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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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 invention relates to the technical field of electronic device detection, in particular to a charge amplifier storage life prediction method, a charge amplifier storage life prediction device, a charge amplifier storage life prediction storage medium and computer equipment.
Background
With the development of science and technology, the popularization rate of electronic equipment is higher and higher, and the storage life is an important index of the electronic equipment. The charge amplifier is used as an indispensable signal adapter, can convert a weak charge signal output by the sensor into an amplified voltage signal, can convert a high-impedance output of the sensor into a low-impedance output, and can prevent a power supply from being short-circuited, so that the charge amplifier is widely applied to the field of weak signal detection.
The charge amplifier is a series link of a test system, and the failure of the charge amplifier directly affects the failure of the whole system, so that the research on the storage life of the charge amplifier is very important. Conventionally, failure life data of a product are obtained through an accelerated life test, and a life prediction model is further established, but within limited accelerated test time, failure data of a charge amplifier are difficult to obtain, and often no failure data exists, so that the life prediction model cannot be established for the charge amplifier.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a charge amplifier storage life prediction method, a charge amplifier storage life prediction apparatus, a storage medium, and a computer device that can predict the storage life of a charge amplifier.
A method for predicting the storage life of a charge amplifier, comprising:
acquiring characteristic parameters of the storage life of the charge amplifier and a characteristic data sequence corresponding to the characteristic parameters;
when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than the preset failure threshold value of the characteristic parameter, taking the characteristic data sequence as a modeling characteristic data sequence;
solving based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier;
and obtaining the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier.
An apparatus for predicting the storage life of a charge amplifier, comprising:
the characteristic parameter acquisition module is used for acquiring the characteristic parameters of the storage life of the charge amplifier and the characteristic data sequence corresponding to the characteristic parameters;
the modeling sequence acquisition module is used for taking the characteristic data sequence as a modeling characteristic data sequence when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than a preset failure threshold value of the characteristic parameter;
the prediction model acquisition module is used for solving based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier;
and the storage life calculation module is used for obtaining the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier.
A storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the above-mentioned method.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the steps of the method being performed when the program is executed by the processor.
The method, the device, the storage medium and the computer equipment for predicting the storage life of the charge amplifier firstly determine the characteristic parameters of the storage life of the charge amplifier, acquire the characteristic data sequence corresponding to the characteristic parameters, when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than the preset failure threshold value of the characteristic parameters, use the characteristic data sequence as a modeling characteristic data sequence, solve based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier, obtain the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier, obtain the modeling characteristic data sequence by characterizing the characteristic parameters of the life characteristics of the charge amplifier, further obtain the prediction model of the storage life of the charge amplifier based on the time response function to calculate the storage life of the charge amplifier, therefore, the problem that the storage life of the charge amplifier cannot be predicted is solved, a storage life prediction model of the charge amplifier is established, and the storage life of the charge amplifier can be predicted.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting charge amplifier storage life in one embodiment;
FIG. 2 is a schematic flow chart diagram illustrating a method for predicting charge amplifier storage life in one embodiment;
FIG. 3 is a schematic flow chart of a method for predicting charge amplifier storage life in one embodiment;
FIG. 4 is a schematic diagram of an embodiment of a device for predicting charge amplifier storage life;
FIG. 5 is a schematic diagram of an embodiment of an apparatus for predicting charge amplifier storage life;
fig. 6 is a data trace of the gain of a charge amplifier in one embodiment.
Detailed Description
As shown in fig. 1, a method for predicting the storage life of a charge amplifier includes:
and S100, acquiring characteristic parameters of the storage life of the charge amplifier and a characteristic data sequence corresponding to the characteristic parameters.
The charge amplifier comprises a charge conversion stage, an adaptive stage, a low-pass filter, a high-pass filter, a final power amplifier, a power supply and the like, and performance parameters of the storage life of the charge amplifier comprise frequency, noise performance, frequency response and gain.
In one embodiment, as shown in fig. 2, the step of obtaining the characteristic parameter of the storage life of the charge amplifier may specifically include: s120, acquiring a monitoring data sequence corresponding to each performance parameter of the storage life of the charge amplifier; s140, carrying out unbiased estimation on the mean value and the variance of each monitoring data sequence to obtain statistic corresponding to each performance parameter; and S160, comparing the statistics corresponding to the performance parameters with preset statistics respectively to obtain the characteristic parameters of the storage life of the charge amplifier.
