CN110146840B - Batch electric energy meter near term life prediction method based on multi-stress influence - Google Patents
Batch electric energy meter near term life prediction method based on multi-stress influence Download PDFInfo
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Abstract
The invention discloses a batch electric energy meter near term life prediction method based on multi-stress influence. When the electric energy meter with the fault is operated on site, the influence degrees of different fault modes under different stresses are different. The method is based on Weibull fitting, a failure rate prediction value of each fault mode is obtained by utilizing a multi-fault-mode near-term life prediction method, then a model between the influence stress and the failure rate of each fault mode is established according to the type of the main influence stress, the influence coefficient is calculated, and the near-term life of the whole electric energy meter is obtained after the failure rates of the prediction stages of each fault mode are adjusted and accumulated. The method considers the influence of different stresses on each fault mode of the electric energy meter, and uses quantized influence coefficients to adjust the near-term life prediction method based on multiple fault modes, so that the near-term life of the electric energy meter is predicted more accurately.
Description
Technical Field
The invention relates to the field of electric energy meter reliability evaluation, in particular to a batch electric energy meter near term life prediction method based on multi-stress influence.
Background
At present, the intelligent electric energy meter basically covers the whole network, data such as electricity consumption information, equipment abnormal information, asset information of the intelligent electric energy meter and the like in the field operation process can be transmitted to each provincial power grid marketing system or electricity consumption information acquisition system in real time, and the mass data provide key basic information for judging the field operation health state of the intelligent electric energy meter, so that the service life of the electric energy meter can be predicted.
Generally speaking, there are two methods for predicting the near-term life of a batch of electric energy meters: firstly, as described in ' a method for predicting the recent service life of a batch electric energy meter ' (Chinese patent application number: 201811484818.X) ', analyzing and processing fault data by using field fault data of the batch electric energy meter and adopting an integral Weibull distribution fitting method to realize the service life prediction of the batch electric energy meter; and secondly, as described in the 'method for predicting the short-term service life of multiple fault modes of a batch electric energy meter' (Chinese patent application number: 201811484825.X), Weibull fitting is carried out on each fault mode of the electric energy meter, partial fault mode prediction results are optimized according to the goodness-of-fit condition, and further, the phase failure rates of all the fault modes are accumulated to obtain the predicted value of the whole service life of the batch electric energy meter.
However, the two prediction methods based on the Weibull distribution fitting are both based on the whole electric energy meter operated on site or the actual failure data of each fault mode, and the influence of external comprehensive stress on the service life of the electric energy meter is not considered. However, different stress types affect different failure modes of the electric energy meter to different degrees. Namely, the factors influencing the stable operation of the electric energy meter are related to one or more stress types, and the degradation process is also related to the stress intensity. These stress types include: temperature, humidity, salt spray, lightning, electrical stress, and the like.
In view of this, a model between the influence stress and failure rates of the fault modes can be established by analyzing the change characteristics of the stress, and the recent life prediction method based on the multiple fault modes is adjusted by using the quantized influence coefficients, so that the recent life of the electric energy meter can be predicted more accurately.
Disclosure of Invention
Based on this, the technical problem to be solved by the present invention is to overcome the defects existing in the prior art, and provide a method for predicting the near term life of a batch electric energy meter based on multiple stress influences, which combines the existing method for predicting the near term life of a batch electric energy meter based on multiple stress influences with the field reliability and the external comprehensive stress level to realize the near term life prediction of the batch electric energy meter based on multiple stress influences.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a batch electric energy meter near term life prediction method based on multi-stress influence comprises the following steps:
s1, obtaining a stage failure rate predicted value of each failure mode;
s2, defining the main influence stress type of each fault mode;
s3, confirming the intensity of the fixing response force and the historical failure rate distribution condition of each failure mode;
s4, establishing a model between the influence stress and each fault mode;
s5, calculating influence coefficients of the failure rate of the stages, and adjusting the failure rate of the prediction stages of each fault mode;
and S6, accumulating the failure rates of the adjusted fault mode stages to obtain the predicted value of the overall service life of the batch of electric energy meters.
Further, the step S4 is to establish an influence relationship between the stress and the failure rate of each failure mode stage by a numerical analysis method according to the failure rate of each failure mode actual stage and the stress intensity distribution data,
furthermore, the numerical analysis method directly utilizes linear correlation analysis to establish a linear functional relationship between the stress and the failure rate of each fault mode stage.
