CN110146840A - A kind of recent life-span prediction method of batch electric energy meter based on more stress influences - Google Patents
A kind of recent life-span prediction method of batch electric energy meter based on more stress influences Download PDFInfo
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Abstract
The invention discloses a kind of recent life-span prediction method of batch electric energy meter based on more stress influences.Live operation troubles electric energy meter, the differently stressed influence degree of different faults mode are different.The present invention is based on Weibull fittings, utilize the recent life-span prediction method of multiple faults mode, obtain the crash rate predicted value of each fault mode, and then stress types are influenced for main, establish the model influenced between stress and each fault mode crash rate, and calculating influence coefficient obtains the recent service life of whole electric energy meter by adjusting the forecast period crash rate of each fault mode after adding up.The present invention considers influence of the different stress to each fault mode of electric energy meter, and is based on the recent life-span prediction method of multiple faults mode using the influence coefficient adjustment of quantization, to more precisely predict the electric energy meter recent service life.
Description
Technical field
The present invention relates to electric energy meter reliability assessment field, especially a kind of batch electric energy meter based on more stress influences is close
Phase life-span prediction method.
Background technique
Currently, intelligent electric energy meter covers the whole network substantially, power information, device exception information in live operational process and
The data such as the assets information of intelligent electric energy meter can real-time Transmission to each net save marketing system or power information acquisition system, this
A little mass datas provide key foundation information to judge intelligent electric energy meter scene operation health status, and electric energy meter is made to realize the service life
Prediction is possibly realized.
In general, the batch electric energy meter recent service life is predicted, there are two types of implementation methods: first is that such as a kind of " batch
The recent life-span prediction method of electric energy meter " (Chinese Patent Application No.: 201811484818.X) " is described, utilizes batch electric energy meter
Field failure data are analyzed and processed fault data using whole Weibull Distribution method, realize batch electric energy meter
Life prediction；Second is that as " a kind of recent life-span prediction method of batch electric energy meter multiple faults mode " (Chinese Patent Application No.:
It is 201811484825.X) described, Weibull fitting is carried out to each fault mode of electric energy meter, and according to goodness of fit situation, optimization
For partial fault model prediction as a result, adding up in turn to the stage crash rate of all fault modes, acquisition batch electric energy meter is whole
Body life time predicted value.
But the above two prediction technique based on Weibull Distribution, it is all based on the scene operation whole table of electric energy meter
Or the considered repealed data of each fault mode, do not consider external combined stress to electric energy meter aging effects.However, different answers
Power type is discrepant to each failure mode effect degree of electric energy meter.Influence electric energy meter stable operation factor and it is a kind of or
A variety of stress types are related, and degenerative process is also related with stress intensity.These stress types include: temperature, humidity, salt fog,
Thunder and lightning and electric stress etc..
In consideration of it, can be by the variation characteristic of analysis stress, establishing influences between stress and each fault mode crash rate
Model, and it is based on the recent life-span prediction method of multiple faults mode using the influence coefficient adjustment of quantization, thus more precisely pre-
Survey the electric energy meter recent service life.
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 based on more stress influences passes through live reliability number and external combined stress water
It is flat, in conjunction with existing batch electric energy meter multiple faults mode life-span prediction method, to realize the batch electric energy based on more stress influences
The recent life prediction of table.
In order to solve the above technical problems, The technical solution adopted by the invention is as follows: a kind of batch based on more stress influences
The recent life-span prediction method of electric energy meter comprising following steps:
S1, the stage crash rate predicted value for obtaining each fault mode；
The main influence stress types of S2, clearly each fault mode；
S3, the history crash rate distribution situation for determining the intensity and each fault mode that influence stress；
S4, the model influenced between stress and each fault mode is established；
The influence coefficient of S5, calculation stages crash rate, and adjust the forecast period crash rate of each fault mode；
S6, it adds up to adjusted each fault mode stage crash rate, obtains the prediction of batch electric energy meter bulk life time
Value.
Further, the step S4 leads to according to each fault mode actual stage crash rate and stress intensity distributed data
The influence relationship that numerical analysis method establishes stress Yu each fault mode stage crash rate is crossed,
Further, the numerical analysis method is directly analyzed using linear dependence, establishes stress and each failure
Linear functional relation between mode phases crash rate.
