CN113065234B - Batch reliability risk level assessment method and system for intelligent electric meters - Google Patents

Batch reliability risk level assessment method and system for intelligent electric meters Download PDF

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CN113065234B
CN113065234B CN202110287286.6A CN202110287286A CN113065234B CN 113065234 B CN113065234 B CN 113065234B CN 202110287286 A CN202110287286 A CN 202110287286A CN 113065234 B CN113065234 B CN 113065234B
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路韬
黄友朋
招景明
唐捷
彭策
赵闻
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Measurement Center of Guangdong Power Grid Co Ltd
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Abstract

The invention provides a batch reliability risk grade assessment method and system for intelligent electric meters, wherein the method comprises the following steps: acquiring fault data and installation data of the intelligent electric meter, and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: rejecting noise data in the fault data and the installation data; performing parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain failure distribution function parameters; and estimating the service life of the intelligent ammeter and evaluating the reliability risk level according to the failure distribution function parameters. According to the method, based on the integral fault performance of the batch intelligent electric energy meters during operation, the reliability risk of the batch electric energy meters is evaluated under the requirement of a specified service life index (including a specified accumulated fault rate), the accuracy of prediction evaluation is improved, and a decision basis is provided for an operation management unit to determine the sampling inspection and the rotation period of the batch electric energy meters.