Furthermore, the numerical analysis method establishes a nonlinear functional relationship between the stage failure rate and the stress by means of a traditional stress life model.
Still further, the conventional stress life model includes: arrhenius model (temperature stress), Hallberg-Peck model (temperature-humidity comprehensive stress), and accumulated fatigue damage model-Miner law (current stress).
Further, step S2 determines the main influence stress type of each failure mode according to the failure analysis result and the statistical analysis result of the historical failure data of the electric energy meter.
Further, step S3 obtains the stress intensity and the distribution thereof with time according to the actual operating environment of the batch of electric energy meters to be predicted; and the historical failure rate data of the electric energy meter is counted and analyzed.
Further, the step S5 obtains an influence coefficient k of the j-th stress type on the stage failure rate of the i-th failure mode according to the stress intensity distribution level of the prediction stage and the model of the stress and the stage failure rate of each failure mode obtained in the step S4ijAnd the stage failure rate lambda of the ith fault mode after adjustmentiIs expressed as
Wherein λ is0iFor the failure rate in the ith fault mode prediction stage obtained in step S1, i takes values of 1, 2, 3, … and N, j takes values of 1, 2, 3, … and M, and the stress types include temperature, humidity, lightning, salt spray and electrical stress.
Further, step S6 is to accumulate all the adjusted failure rates of each failure mode stage to obtain a predicted value of the entire life of the batch of electric energy meters, and the failure rates of the batch of electric energy meters in the stage are expressed as:
the invention has the following beneficial effects: on the basis of a method for predicting the recent life of the batch electric energy meter in multiple fault modes (Chinese patent application number: 201811484825.X), the method considers the influence of different stresses on each fault mode of the electric energy meter, and uses quantized influence coefficients to adjust the method for predicting the recent life of the batch electric energy meter based on the multiple fault modes, so that the recent life of the batch electric energy meter is predicted more accurately.
The method can provide reference for the advance rotation and risk early warning of the electric energy meter and provide technical support for the state replacement of the electric energy meter.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for predicting the near term life of a batch of electric energy meters based on multiple stress effects according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the prediction of phase failure rate of the entire batch of electric energy meters in different failure modes according to an embodiment of the present invention;
FIG. 3 is a linear fitting graph of the monthly average failure rate logarithm of the clock unit and the monthly average reciprocal temperature in an application example of the present invention;
FIG. 4 is a graph of the linear fitting result of the logarithm of the monthly average failure rate and the logarithm of the monthly average humidity in the measurement performance in the application example of the invention.
Detailed Description
In order to make the purpose and technical solution of the present invention more apparent, the present invention is further described with reference to the following embodiments and the accompanying drawings, but the present invention is not limited thereto.
Examples
The embodiment provides a method for predicting the near-term life of a batch electric energy meter based on multi-stress influence, as shown in fig. 1.
First, basic principle
It is assumed that M different stress types may affect the expected life of the electric energy meter, and the N failure modes in the electric energy meter are affected by different stress types in different degrees, and the difference in the degrees of the effects is finally reflected as the adjustment of the stage failure rate of the electric energy meter.
Thus, define kijThe influence coefficient of the j-th stress type (j takes values of 1, 2, 3, … and M) on the stage failure rate of the i-th fault mode (i takes values of 1, 2, 3, … and N) is set, so that the stage failure rate of the whole batch of electric energy meters can be expressed as:
wherein λ isWhole watchFor the predicted value of the phase failure rate of the whole batch of electric energy meters, lambda0Failure rate of the batch of electric energy meters is obtained by a Weibull distribution fitting prediction method based on a sub-fault mode (in detail, for example, a prediction method of the near term life of the batch of electric energy meters in multiple fault modes", chinese patent application No.: 201811484825. X).
It is generally accepted that when k isijWhen the value is 1, the stress type has no obvious influence on the phase failure rate of the fault mode.
Therefore, the method for predicting the near-term service life of the batch electric energy meter based on the multi-stress influence realizes the key processes that:
(1) determining the main influence stress type of each fault mode of the batch of electric energy meters;
(2) defining the intensity distribution of main stress types in the actual environment on site;
(3) and establishing a model between the influence stress and the failure rate of each fault mode stage, and calculating the influence coefficient of the influence stress on the failure rate of each fault mode stage.
