the prediction model of charge amplifier storage life, as shown in figure 3, specifically can be with
Including：S320, the expression formula of settling time receptance function, the expression formula of time response function include the first parameter and the second ginseng
Number；S340, establishes the calculation formula of the first parameter and the calculation formula of the second parameter, based on Modelling feature data sequence, solves
Obtain the first parameter values and the second parameter values；S360, according to the first parameter values, the second parameter values and time response letter
Several expression formulas, obtains the corresponding time response function of characteristic parameter of charge amplifier；And S380, according to characteristic parameter pair
The corresponding time response function of default failure threshold and characteristic parameter answered, obtains the prediction mould of charge amplifier storage life
Type.
The expression formula of time response function is：
The calculation formula of first parameter a is：
The calculation formula of second parameter b is：
Based on Modelling feature data sequence, solution obtains the first parameter values and the second parameter values, specifically, a=
0.0032, b=1.9849.
Thus the corresponding time response function of characteristic parameter for obtaining charge amplifier is：
According to the corresponding default failure threshold 1.89 of characteristic parameter time response function corresponding with characteristic parameter, electricity is obtained
The prediction model of lotus amplifier storage life is：
Wherein, t represents the storage life of charge amplifier, XFailure thresholdRepresent the characteristic parameter of charge amplifier storage life
Failure threshold, XInitiallyRepresent the initial value of the characteristic parameter of charge amplifier storage life.
S400, according to the prediction model of charge amplifier storage life, obtains charge amplifier storage life.
By the failure threshold of the characteristic parameter of charge amplifier storage life and the feature of charge amplifier storage life
The initial value of parameter is substituted into the expression formula of the prediction model of charge amplifier storage life, obtains the charge amplifier storage longevity
Life.
A kind of storage medium, is stored thereon with computer program, which realizes the above method when being executed by processor
Step.
A kind of computer equipment, including memory, processor and storage can be run on a memory and on a processor
The step of computer program, processor realizes the above method when performing the program.
Above-mentioned charge amplifier Storage Life Prediction method, storage medium and computer equipment, obtain electric charge amplification first
The corresponding characteristic sequence of characteristic parameter and characteristic parameter of device storage life, as characteristic sequence monotone decreasing and spy
It is special using characteristic sequence as modeling when characteristic data value in sign data sequence is more than the default failure threshold of characteristic parameter
Data sequence is levied, according to Modelling feature data sequence, is solved based on time response function, obtains charge amplifier storage life
Prediction model, according to the prediction model of charge amplifier storage life, obtains charge amplifier storage life, by characterizing electric charge
The characteristic parameter of amplifier life characteristics, obtains Modelling feature data sequence, based on time response function, and then obtains electric charge and puts
The prediction model of big device storage life, with calculated charge amplifier storage life, so solves charge amplifier storage life
The problem of unpredictable, establish the Storage Life Prediction model of charge amplifier, can realize the storage longevity to charge amplifier
Life is predicted.
In one embodiment, according to Modelling feature data sequence, base in the Forecasting Methodology of charge amplifier storage life
Further included after the step of time response function solves, obtains the prediction model of charge amplifier storage life：
Calculation formula based on average relative error, precision school is carried out to the prediction model of charge amplifier storage life
Test.
Average relative errorCalculation formula be：
Based on Modelling feature data sequence, precision verification, meter are carried out to the prediction model of charge amplifier storage life
ObtainError is less than default Engineering Error value 5%, and therefore, which can be used for long-term forecast.
In one embodiment, as shown in figure 4, a kind of prediction meanss of charge amplifier storage life, including：
Characteristic parameter acquisition module 100, for obtaining the characteristic parameter and characteristic parameter of charge amplifier storage life
Corresponding characteristic sequence；
Retrieval module 200 is modeled, for when the feature in characteristic sequence monotone decreasing and characteristic sequence
When data value is more than the default failure threshold of characteristic parameter, using characteristic sequence as Modelling feature data sequence；
Prediction model acquisition module 300, for according to Modelling feature data sequence, being solved, being obtained based on time response function
To the prediction model of charge amplifier storage life；
Storage life computing module 400, for the prediction model according to charge amplifier storage life, obtains electric charge amplification
Device storage life.