Description

Batch reliability risk level assessment method and system for intelligent electric meters
Technical Field
The invention relates to the technical field of intelligent electric meter instrument detection, in particular to a batch reliability risk level evaluation method and system for intelligent electric meters.
Background
Generally, when the operation time of a certain batch of electric energy meters reaches a specified value, a sampling check or direct replacement is required to be carried out. In recent years, the use quantity of the intelligent electric energy meters is greatly increased, the operation management of the intelligent electric energy meters is carried out according to the requirements, the sampling inspection is carried out according to fixed time, and batch electric energy meters with batch risks may not be discovered through the sampling inspection in time; it is also possible to replace a large number of good-status batch meters in advance, which is a waste. How to identify and warn batch electric energy meters with high risks in the operation process provides decision basis for the selective inspection and the optimization of the alternation period of the batch electric energy meters, and becomes a problem to be solved urgently in the industry at present.
Disclosure of Invention
The invention provides a batch reliability risk grade assessment method and system for intelligent electric meters, and the accuracy of prediction of the failure rate of the intelligent electric meters is improved.
One embodiment of the invention provides a batch reliability risk level assessment method for intelligent electric meters, which comprises the following steps:
acquiring fault data and installation data of the intelligent electric meter, and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: rejecting noise data in the fault data and the installation data;
performing parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain failure distribution function parameters;
and according to the failure distribution function parameters, life prediction and reliability risk grade evaluation of the intelligent electric meter are carried out.
Further, the performing parameter estimation on the reliability data according to the failure distribution model of the electric energy meter to obtain a failure distribution function parameter includes:
selecting two parameters Weibull distribution as the failure distribution model, wherein the failure distribution model is as follows:
Figure BDA0002981004150000021
wherein e is a natural constant, t is time, beta is a shape parameter, and eta is a characteristic life or a true scale parameter;
establishing a likelihood function model of a two-parameter Weibull distribution model according to the shape parameters and the characteristic life or the true scale parameters;
wherein, the likelihood function model of the two-parameter Weibull distribution model is as follows:
Figure BDA0002981004150000022
Figure BDA0002981004150000031
wherein, F (t) j ) Is t j Cumulative failure rate at time F (t) j -1) is t j-1 The accumulative failure rate at the moment, m is the grouping number of the running time of the electric meters in a batch, t j-1 And t j For batch meter run length, R j Is [ t ] j-1 ,t j ]Total number of failures in time interval, l j The number of failures of right-deleted data, e is a natural constant, and C is a constant independent of parameters β and η; beta is a shape parameter, eta is a characteristic life or a true scale parameter;
processing a likelihood function model of the two-parameter Weibull distribution model to obtain failure distribution function parameters;
the failure distribution function parameters are a maximum likelihood estimation result of a shape parameter which enables the likelihood function to take the maximum value and a maximum likelihood estimation result of a characteristic service life or a true scale parameter.
Further, the estimating the service life and the evaluating the reliability risk level of the smart meter according to the failure distribution function parameter include:
according to the failure distribution function parameters, estimating the service life of the intelligent ammeter through a service life model;
wherein the lifetime model is:
Figure BDA0002981004150000032
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002981004150000033
in order for the lifetime to be as long as it is,
Figure BDA0002981004150000034
for maximum likelihood estimates of feature lifetime or true scale parameters,
Figure BDA0002981004150000035
the maximum likelihood estimation result of the shape parameter is obtained, and R is the reliability;
acquiring the factory service life of the intelligent electric meter and the maximum value of the running time of the intelligent electric meter;
and according to the factory service life of the intelligent electric meter, the maximum value of the running time of the intelligent electric meter and the service life, evaluating the intelligent electric meter by presetting an intelligent electric meter reliability risk grade evaluation rule.
Further, the preprocessing further comprises:
converting the installation time and the fault occurrence time in the fault data into operation duration;
specifically, the conversion is by the following formula:
t u ＝T f -T s ；
wherein, T s For installation time, T f Time of occurrence of failure, t u Is the length of the run.
Further, the acquiring fault data and installation data of the smart electric meter, and preprocessing the fault data and the installation data to obtain reliability data includes:
preprocessing the fault data and the installation data to obtain fault reliable data and installation reliable data with noise data in the fault data and the installation data removed;
and counting the fault reliable data and the installation reliable data to obtain the reliability data.
An embodiment of the present invention provides a batch reliability risk level evaluation system for smart meters, including:
the data processing module is used for acquiring fault data and installation data of the intelligent ammeter and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: rejecting noise data in the fault data and the installation data;
the parameter estimation module is used for carrying out parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain failure distribution function parameters;
and the evaluation module is used for carrying out life estimation and reliability risk grade evaluation on the intelligent electric meter according to the failure distribution function parameters.
Further, the parameter estimation module includes:
the failure distribution model calculation submodule is used for selecting two parameters Weibull distribution as the failure distribution model, and the failure distribution model is as follows:
Figure BDA0002981004150000041
wherein beta is a shape parameter, and eta is a characteristic life or a true scale parameter;
the likelihood function model establishing submodule is used for establishing a likelihood function model of a two-parameter Weibull distribution model according to the shape parameters and the characteristic service life or the true scale parameters;
wherein, the likelihood function model of the two-parameter Weibull distribution model is as follows:
Figure BDA0002981004150000051
Figure BDA0002981004150000061
wherein C is a constant independent of parameters β and η; beta is a shape parameter, eta is a characteristic life or a true scale parameter;