Further, the numerical analysis method, by traditional stress life model, establishment stage crash rate with
Nonlinear function between stress.
Further, traditional stress life model includes: that Arrhenius (Arrhenius) model (answer by temperature
Power), Hallberg-Peck model (temperature-humidity combined stress), cumulative fatigue damage model-Miner rule (answer by electric current
Power).
Further, step S2 is according to failure analysis result and electric energy meter historical failure data statistic analysis result, really
The main influence stress types of fixed each fault mode.
Further, step S3 is according to the actual motion environment of batch electric energy meter to be predicted, obtain stress intensity and its with
Annual distribution situation；And statistically analyze electric energy meter history crash rate data.
Further, the step S5 is answered according to what the stress intensity level distribution and step S4 of forecast period obtained
The model of power and each fault mode stage crash rate obtains jth kind stress types to the stage crash rate of i-th kind of fault mode
Influence coefficient kij, the stage crash rate λ of i-th kind of fault mode after adjustmentiIt is expressed asWherein, λ0iTo pass through step
I-th kind of fault mode prediction stage crash rate that rapid S1 is obtained, i value is 1,2,3 ..., N, j value is 1,2,3 ..., M, institute
Stating stress types includes temperature, humidity, thunder and lightning, salt fog and electric stress.
Further, step S6 adds up to all adjusted each fault mode stage crash rates, obtains batch
Electric energy meter bulk life time predicted value, then the stage crash rate statement of the whole table of batch electric energy meter are as follows:
The device have the advantages that as follows: the present invention is " a kind of batch electric energy meter multiple faults mode recent service life is pre-
On the basis of survey method " (Chinese Patent Application No.: 201811484825.X), it is contemplated that different stress are to each failure mould of electric energy meter
The influence of formula, and it is based on the recent life-span prediction method of multiple faults mode using the influence coefficient adjustment of quantization, thus more accurate
Predict the electric energy meter recent service life in ground.
The present invention can shift to an earlier date rotation for electric energy meter, Risk-warning provides reference, provide technology for the replacement of electric energy table status
Support.
Detailed description of the invention
Fig. 1 is a kind of stream of the recent life-span prediction method of batch electric energy meter based on more stress influences in the embodiment of the present invention
Journey schematic diagram；
Fig. 2 is the stage crash rate prognostic chart of the batch electric energy meter different faults mode and whole table in the embodiment of the present invention；
Fig. 3 is that clock unit monthly average crash rate logarithm and monthly mean temperature inverse are fitted in application examples of the present invention
Figure；
Fig. 4 is that metering performance monthly average crash rate logarithm and the logarithmic linear fitting of monthly average humidity are tied in application examples of the present invention
Fruit figure.
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.
Embodiment
The present embodiment provides a kind of recent life-span prediction method of batch electric energy meter based on more stress influences, as shown in Figure 1.
One, basic principle
Suppose there is M kind different stress types may will affect electric energy meter life expectancy, and there are N kind failures inside electric energy meter
The influence degree of the differently stressed type of mode is also different, and the difference of this influence degree was finally reflected as the electric energy meter stage
The adjustment of crash rate.
Therefore, k is definedijFor jth kind stress types (j value is 1,2,3 ..., M) to i-th kind of fault mode, (i value is
1,2,3 ..., N) stage crash rate influence coefficient, so that the stage crash rate of the whole table of batch electric energy meter can be stated are as follows:
Wherein, λWhole tableFor the stage crash rate predicted value of the whole table of batch electric energy meter, λ0A point event is based on for the batch electric energy meter
The crash rate that the Weibull Distribution prediction technique of barrier mode obtains is (in detail as " a kind of batch electric energy meter multiple faults mode is close
Phase life-span prediction method ", Chinese Patent Application No.: described in 201811484825.X).
It is generally believed that working as kijWhen value is 1, the stress types are represented to the stage crash rate of this fault mode without obvious shadow
It rings.