The performance parameters of the storage life of the charge amplifier comprise frequency, noise performance, frequency response and gain, monitoring data corresponding to the performance parameters are obtained, and monitoring data sequences are correspondingly formed respectively. And carrying out unbiased estimation on the mean value and the variance of each monitoring data sequence to obtain statistic corresponding to each performance parameter, comparing the statistic corresponding to each performance parameter with preset statistic, and specifically, when the statistic of a certain performance parameter is larger than the preset statistic of the performance parameter, taking the performance parameter as the characteristic parameter of the storage life of the charge amplifier. Taking a gain monitoring data sequence as an example, obtaining unbiased estimation of a mean value and a variance of the gain monitoring data sequence, further obtaining statistic corresponding to the gain of the charge amplifier, comparing the calculated statistic with preset statistic, when the calculated statistic is greater than or equal to the preset statistic, showing that the gain has obvious degradation, respectively carrying out the same treatment on other performance parameters, and taking the performance parameters with obvious degradation as characteristic parameters of the storage life of the charge amplifier.
In an embodiment, as shown in fig. 2, the step of acquiring the feature data sequence corresponding to the feature parameter may specifically include: s180, acquiring a characteristic data monitoring sequence corresponding to the characteristic parameters; and S190, processing the characteristic data monitoring sequence based on an interpolation method to obtain a characteristic data sequence.
After determining the characteristic parameter of the charge amplifier, acquiring a characteristic data monitoring sequence corresponding to the characteristic parameter, for example, the gain monitoring data sequence of the charge amplifier is as follows: {1.989, 1.98, 1.98, 1.98, 1.98, 1.94, 1.97, 1.93, 1.93}, it can be seen that there are mutation points 1.97 in the sequence, and the characteristic data sequence is obtained by processing the sequence based on interpolation: {1.989,1.98,1.98,1.98,1.98,1.94,1.935,1.93,1.93}. The interpolation-based processing of the sequence is to know the overall trend of the sequence, and an estimation value is used for replacing a mutation value through the former data and the latter data.
And S200, when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than the preset failure threshold value of the characteristic parameter, taking the characteristic data sequence as a modeling characteristic data sequence.
The characteristic data sequence is subjected to monotonicity test, such as the characteristic data sequence {1.989, 1.98, 1.98, 1.98, 1.98, 1.98, 1.94, 1.935, 1.93 and 1.93} of the charge amplifier, and is shown to be monotonously decreased, and each monitored data value is greater than a preset failure threshold value 1.89 of the gain, so that the sequence can be used as a modeling characteristic data sequence.
And S300, solving based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier.
The time response function has the advantage of being suitable for modeling with less data and poor information, and the step of solving based on the time response function according to the modeling characteristic data sequence to obtain the prediction model of the storage life of the charge amplifier can specifically include, as shown in fig. 3: s320, establishing an expression of a time response function, wherein the expression of the time response function comprises a first parameter and a second parameter; s340, establishing a calculation formula of a first parameter and a calculation formula of a second parameter, and solving to obtain a first parameter value and a second parameter value based on the modeling characteristic data sequence; s360, obtaining a time response function corresponding to the characteristic parameter of the charge amplifier according to the first parameter value, the second parameter value and the expression of the time response function; and S380, obtaining a prediction model of the storage life of the charge amplifier according to the preset failure threshold value corresponding to the characteristic parameter and the time response function corresponding to the characteristic parameter.
The expression of the time response function is:
the calculation formula of the first parameter a is as follows:
the calculation formula of the second parameter b is as follows:
based on the modeling feature data sequence, a first parameter value and a second parameter value are obtained through solving, specifically, a is 0.0032, and b is 1.9849.
The time response function corresponding to the characteristic parameter of the charge amplifier is obtained as follows:
according to the preset failure threshold value 1.89 corresponding to the characteristic parameter and the time response function corresponding to the characteristic parameter, a prediction model of the storage life of the charge amplifier is obtained as follows:
wherein t represents the storage life of the charge amplifier, XFailure thresholdFailure threshold of a characteristic parameter representing the storage life of a charge amplifier, XInitialAn initial value of a characteristic parameter representing the storage life of the charge amplifier.