Above-mentioned charge amplifier Storage Life Prediction device, including characteristic parameter acquisition module 100, modeling retrieval mould
Block 200, prediction model acquisition module 300 and storage life computing module 400, obtain charge amplifier storage life first
Characteristic parameter and the corresponding characteristic sequence of characteristic parameter, when in characteristic sequence monotone decreasing and characteristic sequence
Characteristic data value when being more than the default failure threshold of characteristic parameter, using characteristic sequence as Modelling feature data sequence,
According to Modelling feature data sequence, solved based on time response function, obtain the prediction model of charge amplifier storage life, root
According to the prediction model of charge amplifier storage life, charge amplifier storage life is obtained, by characterizing the charge amplifier service life
The characteristic parameter of feature, obtains Modelling feature data sequence, based on time response function, and then obtains the charge amplifier storage longevity
The prediction model of life, with calculated charge amplifier storage life, it is unpredictable so to solve charge amplifier storage life
Problem, establishes the Storage Life Prediction model of charge amplifier, can realize and the storage life of charge amplifier is predicted.
In one embodiment, as shown in figure 5, characteristic parameter obtains mould in the prediction meanss of charge amplifier storage life
Block 100 includes：
Performance parameter acquiring unit 120, for obtaining the corresponding monitoring of each performance parameter of charge amplifier storage life
Data sequence；
Statistic acquiring unit 140, for carrying out the unbiased esti-mator of average and variance to each monitoring data sequence, obtains each
The corresponding statistic of performance parameter；
Characteristic parameter acquiring unit 160, for the corresponding statistic of each performance parameter to be carried out with default statistic respectively
Compare, obtain the characteristic parameter of charge amplifier storage life.
In one embodiment, as shown in figure 5, characteristic parameter obtains mould in the prediction meanss of charge amplifier storage life
Block 100 includes：
Monitoring data acquiring unit 180, for obtaining the corresponding characteristic monitoring data sequent of characteristic parameter；
Interpolation process unit 190, for being handled based on interpolation method characteristic monitoring data sequent, obtains characteristic
Sequence.
The prediction meanss of charge amplifier storage life are corresponded with the Forecasting Methodology of charge amplifier storage life,
The technical characteristic and its advantage illustrated in the embodiment of the Forecasting Methodology of above-mentioned charge amplifier storage life is suitable for
In the embodiment of the prediction meanss of charge amplifier storage life.
In one embodiment, a kind of Forecasting Methodology of charge amplifier storage life, first stores charge amplifier
The monitoring data of each performance parameter in service life carry out hypothesis testing to determine the characteristic parameter of charge amplifier storage life, then
Based on the characteristic parameter of charge amplifier storage life, the prediction model of charge amplifier storage life is established.
The step of carrying out hypothesis testing to the monitoring data of each performance parameter of charge amplifier storage life is as follows：
The first step, packet
The monitoring data of each performance parameter of charge amplifier storage life are grouped, by taking gain as an example, for example are increased
The monitoring data sequence of benefit is { 1.989,1.98,1.98,1.98,1.98,1.94,1.97,1.93,1.93 }, by the monitoring number
It is divided into two groups according to sequence：Monitoring data sequence { 1.989,1.98,1.98,1.98,1.98 } for the previous period and back segment time
Monitoring data sequence { 1.98,1.94,1.97,1.93,1.93 }, to the monitoring data sequence carry out the homogeneous verification of variance, knot
Fruit shows that the monitoring data sequence is homogeneous for variance.
Second step, seeks the average of data sequence and the unbiased esti-mator of variance after packet
Assuming that ξ1..., ξn1It is taken from normal state parent N (u1, σ2) increment, η1..., ηn2It is taken from normal state parent N (u2,
σ2) increment, and the two increments are separate, σ2It is unknown constant, examines null hypothesis H0:u1=u2, two printed words it is equal
Value is respectively：
The average of the two increments and the unbiased esti-mator of variance are respectively：
3rd step, constructs and solves statistic t
If null hypothesis H0:u1=u2It is true, thenRandomly swung around 0, then statistic t is：
Wherein,
It is n that can show that statistic t obeys the free degree1+n2- 2 t- distributions.
Level of significance α is provided, in H0In the case of genuine：
In above formulaIt is n according to the free degree1+n2- 2 t- distribution tables obtain.
4th step, relatively and judges | t | withRelation
When the following condition is satisfied：
Then refuse null hypothesis H0:u1=u2, that is, thinking the average of two increments has significant difference；Otherwise it is assumed that two sons
The average of sample does not have marked difference, you can to think that the two increments come from same parent.
