CN109697525A - A kind of recent life-span prediction method of batch electric energy meter multiple faults mode - Google Patents
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Abstract
The invention discloses a kind of recent life-span prediction methods of batch electric energy meter multiple faults mode.Live operation troubles electric energy meter, fault type are different, and the present invention classifies to failure electric energy meter according to fault mode, establish each fault mode classification table；Calculate the stage crash rate and accumulative failure of each fault mode of electric energy meter；Weibull fitting is carried out to single fault mode；According to goodness of fit situation, optimize part fault mode prediction result；It adds up to the stage crash rate of all fault modes；Obtain batch electric energy meter bulk life time predicted value.The present invention can shift to an earlier date rotation for electric energy meter, Risk-warning provides reference, provide technical support for the replacement of electric energy table status.
Description
Technical field
The present invention relates to electric energy meter reliability assessment field, especially a kind of batch electric energy meter multiple faults mode recent service life
Prediction technique.
Background technique
Currently, some provinces and area realize intelligent electric energy meter all standing, power information acquires entirely, live operational process
In the data such as power information, device exception information can be transmitted to electricity information acquisition system master station, these mass datas
Health status is run for evaluation electric energy meter field and provides key foundation information, makes it possible that electric energy meter realizes life prediction.
In general, using the method for batch electric energy meter entirety Weibull Distribution to the reliability number of failure electric energy meter
According to being handled, batch electric energy calendar life can be predicted, but the method is it is not intended that difference between specific fault type,
The accuracy of prediction result is to be improved.How Weibull Distribution is flexibly used, obtains more accurate prediction result, value
It obtains and further studies.
Summary of the invention
One kind is provided the technical problem to be solved by the present invention is to overcome the problems of the above-mentioned prior art based on this
The recent life-span prediction method of batch electric energy meter multiple faults mode is realized and is criticized in the following short-term by reliability field test data
Secondary electric energy meter multiple faults mode life prediction.
In order to solve the above technical problems, the technical solution adopted by the present invention are as follows: a kind of batch electric energy meter multiple faults mode is close
Phase life-span prediction method comprising following steps:
S1, classify according to fault mode to failure electric energy meter, establish each fault mode classification table；
S2, the stage crash rate and accumulative failure for calculating each fault mode of electric energy meter；
S3, Weibull fitting is carried out to single fault mode；
S4, according to goodness of fit situation, optimize part fault mode prediction result；
S5, it adds up to the stage crash rate of all fault modes；
S6, batch electric energy meter bulk life time predicted value is obtained.
The present invention is based on Weibull Function, it can get and be predicted the product electric energy meter batch service life distribution letter of accumulation in the recent period
Number.
Supplement as above-mentioned technical proposal, the step S2 the following steps are included:
S21, calculate every electric energy meter failure before use the time；
S22, statistics stage failure number and stage extant number；
S23, calculation stages crash rate and accumulative failure: stage crash rate is defined as the stage failure number except above one
Stage extant number, accumulative failure are defined as cumulative failure number divided by parent number.
Supplement as above-mentioned technical proposal, in step S21, using the time using day or the moon as the time before the failure of electric energy meter
Unit.
Supplement as above-mentioned technical proposal was the period with one month in step S22, and statistics batch electric energy meter is from pacifying
The stage failure number and stage extant number of the various fault modes of every month after dress, stage failure number are of that month electric energy meter failure
Quantity, stage extant number are the electric energy meter quantity not failed when the end of month；Wherein, the stage extant number of first month is equal to electric energy meter
Batch parent number subtracts the failure quantity of the same fault mode in first month；The stage extant number of second month is equal to first
A month stage extant number subtracts the failure number of the same fault mode in second month；The rest may be inferred.