S400, obtaining the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier.
And substituting the failure threshold value of the characteristic parameter of the storage life of the charge amplifier and the initial value of the characteristic parameter of the storage life of the charge amplifier into an expression of a prediction model of the storage life of the charge amplifier to obtain the storage life of the charge amplifier.
A storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the above-mentioned method.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the steps of the method being performed when the program is executed by the processor.
The method for predicting the storage life of the charge amplifier, the storage medium and the computer device firstly acquire the characteristic parameters of the storage life of the charge amplifier and the characteristic data sequence corresponding to the characteristic parameters, when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than the preset failure threshold value of the characteristic parameters, the characteristic data sequence is taken as a modeling characteristic data sequence, the model is solved based on the time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier, the storage life of the charge amplifier is obtained according to the prediction model of the storage life of the charge amplifier, the modeling characteristic data sequence is obtained by characterizing the characteristic parameters of the life characteristics of the charge amplifier, the prediction model of the storage life of the charge amplifier is further obtained based on the time response function to calculate the storage life of the charge amplifier, therefore, the problem that the storage life of the charge amplifier cannot be predicted is solved, a storage life prediction model of the charge amplifier is established, and the storage life of the charge amplifier can be predicted.
In one embodiment, the step of solving based on the time response function according to the modeling feature data sequence to obtain the prediction model of the storage life of the charge amplifier in the method for predicting the storage life of the charge amplifier further includes:
and carrying out accuracy verification on the prediction model of the storage life of the charge amplifier based on a calculation formula of the average relative error.
Average relative errorThe calculation formula of (2) is as follows:
based on the modeling characteristic data sequence, the accuracy of the prediction model of the storage life of the charge amplifier is verified, and the prediction model is obtained through calculationThe error is less than 5% of the preset engineering error value, so the prediction model can be used for long-term prediction.
In one embodiment, as shown in fig. 4, an apparatus for predicting storage life of a charge amplifier includes:
a characteristic parameter obtaining module 100, configured to obtain a characteristic parameter of the storage life of the charge amplifier and a characteristic data sequence corresponding to the characteristic parameter;
a modeling sequence obtaining module 200, configured to take the feature data sequence as a modeling feature data sequence when the feature data sequence monotonically decreases and a feature data value in the feature data sequence is greater than a preset failure threshold of the feature parameter;
the prediction model obtaining module 300 is configured to solve based on a time response function according to the modeling feature data sequence to obtain a prediction model of the storage life of the charge amplifier;
and a storage life calculation module 400, configured to obtain the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier.
The charge amplifier storage life prediction device comprises a characteristic parameter acquisition module 100, a modeling sequence acquisition module 200, a prediction model acquisition module 300 and a storage life calculation module 400, wherein the characteristic parameters of the charge amplifier storage life and a characteristic data sequence corresponding to the characteristic parameters are firstly acquired, when the characteristic data sequence monotonically decreases and the characteristic data value in the characteristic data sequence is greater than a preset failure threshold value of the characteristic parameters, the characteristic data sequence is used as the modeling characteristic data sequence, the model is solved based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the charge amplifier storage life, the charge amplifier storage life is obtained according to the prediction model of the charge amplifier storage life, the modeling characteristic data sequence is obtained by characterizing the characteristic parameters of the charge amplifier life, and the model is based on the time response function, and then a prediction model of the storage life of the charge amplifier is obtained to calculate the storage life of the charge amplifier, so that the problem that the storage life of the charge amplifier cannot be predicted is solved, the storage life prediction model of the charge amplifier is established, and the storage life of the charge amplifier can be predicted.
In one embodiment, as shown in fig. 5, the characteristic parameter obtaining module 100 in the apparatus for predicting the storage life of a charge amplifier includes:
a performance parameter acquiring unit 120, configured to acquire a monitoring data sequence corresponding to each performance parameter of the storage life of the charge amplifier;
a statistic acquisition unit 140, configured to perform unbiased estimation on the mean and the variance of each monitored data sequence to obtain statistics corresponding to each performance parameter;
the characteristic parameter obtaining unit 160 is configured to compare statistics corresponding to each performance parameter with preset statistics, respectively, to obtain a characteristic parameter of the storage life of the charge amplifier.