For each performance parameter of charge amplifier storage life, ifThen inspection result refusal is former false
If H0:u1=u2, that is, think the averages of the average of the monitoring data of the performance parameter for the previous period and the monitoring data of back segment time
There were significant differences, i.e., the performance parameter has significant degeneration, and the monitoring data of the performance parameter can be used for modeling and the service life is pre-
Survey.
IfThen inspection result receives null hypothesis H0:u1=u2, that is, think the performance parameter for the previous period
Monitoring data average and back segment time monitoring data average without significant difference, i.e. the performance parameter without significantly degeneration,
Value of the performance parameter without prediction.
For the monitoring data { 1.989,1.98,1.98,1.98,1.98,1.94,1.97,1.93,1.93 } of gain, press
Above-mentioned steps solve to obtain its statistic t=0.017378, table look-upIt follows that the prison of gain
Surveying data has significant degeneration, and the characteristic parameter for having thereby determined that charge amplifier storage life is gain.
The step of establishing the prediction model of charge amplifier storage life is as follows：
The first step：The monitoring data of the characteristic parameter of definite charge amplifier storage life are pre-processed
The gain monitoring data sequence of charge amplifier is：1.989,1.98,1.98,1.98,1.98,1.94,1.97,
1.93,1.93 }, since the sequence is there are catastrophe point 1.97, based on interpolation method, data sequence after being handled 1.989,
1.98,1.98,1.98,1.98,1.94,1.935,1.93,1.93 }.
Second step：Model feasibility analysis
Gain monitoring data first to the charge amplifier after processing carry out monotonicity inspection, specifically, in the sequenceThe sequence is monotonically decreasing sequence, as shown in fig. 6, the default failure threshold of the gain of charge amplifier is 1.89,
After considering, which can be used for modeling.
3rd step：Establish the prediction model of charge amplifier storage life
It is using the gain monitoring data of the charge amplifier after processing as original series, i.e. original series：1.989,
1.98,1.98,1.98,1.98,1.94,1.935,1.93,1.93 }, according to the spy such as charge amplifier sample size is few, data volume is few
Point, binding time receptance function have the advantages that to be suitable for a small number of evidences, poor information modeling, determine using time response function as mould
Type, specifically, the formula of time response function is：
The calculation formula of above-mentioned Model Parameter a and b is respectively：
Wherein,The data in sequence are gradually summed in expression, and specifically, for example original series are：1.989,1.98,
1.98,1.98,1.98,1.94,1.935,1.93,1.93 }, then
And so on；Parameter a and b are solved based on original series, obtain a=0.0032, b=1.9849.
Thus the time response function for obtaining charge amplifier gain is：
Since the performance parameter gain requirement of charge amplifier is more than XFailure threshold, thus obtain charge amplifier storage life
Prediction model be：
Wherein, t represents the storage life of charge amplifier, XFailure thresholdRepresent the failure threshold of charge amplifier gain, XInitially
Represent the initial value of charge amplifier gain.
4th step：Test to the prediction model of charge amplifier storage life
In order to ensure the prediction model of charge amplifier storage life has higher precision of prediction, it is necessary to test, adopt
Tested with relative error test rating, average relative error is smaller, and the precision of prediction of the prediction model is higher.
Average relative errorCalculation formula be：
Wherein,Represent actual value,Represent the predicted value being calculated by charge amplifier Life Prediction Model, it is right
The prediction model of charge amplifier storage life is tested, and is obtainedError is less than default Engineering Error value
5%, therefore, which can be used for long-term forecast.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope that this specification is recorded all is considered to be.
Embodiment described above only expresses the several embodiments of the present invention, its description is more specific and detailed, but simultaneously
Cannot therefore it be construed as limiting the scope of the patent.It should be pointed out that come for those of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
- A kind of 1. Forecasting Methodology of charge amplifier storage life, it is characterised in that including：Obtain the characteristic parameter and the corresponding characteristic sequence of the characteristic parameter of charge amplifier storage life；When the characteristic data value in the characteristic sequence monotone decreasing and the characteristic sequence is joined more than the feature During several default failure threshold, using the characteristic sequence as Modelling feature data sequence；According to the Modelling feature data sequence, solved based on time response function, obtain the pre- of charge amplifier storage life Survey model；According to the prediction model of the charge amplifier storage life, charge amplifier storage life is obtained.
- 2. the Forecasting Methodology of charge amplifier storage life according to claim 1, it is characterised in that the acquisition electric charge The step of characteristic parameter of amplifier storage life, includes：Obtain the corresponding monitoring data sequence of each performance parameter of charge amplifier storage life；The unbiased esti-mator of average and variance is carried out to each monitoring data sequence, obtains the corresponding statistic of each performance parameter；The corresponding statistic of each performance parameter is obtained into the charge amplifier storage longevity compared with default statistic respectively The characteristic parameter of life.