Supplement as above-mentioned technical proposal, the step S3 the following steps are included:
1) according to successive phases-time ti, the stage failure number of certain fault mode is arranged, successively calculating parameter Xi
And Yi, wherein Xi=ln (ti), Yi=ln (ln (1/ (1-F (ti)))), tiIndicate time, F (ti) indicate certain fault mode
Stage crash rate；
2) least square method is utilized, the parameter alpha of the Weibull distribution of single fault mode is calculatedi、βi, wherein parameter alphaiWith
βiThe calculation formula of numerical value is expressed as follows:
It enablesN expression parameter XiAnd YiNumber
Amount；
Then αi=exp (A/B), βi=B；The goodness of fit
Supplement as above-mentioned technical proposal, step S4 the following steps are included:
To all fault modes, fitting effect is judged, if goodness of fit R2More than or equal to 0.9 and less than 1, then it is assumed that fitting
It works well, predicted value is fitted using Weibull；Otherwise, it is optimized by following two ways:
1) Weibull at times is carried out to certain fault mode to be fitted, obtain the predicted value of the crash rate at times λ ' of the modei
(t)；
2) the history average phase crash rate of certain fault mode is calculatedAs predicted value；
The stage crash rate for single fault mode is acquired as a result:
In formula, t indicates the time；If Weibull fitting coefficient is more than or equal to 0.9 and less than 1 certain fault mode at times, then
Crash rate λ ' is fitted using the Weibull of the fault mode last periodi(t) as the subsequent crash rate prediction of the fault mode
Value；Otherwise, the fault mode history average phase crash rate is selectedAs predicted value.
Supplement as above-mentioned technical proposal, the step S5 add up to the stage crash rate of all fault modes,
I.e. the whole stage crash rate of batch electric energy meter is to be added up to form by the stage crash rate of same time phase different faults mode, i.e.,
Stage crash rate:
Wherein, p is the fault mode number using Weibull Distribution method, and q is using history average phase crash rate
The fault mode number of method.
The recent life-span prediction method of a kind of batch electric energy meter multiple faults mode proposed by the present invention, based on scene operation electric energy
All kinds of fault modes of table, individually carry out Weibull Distribution, and adding up to each fault mode crash rate, it is whole to obtain batch electric energy meter
Body is expected crash rate, realizes that the batch electric energy meter recent service life is precisely predicted.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of the recent life-span prediction method of batch electric energy meter multiple faults mode of the invention；
Fig. 2 is the Weibull straight line fitting result figure of four class fault modes in the embodiment of the present invention；
Fig. 3 is Weibull straight line fitting correction result figure in the embodiment of the present invention.
Specific embodiment
For the purpose of the present invention, technical solution is more clearly understood, below in conjunction with specific embodiment, and referring to attached drawing,
The present invention is described further, but does not cause any restrictions to the present invention.
In this specific embodiment, batch electric energy meter to be predicted is that certain producer puts into operation for 2010, parent electric energy meter number
It is altogether 116990, global failure rate is about 4.8% at present.Occur a certain number of failure electric energy in the process of running at present
Therefore table can predict the batch electric energy meter recent service life based on the Weibull distribution of each fault mode.
For the recent life-span prediction method of batch electric energy meter multiple faults mode, include the following steps:
S101, classify according to fault mode to failure electric energy meter.
Fault mode is divided into: storage unit, and ammeter does not work, power supply unit, takes and controls unit, metering performance, software fault,
Clock unit, communication unit, appearance failure, other failures totally 10 class establish failure modes table respectively；
S102, the stage crash rate and accumulative failure for calculating each fault mode.
