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.
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.