II, predicting step
In view of this, a method for predicting the near-term life of a batch of electric energy meters based on multi-stress influence includes the following specific steps:
s1, obtaining a stage failure rate predicted value of each failure mode through the existing method for predicting the recent life of the batch electric energy meter in multiple failure modes (Chinese patent application number: 201811484825. X);
s2, defining the main influence stress type of each fault mode;
s3, confirming the intensity of the fixing response force and the historical failure rate distribution condition of each failure mode;
s4, establishing a model between the influence stress and each fault mode;
s5, calculating influence coefficients of the failure rate of the stages, and adjusting the failure rate of the prediction stages of each fault mode;
and S6, accumulating the failure rates of the adjusted fault mode stages to obtain the predicted value of the overall service life of the batch of electric energy meters.
On the basis of a method for predicting the recent life of the batch electric energy meter in multiple failure modes (Chinese patent application number: 201811484825.X), a model between the influence stress and each failure mode is established, and the recent life prediction method based on the multiple failure modes is adjusted by using quantized influence coefficients, so that the recent life of the electric energy meter is predicted more accurately.
Generally, step S1 can be developed according to the steps described in the patent "a method for predicting recent life of multiple failure modes of a batch electric energy meter" (chinese patent application No. 201811484825.X), which are not described herein again;
and step S2, determining the main influence stress type of each fault mode according to the fault analysis result and the historical fault data statistical analysis result of the electric energy meter.
Generally, based on the principle characteristics of the internal circuit of the electric energy meter and the analysis of the main fault cause in the field, the relationship between the main influence stress of the electric energy meter and the common possible fault mode is shown in the following table.
Meter 1 relationship between electric energy meter influence stress and each failure mode
| Serial number | Type of stress | Failure modes of major influence | Common influencing process |
| 1 | Temperature of | Clock unit, metering performance | Cause clock drift failure and metering deviation |
| 2 | Humidity | Metering performance | Resulting in electrochemical migration and parameter drift |
| 3 | Salt fog | Metering performance, power supply unit | Salt fog induced migration and corrosion induced failure |
| 4 | Lightning | Appearance failure, communication unit | Cause meter burning and communication failure |
| 5 | Current, voltage | Power supply unit and communication unit | High power, overheating, power supply fluctuation |
Step S3, obtaining the stress intensity and the distribution condition of the stress intensity along with time according to the actual operation environment of the electric energy meter to be predicted in batch; and the historical failure rate data of the electric energy meter is counted and analyzed.
Generally, for the stress of natural environments such as temperature, humidity, salt fog, thunder and lightning and the like, the stress can be obtained through meteorological data of an installation area of the electric energy meter; the electric stress such as voltage, current and the like can be obtained by monitoring the operation voltage of a power grid and the electric load level of a user.
And step S4, establishing a model between the influence stress and each fault mode through a numerical analysis method according to the comprehensive stress intensity distribution of the electric energy meter and the failure rate data of each fault mode at the actual stage.
The numerical analysis method can directly utilize linear correlation analysis to establish a linear function relationship between the stress and the failure rate of each fault mode stage; a nonlinear functional relationship between the stage failure rate and the stress can be established by means of a traditional stress life model. The stress life model includes: arrhenius model (temperature stress), Hallberg-Peck model (temperature-humidity comprehensive stress), and accumulated fatigue damage model-Miner law (current stress).
Step S5, according to the stress intensity distribution level of the predicted stage and the stress obtained in step S4 and the failure rate model of each failure mode stage, obtaining the influence coefficient k of the j stress type (j takes the values of 1, 2, 3, … and M) on the failure rate of the i failure mode (i takes the values of 1, 2, 3, … and N) in the stageijAnd the stage failure rate lambda of the ith fault mode after adjustmentiCan be expressed as:
wherein λ is0iPredicting stage failure rates for the i-th failure mode obtained by step S1, the stress types including: temperature, humidity, lightning, salt spray, electrical stress, and the like.
Step S6 is performed to accumulate all the adjusted failure rates of each failure mode stage to obtain the predicted value of the overall life of the batch of electric energy meters. The phase failure rate of the whole electric energy meter of the batch can be expressed as:
application example
In the application example, the electric energy meters of the batch to be predicted are put into operation by a certain manufacturer in 2010, the number of parent electric energy meters is 116990, and the current overall failure rate is about 4.8%. At present, a certain number of invalid electric energy meters appear in the operation process, so that the near-term service life of a batch of electric energy meters can be predicted by using a near-term service life prediction method based on multi-stress influence.
The method for predicting the recent life of the batch of electric energy meters in the multiple failure modes comprises the following steps:
s101, obtaining a stage failure rate predicted value of each failure mode through a recent life prediction method (Chinese patent application number: 201811484825.X) of multiple failure modes of the batch electric energy meter, as shown in a table 2.