It is directed to the bug list of every kind of fault mode, carries out following three step and calculates, comprising:
(1) time is used before calculating the failure of every electric energy meter, generally using day or the moon as chronomere；
(2) stage failure number and extant number are counted；It was generally the period with one month, after counting batch electric energy meter self installation
Each fault mode stage failure number (of that month failure number) of every month and stage extant number (the electric energy meter number that this end of month does not fail
Amount).Wherein, first month stage extant number is equal to the mistake that electric energy meter batch parent number subtracts same fault mode in first month
Quantity is imitated, the stage extant number of second month is equal to the failure that extant number of upper stage subtracts the same fault mode in second month
Quantity, and so on；
(3) calculation stages crash rate and accumulative failure；Stage crash rate is defined as the stage failure number except the above stage
Extant number, accumulative failure are defined as cumulative failure number divided by parent number；
As shown in table 1, each stage number of faults under the electric energy meter batch different faults mode is given.
Each stage number of faults of 1 different faults mode of table
S103, Weibull fitting is carried out to single fault mode.
Mainly include following two step:
(1) firstly, according to successive phases-time, the stage failure number of each fault mode is arranged, is successively counted
Calculate parameter XiAnd Yi, wherein Xi=ln (ti), Yi=ln (ln (1/ (1-F (ti))))；
(2) then, using least square method, the parameter alpha of the Weibull distribution of single fault mode is calculatedi、βi, wherein joining
Number αiAnd βiThe calculation formula of numerical value is expressed as follows:
It enablesN expression parameter XiAnd YiNumber
Amount；
Then αi=exp (A/B), βi=B；The goodness of fit
According to above-mentioned formula, parameter alpha is obtainedi、βiAnd R2, calculated result is as listed in table 2；
Wherein R2For the goodness of fit, the value is closer to 1, then it represents that fitting effect is better.
2 different mode fault mode Weibull Distribution parameter of table
| Storage unit | Ammeter does not work | Power supply unit | Take control unit | Metering performance | Software fault | Clock unit | Communication unit | Appearance failure | It is other | |
| α | 0.800 | 0.828 | 1.931 | 1.609 | 1.462 | 0.750 | 1.961 | 1.696 | 1.111 | 1.138 |
| β | -15.471 | -11.271 | -21.870 | -22.265 | -17.804 | -15.304 | -20.883 | -17.921 | -13.481 | - 13.954 |
| R2 | 0.800 | 0.977 | 0.596 | 0.810 | 1.462 | 0.750 | 0.759 | 0.957 | 0.989 | 0.987 |
S104, according to goodness of fit situation, optimize part fault mode prediction result.
To all fault modes, fitting effect is judged: if goodness of fit R2More than or equal to 0.9 and less than 1, then it is assumed that fitting
It works well, using Weibull Distribution predicted value；Otherwise, it can be modified by following two mode:
1) Weibull at times is carried out to certain fault mode to be fitted, obtain the predicted value of the crash rate at times λ ' of the modei
(t)；2) the history average phase crash rate of certain fault mode is calculatedAs predicted value.
The stage crash rate of each fault mode is known as a result:
Such as Weibull fitting coefficient is more than or equal to 0.9 and less than 1 the fault mode at times, then most using the fault mode
The Weibull of period is fitted crash rate λ ' afterwardsi(t) it is used as the subsequent crash rate predicted value of the fault mode；Otherwise, the event is selected
Hinder mode history average phase crash rateAs predicted value.
In this embodiment, ammeter does not work, the goodness of fit of communication unit, appearance failure, other failures is all larger than 0.9, table
Show that fitting effect is good；Remaining 6 class fault fitting effects are poor, need to optimize, should not directly adopt fitting result.
Therefore, for being suitble to the fault mode that Weibull is fitted at times, according toIt obtains
The model parameter of each fault mode, to obtain each fault mode Weibull Function, linear fit result is as shown in Figure 2；
Wherein, power supply unit, metering performance, the Weibull at times of four kinds of fault modes of software fault and clock unit are quasi-
Collaboration number is more than or equal to 0.9 and less than 1, should be using the Weibull fitting crash rate of each fault mode last period as the failure
Mode failures even rate predicted value；And for storage unit and take control unit, select each fault mode history average phase crash rate to make
For predicted value, as a result as shown in table 3, Fig. 3 and table 4.In table 3,1.95E+15 be using scientific notation numerical value, it is subsequent its
His numerical value is similar.