In one embodiment, as shown in fig. 5, the characteristic parameter obtaining module 100 in the apparatus for predicting the storage life of a charge amplifier includes:
a monitoring data obtaining unit 180, configured to obtain a characteristic data monitoring sequence corresponding to the characteristic parameter;
and the interpolation processing unit 190 is configured to process the characteristic data monitoring sequence based on an interpolation method to obtain a characteristic data sequence.
The device for predicting the storage life of the charge amplifier and the method for predicting the storage life of the charge amplifier correspond to each other, and the technical features and the advantages thereof described in the embodiment of the method for predicting the storage life of the charge amplifier are all applied to the embodiment of the device for predicting the storage life of the charge amplifier.
In one embodiment, a method for predicting the storage life of a charge amplifier includes performing a hypothesis test on monitored data of performance parameters of the storage life of the charge amplifier to determine characteristic parameters of the storage life of the charge amplifier, and then building a prediction model of the storage life of the charge amplifier based on the characteristic parameters of the storage life of the charge amplifier.
The steps of performing hypothesis testing on the monitoring data of each performance parameter of the storage life of the charge amplifier are as follows:
first, grouping
The monitoring data of each performance parameter of the storage life of the charge amplifier are grouped, for example, the monitoring data sequence of the gain is {1.989, 1.98, 1.98, 1.98, 1.98, 1.94, 1.97, 1.93, 1.93}, and the monitoring data sequence is divided into two groups: and performing variance homogeneous check on the monitoring data sequence {1.989, 1.98, 1.98, 1.98, 1.98} of the previous period and {1.98, 1.94, 1.97, 1.93, 1.93} of the monitoring data sequence {1.98, 1.98, 1.98} of the later period, wherein the results show that the monitoring data sequence is variance homogeneous.
Second, an unbiased estimate of the mean and variance of the grouped data sequences is solved
Suppose ξ1，…，ξn1Is taken from the normal parent N (u)1，σ2) Sub-sample of (8), η1，…，ηn2Is taken from the normal parent N (u)2，σ2) And the two subsamples are independent of each other, σ2Is an unknown constant, examines the original hypothesis H0:u1＝u2The mean values of the two typefaces are respectively:
unbiased estimates of the mean and variance of these two subsamples are:
thirdly, constructing and solving statistic t
If the original hypothesis H0:u1＝u2Is true, thenRandomly wobbles around 0, so the statistic t is:
wherein,
the statistical quantity t obeys a degree of freedom n1+n2-a t-distribution of 2.
Giving a significance level of α, at H0In the case of true:
in the above formulaAccording to a degree of freedom of n1+n2And (3) obtaining a t-distribution table of-2.
The fourth step, compare and judge | t | andrelationships between
When the following conditions are satisfied:
then the original hypothesis H is rejected0:u1＝u2That is, the mean values of the two subsamples are considered to have significant difference; otherwise, the mean values of the two subsamples are considered to be not significantly different, i.e., the two subsamples can be considered to be from the same parent.
For each performance parameter of the storage life of the charge amplifier, ifThe test result rejects the original hypothesis H0:u1＝u2That is, the mean value of the monitoring data of the performance parameter at the front period is considered to have a significant difference from the mean value of the monitoring data of the performance parameter at the rear period, that is, the performance parameter has significant degradation, and the monitoring data of the performance parameter can be used for modeling and life prediction.
If it isThe test result accepts the original hypothesis H0:u1＝u2That is, the mean value of the monitoring data of the performance parameter at the front period is considered to have no significant difference from the mean value of the monitoring data of the performance parameter at the rear period, that is, the performance parameter has no significant degradation, and the performance parameter has no prediction value.
For the gain monitoring data {1.989, 1.98, 1.98, 1.98, 1.98, 1.98, 1.94, 1.97, 1.93, 1.93}, solving the above steps to obtain a statistic t-0.017378, and obtaining by table lookupTherefore, the monitoring data of the gain is obviously degraded, and the characteristic parameter of the storage life of the charge amplifier is determined to be the gain.