TABLE 2 failure rate prediction results for each failure mode and the entire TABLE within 1 year in the future
S102, defining the main influence stress type of each fault mode;
according to the fault analysis result, the temperature stress is the main stress type influencing the fault of the clock unit of the electric energy meter; humidity stress is the dominant type of stress affecting metering performance. The relationship between other failure modes and stress is not clear and is temporarily considered to have no significant effect. Then, it can be known that:
phase failure rate prediction value lambda of clock unit faultClock unitCan be expressed as:
λclock unit＝kT·λ0 clock unit (4)
Stage failure rate prediction value lambda for metering performance faultsMetering performanceCan be expressed as:
λmetering performance＝kRH·λ0 measurement Performance (5)
Wherein k isTCoefficient of influence, k, for temperature stress-clock unit failureRHIs the coefficient of influence of humidity stress-gauge performance failure.
S103, confirming the intensity of the fixing response force and the historical failure rate distribution condition of each failure mode;
by querying the meteorological data of the electric energy meter installation area of the batch, the annual average temperature and humidity level of each month in the history of the area can be obtained, as shown in table 3.
Table 3 average annual temperature and humidity and failure rate level of each month in the installation area of the batch of electric energy meters
S104, establishing a model between the influence stress and each fault mode;
(1) establishing a model of temperature-affected stress and clock unit failure:
according to an Arrhenius model, the logarithm of the failure rate of the clock unit of the batch electric energy meter and the reciprocal of the absolute temperature are in a linear relation. Based on this, by using a linear fitting method, as shown in fig. 3, a linear relation between the logarithm of the failure rate in the monthly average stage of the clock unit and the reciprocal of the monthly average absolute temperature is established, as shown in equation (6).
(2) Establishing a model of humidity influence stress and measurement performance faults:
the logarithm of the metering performance failure rate of the batch electric energy meter and the logarithm of the humidity are in a linear relation by comprehensively considering an Arrhenius model and a Hallberg-Peck model (temperature-humidity comprehensive stress). Based on this, by a linear fitting method, as shown in fig. 4, a linear relationship between the logarithm of the monthly average stage failure rate of the metering performance and the logarithm of the monthly average relative humidity is established, as shown in formula (7).
ln(λMetering performance)＝-4.3527ln(RH)-29.886 (7)
S105, calculating an influence coefficient of the stage failure rate, and adjusting the prediction stage failure rate of each fault mode;
for temperature stress: (1) defining the influence coefficient of the batch of installation areas under the condition of year weighted average temperature (17 ℃) as 1; (2) substituting the annual average temperature level value (17 ℃) into the formula (4) to obtain the stage failure rate of the clock unit under the annual average temperature condition
(3) According to the stage failure rate of the clock unit under the acquired annual average temperature condition
Substituting into equation (6), calculatingCoefficient of influence k of temperature stress-clock unit failure under different temperature conditionsT。
For the humidity stress: (1) defining the influence coefficient of the batch of the installation area under the condition of year weighted average humidity (76.17% RH) to be 1; (2) substituting the annual average humidity level value (76.17% RH) into equation (5) to obtain the stage failure rate of the metering performance under the annual average humidity condition
(3) Staging failure rate based on measured performance at acquired annual average humidity
Substituting into formula (7), calculating influence coefficient k of humidity stress-metering performance fault under different humidity conditionsRH。
TABLE 42018 influence coefficients of temperature stress-clock unit failure and humidity stress-metrology performance failure in different months
The phase failure rate of the predicted month can be adjusted according to the formula (2) and the influence coefficient of table 4.
S106, accumulating the failure rates of the adjusted fault mode stages to obtain the predicted value of the whole service life of the electric energy meters in batches, wherein the result is shown in figure 2.
The above-described embodiments are merely illustrative of one embodiment of the present invention, and the description is specific and detailed, but should not be 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.