- 3. the Forecasting Methodology of charge amplifier storage life according to claim 2, it is characterised in that it is described will be described each The corresponding statistic of performance parameter compared with default statistic, obtains the characteristic parameter of charge amplifier storage life respectively The step of include：By the corresponding statistic of each performance parameter respectively compared with default statistic, when the statistics of the performance parameter When amount is more than the default statistic of the performance parameter, join the performance parameter as the feature of charge amplifier storage life Number.
- 4. the Forecasting Methodology of charge amplifier storage life according to claim 1, it is characterised in that described in the acquisition The step of characteristic parameter corresponding characteristic sequence, includes：Obtain the corresponding characteristic monitoring data sequent of the characteristic parameter；The characteristic monitoring data sequent is handled based on interpolation method, obtains characteristic sequence.
- 5. the Forecasting Methodology of charge amplifier storage life according to claim 1, it is characterised in that described in the basis Modelling feature data sequence, is solved, the step of obtaining the prediction model of charge amplifier storage life based on time response function Including：The expression formula of settling time receptance function, the expression formula of the time response function include the first parameter and the second parameter；The calculation formula of first parameter and the calculation formula of second parameter are established, based on the Modelling feature data sequence Row, solution obtain the first parameter values and the second parameter values；According to the expression formula of first parameter values, second parameter values and the time response function, electric charge is obtained The corresponding time response function of characteristic parameter of amplifier；According to the corresponding default failure threshold of the characteristic parameter and the corresponding time response function of the characteristic parameter, electricity is obtained The prediction model of lotus amplifier storage life.
- A kind of 6. prediction meanss of charge amplifier storage life, it is characterised in that including：Characteristic parameter acquisition module, characteristic parameter and the characteristic parameter for obtaining charge amplifier storage life correspond to Characteristic sequence；Retrieval module is modeled, for when the feature in the characteristic sequence monotone decreasing and the characteristic sequence When data value is more than the default failure threshold of the characteristic parameter, using the characteristic sequence as Modelling feature data sequence Row；Prediction model acquisition module, for according to the Modelling feature data sequence, being solved based on time response function, obtaining electricity The prediction model of lotus amplifier storage life；Storage life computing module, for the prediction model according to the charge amplifier storage life, obtains charge amplifier Storage life.
- 7. the prediction meanss of charge amplifier storage life according to claim 6, it is characterised in that the characteristic parameter Acquisition module includes：Performance parameter acquiring unit, the corresponding monitoring data sequence of each performance parameter for obtaining charge amplifier storage life Row；Statistic acquiring unit, for carrying out the unbiased esti-mator of average and variance to each monitoring data sequence, obtains each property Can the corresponding statistic of parameter；Characteristic parameter acquiring unit, for the corresponding statistic of each performance parameter to be compared with default statistic respectively Compared with obtaining the characteristic parameter of charge amplifier storage life.
- 8. the prediction meanss of charge amplifier storage life according to claim 6, it is characterised in that the characteristic parameter Acquisition module includes：Monitoring data acquiring unit, for obtaining the corresponding characteristic monitoring data sequent of the characteristic parameter；Interpolation process unit, for being handled based on interpolation method the characteristic monitoring data sequent, obtains characteristic sequence Row.
- 9. a kind of computer equipment, including memory, processor and storage are on a memory and the meter that can run on a processor Calculation machine program, it is characterised in that the processor realizes any one the method in claim 1-5 when performing described program The step of.
- 10. a kind of storage medium, is stored thereon with computer program, it is characterised in that real when described program is executed by processor In existing claim 1-5 the step of any one the method.
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| CN109146307A (en) * | 2018-09-03 | 2019-01-04 | 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 nature storehouse storage life characteristic parameter appraisal procedure |
| CN109165790A (en) * | 2018-09-03 | 2019-01-08 | 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）） | Photoelectrical coupler natural storage life-span prediction method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146307A (en) * | 2018-09-03 | 2019-01-04 | 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 nature storehouse storage life characteristic parameter appraisal procedure |
| CN109165790A (en) * | 2018-09-03 | 2019-01-08 | 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）） | Photoelectrical coupler natural storage life-span prediction method |
| CN109325270A (en) * | 2018-09-03 | 2019-02-12 | 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）） | Magnetron natural storage life-span prediction method |
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