The steps of establishing a predictive model of charge amplifier storage life are as follows:
the first step is as follows: preprocessing the monitoring data of the characteristic parameters of the determined storage life of the charge amplifier
The charge amplifier gain monitor data sequence is: {1.989, 1.98, 1.98, 1.98, 1.98, 1.94, 1.97, 1.93, 1.93}, the processed data sequence {1.989, 1.98, 1.98, 1.98, 1.98, 1.94, 1.935, 1.93, 1.93} is obtained based on interpolation due to the presence of mutation points 1.97 in the sequence.
The second step is that: modeling feasibility analysis
Firstly, gain monitoring data of the processed charge amplifier is inputLine monotonicity test, in particular, in the sequenceThe sequence is a monotonically decreasing sequence, as shown in fig. 6, with a preset failure threshold of the gain of the charge amplifier of 1.89, and after comprehensive consideration, the sequence can be used for modeling.
The third step: establishing a prediction model of charge amplifier storage life
Taking the processed gain monitoring data of the charge amplifier as an original sequence, namely the original sequence is as follows: {1.989, 1.98, 1.98, 1.98, 1.98, 1.94, 1.935, 1.93, 1.93}, according to the characteristics of small sample size and small data size of the charge amplifier, in combination with the advantage that the time response function has the suitability for modeling with less data and poor information, determining the time response function as a model, specifically, the formula of the time response function is as follows:
the calculation formulas of the parameters a and b in the model are respectively as follows:
wherein,it represents the successive summation of data in a sequence, specifically, for example, the original sequence is: {1.989, 1.98, 1.98, 1.98, 1.98, 1.98, 1.94, 1.935, 1.93, 1.93}, then And so on; solving the parameters a and b based on the original sequence yields a 0.0032 and b 1.9849.
The time response function of the gain of the charge amplifier is thus obtained as:
gain requirement is greater than X due to performance parameter of charge amplifierFailure thresholdThe prediction model for the storage life of the charge amplifier is obtained by:
wherein t represents the storage life of the charge amplifier, XFailure thresholdIndicating the failure threshold of the charge amplifier gain, XInitialRepresenting an initial value of the charge amplifier gain.
The fourth step: testing a model for predicting charge amplifier storage life
In order to ensure that the prediction model of the storage life of the charge amplifier has higher prediction accuracy, the test is carried out by using a relative error test index, and the prediction accuracy of the prediction model is higher when the average relative error is smaller.
Average relative errorThe calculation formula of (2) is as follows:
wherein,the actual value is represented by a value that is,the prediction model of the storage life of the charge amplifier is checked to obtain a prediction value calculated by the charge amplifier life prediction modelThe error is less than 5% of the preset engineering error value, so the prediction model can be used for long-term prediction.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for predicting the storage life of a charge amplifier, comprising:
acquiring characteristic parameters of the storage life of the charge amplifier and a characteristic data sequence corresponding to the characteristic parameters;
when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than the preset failure threshold value of the characteristic parameter, taking the characteristic data sequence as a modeling characteristic data sequence;
solving based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier;
and obtaining the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier.
2. The method of claim 1, wherein the step of obtaining the characteristic parameters of the storage life of the charge amplifier comprises:
acquiring a monitoring data sequence corresponding to each performance parameter of the storage life of the charge amplifier;
carrying out unbiased estimation on the mean value and the variance of each monitoring data sequence to obtain statistic corresponding to each performance parameter;
and comparing the statistics corresponding to the performance parameters with preset statistics to obtain the characteristic parameters of the storage life of the charge amplifier.
3. The method for predicting the storage life of the charge amplifier as claimed in claim 2, wherein the step of comparing the statistics corresponding to the performance parameters with preset statistics to obtain the characteristic parameters of the storage life of the charge amplifier comprises:
and comparing the statistics corresponding to the performance parameters with preset statistics respectively, and taking the performance parameters as the characteristic parameters of the storage life of the charge amplifier when the statistics of the performance parameters are larger than the preset statistics of the performance parameters.