Therefore, a kind of recent life-span prediction method of batch electric energy meter based on more stress influences of the present invention, the key of realization
Process is:
(1) the main influence stress types of clear each fault mode of batch electric energy meter；
(2) main stress types intensity distribution under clear live actual environment；
(3) model influenced between stress and each fault mode stage crash rate is established, calculating influences stress to each failure
The influence coefficient of mode phases crash rate.
Two, prediction steps
In consideration of it, a kind of recent life-span prediction method of batch electric energy meter based on more stress influences, specific implementation step packet
It includes:
S1, by a kind of recent life-span prediction method of existing batch electric energy meter multiple faults mode (Chinese Patent Application No.:
201811484825.X), obtain the stage crash rate predicted value of each fault mode；
The main influence stress types of S2, clearly each fault mode；
S3, the history crash rate distribution situation for determining the intensity and each fault mode that influence stress；
S4, the model influenced between stress and each fault mode is established；
The influence coefficient of S5, calculation stages crash rate, and adjust the forecast period crash rate of each fault mode；
S6, it adds up to adjusted each fault mode stage crash rate, obtains the prediction of batch electric energy meter bulk life time
Value.
The present invention " a kind of recent life-span prediction method of batch electric energy meter multiple faults mode " (Chinese Patent Application No.:
On the basis of 201811484825.X), the model influenced between stress and each fault mode is established, the influence coefficient of quantization is used
Adjustment is based on the recent life-span prediction method of multiple faults mode, to more precisely predict the electric energy meter recent service life.
Generally, step S1 can be according to patent " a kind of recent life-span prediction method of batch electric energy meter multiple faults mode "
(Chinese Patent Application No.: 201811484825.X) described step is carried out, and details are not described herein；
Step S2 determines each failure mould according to failure analysis result and electric energy meter historical failure data statistic analysis result
The main influence stress types of formula.
Generally, based on electric energy meter internal circuit characteristics of principle and the live major failure analysis of causes, the main shadow of electric energy meter
The relationship responded between power and common possible breakdown mode is as shown in the table.
1 electric energy meter of table influences relationship between stress and each fault mode
| Serial number | Stress types | The fault mode mainly influenced | Common influence process |
| 1 | Temperature | Clock unit, metering performance | Lead to clock drift failure, measurement deviation |
| 2 | Humidity | Metering performance | Lead to electrochemical migration, parameter drift |
| 3 | Salt fog | Metering performance, power supply unit | Salt fog causes migration, corrosion to induce failure |
| 4 | Thunder and lightning | Appearance failure, communication unit | Lead to burning table, communication failure |
| 5 | Electric current, voltage | Power supply unit, communication unit | Power is big, causes overheat to burn, power-supply fluctuation etc. |
Step S3 obtains stress intensity and its is distributed feelings at any time according to the actual motion environment of batch electric energy meter to be predicted
Condition；And statistically analyze electric energy meter history crash rate data.
Typically, for the natural environments stress such as temperature, humidity, salt fog, thunder and lightning, the gas of electric energy meter installation region can be passed through
Image data obtains；For electric stress such as voltage, electric currents, can be obtained by monitoring Operating Voltage, user power utilization load level.
Step S4 is distributed according to electric energy meter comprehensive stress intensity and each fault mode actual stage crash rate data, passes through number
It is worth analysis method and establishes the model influenced between stress and each fault mode.
The numerical analysis method can be analyzed directly using linear dependence, establish stress and each fault mode stage loses
Linear functional relation between efficiency；It can also be by traditional stress life model, between establishment stage crash rate and stress
Nonlinear function.The stress life model include: Arrhenius (Arrhenius) model (temperature stress),
Hallberg-Peck model (temperature-humidity combined stress), cumulative fatigue damage model-Miner are regular (current stress).
The stress and each failure mould that step S5 is obtained according to the stress intensity level distribution and step S4 of institute's forecast period
The model of formula stage crash rate, obtain jth kind stress types (j value be 1,2,3 ..., M) to i-th kind of fault mode (i value
For 1,2,3 ..., N) stage crash rate influence coefficient kij, the stage crash rate λ of i-th kind of fault mode after adjustmentiIt can state
Are as follows:
Wherein, λ0iFor the i-th kind of fault mode prediction stage crash rate obtained by step S1, the stress types packet
It includes: temperature, humidity, thunder and lightning, salt fog and electric stress etc..