Claims (4)
1. A method for predicting the near term life of a batch of electric energy meters based on multi-stress influence is characterized by comprising the following steps:
s1, obtaining a stage failure rate predicted value of each failure mode;
s2, defining the main influence stress type of each fault mode;
s3, confirming the intensity of the fixing response force and the historical failure rate distribution condition of each failure mode;
s4, establishing a model between the influence stress and each fault mode;
s5, calculating influence coefficients of the failure rate of the stages, and adjusting the failure rate of the prediction stages of each fault mode;
s6, accumulating the failure rates of the adjusted failure mode prediction stages to obtain a predicted value of the whole service life of the batch of electric energy meters;
step S2 specifies the main impact stress types of each failure mode as follows:
according to the fault analysis result, the temperature stress is the main stress type influencing the fault of the clock unit of the electric energy meter; humidity stress is the dominant type of stress affecting metrology performance, then:
failure rate predicted value lambda of clock unit monthly average stageClock unitThe expression is as follows:
λclock unit＝kT·λ0 clock unit (4)
Failure rate predicted value lambda of monthly average stage of metering performanceMetering performanceThe expression is as follows:
λmetering performance＝kRH·λ0 measurement Performance (5)
Wherein k isTCoefficient of influence, k, for temperature stress-clock unit failureRHThe influence coefficient is humidity stress-metering performance fault;
step S4 builds a model between the impact stress and each failure mode as follows:
1) establishing a model of temperature-affected stress and clock unit failure:
according to an Arrhenius model, the logarithm of the failure rate of the clock units of the batch electric energy meter and the reciprocal of the absolute temperature are in a linear relation, and on the basis, the linear relation between the logarithm of the failure rate predicted value of the clock units in the monthly average stage and the reciprocal of the monthly average absolute temperature is established through a linear fitting method, as shown in the formula (6):
2) establishing a model of humidity influence stress and measurement performance faults:
considering an Arrhenius model and a Hallberg-Peck model comprehensively, the logarithm of the failure rate of the batch electric energy meter in the measurement performance and the logarithm of the humidity are in a linear relation, and on the basis, a linear relation between the logarithm of the failure rate predicted value in the monthly average stage of the measurement performance and the logarithm of the monthly average relative humidity is established by a linear fitting method, as shown in formula (7):
ln(λmetering performance)＝-4.3527ln(RH)-29.886 (7)
Step S5 calculates the influence coefficient of phase failure rate, and adjusts the content of the predicted phase failure rate of each failure mode as follows:
for temperature stress: (1) defining the influence coefficient of a certain batch of installation area under the condition of year weighted average temperature as 1; (2) substituting the annual average temperature level value into the formula (6) to obtain the stage failure rate of the clock unit under the annual average temperature condition
(3) According to the stage failure rate of the clock unit under the acquired annual average temperature condition
Substituting the formula (4) into the formula to calculate the influence coefficient k of the temperature stress-clock unit fault under different temperature conditionsT；
For the humidity stress: (1) defining the influence coefficient of a certain batch of installation area under the condition of year weighted average humidity as 1; (2) substituting the annual average humidity level value into formula (7) to obtain the stage failure rate of the metering performance under the annual average humidity condition
(3) Staging failure rate based on measured performance at acquired annual average humidity
Substituting into formula (5), calculating influence coefficient k of humidity stress-metering performance fault under different humidity conditionsRH；
The step S5 obtains the influence coefficient k of the j-th stress type on the stage failure rate of the i-th failure mode according to the stress intensity distribution level of the prediction stage and the models of the stress and the stage failure rate of each failure mode obtained in the step S4ijAnd the stage failure rate lambda of the ith fault mode after adjustmentiThe expression is as follows:
wherein λ is0iFor the failure rate of the ith fault mode prediction stage obtained in the step S1, the values of i are 1, 2, 3, … and N, the values of j are 1, 2, 3, … and M, and the stress types comprise temperature, humidity, thunder, salt fog and electric stress;
and (3) adjusting the phase failure rate of the predicted month according to the formula (2) and the temperature stress-clock unit failure influence coefficient and the humidity stress-metering performance failure influence coefficient.
2. The method for predicting the recent life of a batch of electric energy meters based on multiple stress influences according to claim 1, wherein step S2 is implemented to determine the main influence stress type of each fault mode according to the fault analysis result and the statistical analysis result of the historical fault data of the electric energy meters.
3. The batch electric energy meter near term life prediction method based on multiple stress influences as claimed in claim 1, wherein step S3 is to obtain the stress intensity and the distribution thereof with time according to the actual operation environment of the batch electric energy meter to be predicted; and the historical failure rate data of the electric energy meter is counted and analyzed.
4. The method according to claim 1, wherein the step S6 is implemented by accumulating the adjusted failure rates of the failure modes in the prediction stages to obtain the predicted value of the total life of the batch of electric energy meters, and the failure rates of the batch of electric energy meters in the prediction stages are expressed as:
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