Table 3 is using the Weibull fitting crash rate of last period as prediction result
| Power supply unit | Metering performance | Software fault | Clock unit | |
| α | 39795 | 1.95E+15 | 2.43E+22 | 511925 |
| β | 1.4860 | 0.2292 | 0.2069 | 0.7195 |
| R2 | 0.9365 | 0.9405 | 0.9256 | 0.9890 |
Table 4 is using history average phase crash rate as prediction result
| Storage unit | Take control unit | |
| Cumulative failure number | 19 | 6 |
| Accumulative failure/% | 1.62E-2 | 5.13E-03 |
| Monthly crash rate/% | 1.96E-04 | 6.18E-05 |
| Predicted month crash rate/% | 1.96E-04 | 6.18E-05 |
The prediction crash rate value for all fault modes can be acquired as a result,.
S105: it adds up to the stage crash rate of all fault modes.
It adds up to each stage crash rate of all fault modes, obtains the whole stage crash rate of electric energy meter.Table 5 gives
Each stage (as unit of the moon) crash rate prediction result in 1 year following.
Each fault mode and whole table stage crash rate prediction result in table 5 is 1 year following
S106: batch electric energy meter bulk life time predicted value is obtained.
Forthcoming generations crash rate predicted value is obtained, and then can get batch electric energy meter and integrally accumulate life prediction value.Such as table 6
It is shown.
Each stage accumulative failure predicted value in table 6 is 1 year following
| The △ t/ month | Accumulative failure predicted value/% |
| 1 | 4.93 |
| 2 | 5.04 |
| 3 | 5.14 |
| 4 | 5.24 |
| 5 | 5.34 |
| 6 | 5.43 |
| 7 | 5.52 |
| 8 | 5.61 |
| 9 | 5.71 |
| 10 | 5.80 |
| 11 | 5.89 |
| 12 | 5.98 |
According to prediction result table 5, table 6 it is found that after 1 year, the expected moon crash rate of the batch electric energy meter is about 0.0787%,
Accumulative failure is about 5.98%.
One embodiment of the present invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Limitation of the scope of the invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art,
Without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection model of the invention
It encloses.
Claims (7)
1. a kind of recent life-span prediction method of batch electric energy meter multiple faults mode, which comprises the following steps:
S1, classify according to fault mode to failure electric energy meter, establish each fault mode classification table；
S2, the stage crash rate and accumulative failure for calculating each fault mode of electric energy meter；
S3, Weibull fitting is carried out to single fault mode；
S4, according to goodness of fit situation, optimize part fault mode prediction result；
S5, it adds up to the stage crash rate of all fault modes；
S6, batch electric energy meter bulk life time predicted value is obtained.
2. the recent life-span prediction method of batch electric energy meter multiple faults mode according to claim 1, which is characterized in that described
Step S2 the following steps are included:
S21, calculate every electric energy meter failure before use the time；
S22, statistics stage failure number and stage extant number；
S23, calculation stages crash rate and accumulative failure: stage crash rate is defined as the stage failure number except the above stage
Extant number, accumulative failure are defined as cumulative failure number divided by parent number.
3. the recent life-span prediction method of batch electric energy meter multiple faults mode according to claim 2, which is characterized in that step
In S21, using the time using day or the moon as chronomere before the failure of electric energy meter.
4. the recent life-span prediction method of batch electric energy meter multiple faults mode according to claim 2, which is characterized in that step
It was the period with one month in S22, the stage failure number of the various fault modes of every month after statistics batch electric energy meter self installation
With stage extant number, stage failure number is of that month electric energy meter failure quantity, and stage extant number is the electric energy not failed when the end of month
Table quantity；Wherein, the stage extant number of first month subtracts the same failure in first month equal to electric energy meter batch parent number
The failure quantity of mode；The stage extant number of second month subtracts same in second month equal to first month stage extant number
The failure quantity of fault mode；The rest may be inferred.