Step S6 adds up to all adjusted each fault mode stage crash rates, obtains the batch electric energy meter whole longevity
Order predicted value.Then the stage crash rate of the whole table of batch electric energy meter can be stated are as follows:
Application examples
In the application examples, batch electric energy meter to be predicted is that certain producer puts into operation for 2010, and parent electric energy meter number is altogether
116990, global failure rate is about 4.8% at present.Occur a certain number of failure electric energy meters in the process of running at present,
Therefore, the batch electric energy meter recent longevity can be predicted using the recent life-span prediction method of batch electric energy meter based on more stress influences
Life.
For the recent life-span prediction method of batch electric energy meter multiple faults mode, include the following steps:
S101, by a kind of recent life-span prediction method of batch electric energy meter multiple faults mode (Chinese Patent Application No.:
201811484825.X), the stage crash rate predicted value of each fault mode is obtained, as shown in table 2.
Each fault mode and whole table stage crash rate prediction result in table 2 is 1 year following
The main influence stress types of S102, clearly each fault mode；
According to failure analysis result, temperature stress is to influence the main stress types of clock of power meter cell failure；Humidity
Stress is the main stress types for influencing metering performance.The relationship of other fault modes and stress is still not clear, and temporarily thinks without bright
Development is rung.Known to then:
The stage crash rate predicted value λ of clock unit failureClock unitIt can state are as follows:
λClock unit=kT·λ0 clock unit (4)
The stage crash rate predicted value λ of metering performance failureMetering performanceIt can state are as follows:
λMetering performance=kRH·λ0 metering performance (5)
Wherein, kTFor temperature stress-clock unit failure influence coefficient, kRHFor humidity modification-metering performance failure
Influence coefficient.
S103, the history crash rate distribution situation for determining the intensity and each fault mode that influence stress；
By inquiring the meteorological data of the batch electric energy meter installation region, the annual in the regional historical each month can get
Temperature and humidity is horizontal, as shown in table 3.
Each month mean annual temperature humidity in batch electric energy meter installation region of table 3 and crash rate are horizontal
S104, the model influenced between stress and each fault mode is established；
(1) establishing temperature influences the model of stress and clock unit failure:
According to Arrhenius (Arrhenius) model, batch clock of power meter element failure rate logarithm and absolute temperature
Inverse is linear relationship.Based on this, by the method for linear fit, as shown in figure 3, establishing the failure of clock unit monthly average stage
Linear relationship between the logarithm and monthly average absolute temperature inverse of rate, as shown in formula (6).
(2) establishing humidity influences the model of stress and metering performance failure:
Comprehensively considering Arrhenius (Arrhenius) model and Hallberg-Peck model, (temperature-humidity synthesis is answered
Power), the logarithm of batch electric energy meter metering performance crash rate and the logarithm of humidity are linear relationship.Based on this, pass through linear fit
Method, as shown in figure 4, establishing between the logarithm and monthly mean relative humidity logarithm of metering performance monthly average stage crash rate
Linear relationship, as shown in formula (7).
λMetering performance=-4.3527ln (RH) -29.886 (7)
The influence coefficient of S105, calculation stages crash rate, and adjust the forecast period crash rate of each fault mode；
For temperature stress: (1) defining the influence system under the conditions of the batch installation region year weighted mean (17 DEG C)
Number is 1；(2) mean annual temperature level value (17 DEG C) value is substituted into formula (6), obtains clock unit under the conditions of year-round average temperature
Stage crash rate(3) according to the linear relationship of formula (6), temperature stress-clock unit under condition of different temperatures is calculated
The influence coefficient k of failureT。
For humidity modification: (1) defining the shadow under the conditions of the batch installation region year weighted average humidity (76.17%RH)
Ringing coefficient is 1；(2) mean annual temperature level value (76.17%RH) value is substituted into formula (7), obtains mean annual humidity condition
The stage crash rate of lower metering performance(3) according to the linear relationship of formula (7), humidity is answered under the conditions of calculating different humidity
Power-metering performance failure influence coefficient kRH。
Temperature stress-clock unit failure and humidity modification-metering performance failure shadow under table 4 2018 years different months
Ring coefficient
The stage crash rate in prediction month can be adjusted by influencing coefficient according to formula (2) and table 4.