4. The method for predicting the storage life of the charge amplifier as claimed in claim 1, wherein the step of obtaining the characteristic data sequence corresponding to the characteristic parameter comprises:
acquiring a characteristic data monitoring sequence corresponding to the characteristic parameter;
and processing the characteristic data monitoring sequence based on an interpolation method to obtain a characteristic data sequence.
5. The method of claim 1, wherein the step of solving based on the time response function from the modeled characteristic data series to obtain a predictive model of charge amplifier storage life comprises:
establishing an expression of a time response function, wherein the expression of the time response function comprises a first parameter and a second parameter;
establishing a calculation formula of the first parameter and a calculation formula of the second parameter, and solving to obtain a first parameter value and a second parameter value based on the modeling characteristic data sequence;
obtaining a time response function corresponding to the characteristic parameter of the charge amplifier according to the first parameter value, the second parameter value and the expression of the time response function;
and obtaining a prediction model of the storage life of the charge amplifier according to the preset failure threshold value corresponding to the characteristic parameter and the time response function corresponding to the characteristic parameter.
6. An apparatus for predicting a storage life of a charge amplifier, comprising:
the characteristic parameter acquisition module is used for acquiring the characteristic parameters of the storage life of the charge amplifier and the characteristic data sequence corresponding to the characteristic parameters;
the modeling sequence acquisition module is used for taking the characteristic data sequence as a modeling characteristic data sequence when the characteristic data sequence is monotonically decreased and the characteristic data value in the characteristic data sequence is greater than the preset failure threshold value of the characteristic parameter;
the prediction model acquisition module is used for solving based on a time response function according to the modeling characteristic data sequence to obtain a prediction model of the storage life of the charge amplifier;
and the storage life calculation module is used for obtaining the storage life of the charge amplifier according to the prediction model of the storage life of the charge amplifier.
7. The apparatus for predicting storage life of a charge amplifier according to claim 6, wherein the characteristic parameter obtaining module comprises:
the performance parameter acquisition unit is used for acquiring monitoring data sequences corresponding to various performance parameters of the storage life of the charge amplifier;
a statistic acquisition unit, configured to perform unbiased estimation on the mean and the variance of each monitored data sequence to obtain statistics corresponding to each performance parameter;
and the characteristic parameter acquisition unit is used for comparing the statistics corresponding to the performance parameters with preset statistics respectively to obtain the characteristic parameters of the storage life of the charge amplifier.
8. The apparatus for predicting storage life of a charge amplifier according to claim 6, wherein the characteristic parameter obtaining module comprises:
the monitoring data acquisition unit is used for acquiring a characteristic data monitoring sequence corresponding to the characteristic parameter;
and the interpolation processing unit is used for processing the characteristic data monitoring sequence based on an interpolation method to obtain a characteristic data sequence.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-5 are implemented when the program is executed by the processor.
10. A storage medium on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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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 |
| 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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103630343A (en) * | 2013-10-22 | 2014-03-12 | 航天科工防御技术研究试验中心 | Accelerating test method for shelf lives of vibration isolators |
| CN105849798A (en) * | 2013-12-25 | 2016-08-10 | Eizo株式会社 | Life prediction method, life prediction program, and life prediction device |
-
2017
- 2017-11-08 CN CN201711108210.2A patent/CN107918704A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103630343A (en) * | 2013-10-22 | 2014-03-12 | 航天科工防御技术研究试验中心 | Accelerating test method for shelf lives of vibration isolators |
| CN105849798A (en) * | 2013-12-25 | 2016-08-10 | Eizo株式会社 | Life prediction method, life prediction program, and life prediction device |
Non-Patent Citations (5)
| Title |
|---|
| JIKUN YANG 等: "The storage life prediction and simulation system development of solid rocket motor", 《2012 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING》 * |
| 张子华 等: "某型固体继电器储存性能退化规律研究", 《装备环境工程》 * |
| 张济世 等: "《统计水文学》", 31 March 2006, 黄河水利出版社 * |
| 李坤兰 等: "某型混合集成电路长期贮存寿命研究", 《装备环境工程》 * |
| 李坤兰: "某种有机薄膜电容器长期贮存寿命预测研究", 《装备环境工程》 * |
Cited By (3)
| 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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