5. the recent life-span prediction method of batch electric energy meter multiple faults mode according to claim 1, which is characterized in that described
Step S3 the following steps are included:
1) according to successive phases-time, the stage failure number of certain fault mode is arranged, successively calculating parameter XiAnd Yi,
Wherein Xi=ln (ti), Yi=ln (ln (1/ (1-F (ti)))), tiIndicate time, F (ti) indicate that the stage of certain fault mode loses
Efficiency；
2) least square method is utilized, the parameter alpha of the Weibull distribution of single fault mode is calculatedi、βi, wherein parameter alphaiAnd βiNumerical value
Calculation formula be expressed as follows:
It enablesN expression parameter XiAnd YiQuantity；
Then αi=exp (A/B), βi=B；The goodness of fit
6. the recent life-span prediction method of batch electric energy meter multiple faults mode according to claim 5, which is characterized in that step
S4 the following steps are included:
To all fault modes, fitting effect is judged, if goodness of fit R2More than or equal to 0.9 and less than 1, then it is assumed that fitting effect
Well, predicted value is fitted using Weibull；Otherwise, it is optimized by following two ways:
1) Weibull at times is carried out to certain fault mode to be fitted, obtain the predicted value of the crash rate at times λ ' of the modei(t)；
2) the history average phase crash rate of certain fault mode is calculatedAs predicted value；
The stage crash rate for single fault mode is acquired as a result:
In formula, t indicates the time；If Weibull fitting coefficient is more than or equal to 0.9 and less than 1 certain fault mode at times, then use
The Weibull of the fault mode last period is fitted crash rate λ 'i(t) it is used as the subsequent crash rate predicted value of the fault mode；It is no
Then, the fault mode history average phase crash rate is selectedAs predicted value.
7. the recent life-span prediction method of batch electric energy meter multiple faults mode according to claim 6, which is characterized in that described
Step S5 adds up to the stage crash rate of all fault modes, i.e. the whole stage crash rate of batch electric energy meter is by same a period of time
Between stage different faults mode stage crash rate it is cumulative form, i.e. stage crash rate:
Wherein, p is the fault mode number using Weibull Distribution method, and q is using history average phase crash rate method
Fault mode number.
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| CN110261811A (en) * | 2019-07-05 | 2019-09-20 | 北京志翔科技股份有限公司 | Intelligent electric meter batch method for early warning and system |
| CN110738346A (en) * | 2019-08-28 | 2020-01-31 | 国网浙江省电力有限公司 | batch electric energy meter reliability prediction method based on Weibull distribution |
| WO2021098246A1 (en) * | 2019-11-19 | 2021-05-27 | 河南许继仪表有限公司 | Electric energy meter service life prediction method and apparatus, and storage medium |
| CN111542218A (en) * | 2020-04-23 | 2020-08-14 | 国网浙江省电力有限公司电力科学研究院 | Electric energy meter credible production patch link acquisition verification method and system |
| CN113610266A (en) * | 2021-06-25 | 2021-11-05 | 东风本田发动机有限公司 | Method and device for predicting failure of automobile part, computer device and storage medium |
| CN115526369A (en) * | 2021-06-25 | 2022-12-27 | 东风本田发动机有限公司 | Failure prediction method and device for automobile parts, computer equipment and storage medium |
| CN115291157A (en) * | 2022-07-14 | 2022-11-04 | 国网山东省电力公司营销服务中心(计量中心) | Electric energy meter residual life prediction method and system based on clock deviation |
| CN115291157B (en) * | 2022-07-14 | 2023-09-22 | 国网山东省电力公司营销服务中心(计量中心) | Electric energy meter residual life prediction method and system based on clock deviation |
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Application publication date: 20190430 |