S106, it adds up to adjusted each fault mode stage crash rate, it is pre- to obtain batch electric energy meter bulk life time
Measured value, as a result as shown in Figure 2.
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 (9)
1. a kind of recent life-span prediction method of batch electric energy meter based on more stress influences, which comprises the following steps:
S1, the stage crash rate predicted value for obtaining each fault mode；
The main influence stress types of S2, clearly each fault mode；
S3, the history crash rate distribution situation for determining the intensity and each fault mode that influence stress；
S4, the model influenced between stress and each fault mode is established；
The influence coefficient of S5, calculation stages crash rate, and adjust the forecast period crash rate of each fault mode；
S6, it adds up to adjusted each fault mode stage crash rate, obtains batch electric energy meter bulk life time predicted value.
2. the batch electric energy meter recent life-span prediction method according to claim 1 based on more stress influences, feature exist
In the step S4 passes through numerical analysis method according to each fault mode actual stage crash rate and stress intensity distributed data
Establish the influence relationship of stress Yu each fault mode stage crash rate.
3. the batch electric energy meter recent life-span prediction method according to claim 2 based on more stress influences, feature exist
In, the numerical analysis method, directly analyzed using linear dependence, establish stress and each fault mode stage crash rate it
Between linear functional relation.
4. the batch electric energy meter recent life-span prediction method according to claim 2 based on more stress influences, feature exist
In the numerical analysis method is non-linear between establishment stage crash rate and stress by traditional stress life model
Functional relation.
5. the batch electric energy meter recent life-span prediction method according to claim 4 based on more stress influences, feature exist
In traditional stress life model includes: Arrhenius relationship, Hallberg-Peck model, cumulative fatigue damage mould
Type-Miner rule.
6. the batch electric energy meter recent life-span prediction method according to claim 1 based on more stress influences, feature exist
In step S2 determines each fault mode according to failure analysis result and electric energy meter historical failure data statistic analysis result
It is main to influence stress types.
7. the batch electric energy meter recent life-span prediction method according to claim 1 based on more stress influences, feature exist
In step S3 obtains stress intensity and its at any time distribution situation according to the actual motion environment of batch electric energy meter to be predicted；And
Statistically analyze electric energy meter history crash rate data.
8. the batch electric energy meter recent life-span prediction method according to claim 1-7 based on more stress influences,
It is characterized in that, stress that the step S5 is obtained according to the stress intensity level distribution and step S4 of forecast period and each
The model of fault mode stage crash rate obtains jth kind stress types to the influence system of the stage crash rate of i-th kind of fault mode
Number kij, the stage crash rate λ of i-th kind of fault mode after adjustmentiIt is expressed asWherein, λ0iTo be obtained by step S1
I-th kind of fault mode prediction stage crash rate, i value be 1,2,3 ..., N, j value be 1,2,3 ..., M, the stress
Type includes temperature, humidity, thunder and lightning, salt fog and electric stress.
9. the batch electric energy meter recent life-span prediction method according to claim 8 based on more stress influences, feature exist
In step S6 adds up to all adjusted each fault mode stage crash rates, and it is pre- to obtain batch electric energy meter bulk life time
Measured value, then the stage crash rate statement of the whole table of batch electric energy meter are as follows:
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| CN113065234A (en) * | 2021-03-17 | 2021-07-02 | 广东电网有限责任公司计量中心 | Batch reliability risk level assessment method and system for intelligent electric meters |
| CN114252794A (en) * | 2021-11-24 | 2022-03-29 | 国电南瑞科技股份有限公司 | Method and device for predicting residual life of disassembled intelligent electric energy meter |
| CN114252794B (en) * | 2021-11-24 | 2024-04-09 | 国电南瑞科技股份有限公司 | Method and device for predicting residual life of disassembled intelligent ammeter |
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