the likelihood function model processing submodule is used for processing a likelihood function model of the two-parameter Weibull distribution model to obtain failure distribution function parameters;
the failure distribution function parameters are a maximum likelihood estimation result of a shape parameter which enables the likelihood function to take the maximum value and a maximum likelihood estimation result of a characteristic service life or a true scale parameter.
Further, the evaluation module includes:
the service life evaluation submodule is used for predicting the service life of the intelligent ammeter through a service life model according to the failure distribution function parameters;
wherein the lifetime model is:
Figure BDA0002981004150000062
wherein the content of the first and second substances,
Figure BDA0002981004150000063
in order for the lifetime to be as long as it is,
Figure BDA0002981004150000064
as a result of the maximum likelihood estimation of the feature lifetime or true scale parameter,
Figure BDA0002981004150000065
the maximum likelihood estimation result of the shape parameter is obtained, and R is the reliability;
the numerical value acquisition sub-module is used for acquiring the maximum value of the delivery life of the intelligent electric meter and the operation time of the intelligent electric meter;
and the evaluation submodule is used for evaluating the intelligent electric meter according to the factory service life of the intelligent electric meter, the maximum value of the running time of the intelligent electric meter and the service life by presetting an intelligent electric meter reliability risk grade evaluation rule.
Further, the preprocessing further comprises:
converting the installation time and the fault occurrence time in the fault data into operation duration;
specifically, the conversion is by the following formula:
t u ＝T f -T s ；
wherein, T s For installation time, T f Time of occurrence of failure, t u Is the length of the run.
Further, the data processing module includes:
the data denoising submodule is used for preprocessing the fault data and the installation data to obtain fault reliable data and installation reliable data of which the noise data in the fault data and the installation data are removed;
and the data statistics submodule is used for carrying out statistics on the fault reliable data and the installation reliable data to obtain the reliability data.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
one embodiment of the invention provides a batch reliability risk level evaluation method for smart electric meters, which comprises the following steps: acquiring fault data and installation data of the intelligent ammeter, and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: rejecting noise data in the fault data and the installation data; performing parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain failure distribution function parameters; and according to the failure distribution function parameters, life prediction and reliability risk grade evaluation of the intelligent electric meter are carried out. According to the method, based on the integral fault performance of the batch intelligent electric energy meters during operation, the reliability risk of the batch electric energy meters is evaluated under the requirement of a specified service life index (including a specified accumulated fault rate), the accuracy of prediction evaluation is improved, and a decision basis is provided for an operation management unit to determine the sampling inspection and the rotation period of the batch electric energy meters.
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In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the embodiments will be briefly described below, and obviously, the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a batch reliability risk level evaluation method for smart meters according to an embodiment of the present invention;
fig. 2 is a flowchart of a batch reliability risk level evaluation method for smart meters according to another embodiment of the present invention;
FIG. 3 is a flowchart illustrating a batch reliability risk level assessment method for smart meters according to another embodiment of the present invention;
FIG. 4 is a flowchart illustrating a batch reliability risk level assessment method for smart meters according to another embodiment of the present invention;
fig. 5 is a device diagram of a batch reliability risk level evaluation system for smart meters according to an embodiment of the present invention;
fig. 6 is a device diagram of a batch reliability risk level evaluation system for smart meters according to another embodiment of the present invention;
fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are only for convenience of description and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Based on the operation fault data and the installation data of the intelligent electric energy meter, researchers develop failure distribution function fitting verification based on Weibull distribution; on the basis of Weibull distribution, researchers estimate distribution parameters of the intelligent electric energy meter failure distribution function by using a least square method; the future failure rate of the intelligent electric energy meter is predicted by using a neural network algorithm.
The prior art has the problems that:
the distribution parameters of the intelligent electric energy meter failure distribution function are estimated by using a least square method, and the least square method is not considered to be not suitable for data with a large number of deletions.
The technology is not found for a while, and a batch reliability risk level evaluation rule of the intelligent electric energy meters is formulated.
A first aspect.
Referring to fig. 1-4, an embodiment of the invention provides a batch reliability risk level evaluation method for smart meters, including:
s10, acquiring fault data and installation data of the intelligent electric meter, and preprocessing the fault data and the installation data to obtain reliability data. Wherein the pre-processing comprises: and eliminating noise data in the fault data and the installation data.
In a specific embodiment, the pre-processing further comprises:
converting the installation time and the fault occurrence time in the fault data into operation duration;
specifically, the conversion is by the following formula:
t u ＝T f -T s ；
wherein, T s For installation time, T f Time of occurrence of failure, t u Is the length of the run.
In a specific embodiment, the S10, acquiring fault data and installation data of the smart meter, and preprocessing the fault data and the installation data to obtain reliability data, includes:
s11, preprocessing the fault data and the installation data to obtain fault reliable data and installation reliable data of eliminating noise data in the fault data and the installation data.
And S12, counting the fault reliable data and the installation reliable data to obtain the reliability data.
In a specific embodiment, a batch reliability risk level evaluation method for smart meters includes:
step 1: and collecting fault data and installation data of the intelligent electric energy meter.
The fault data is: and the information of the installation time, the fault occurrence time, the fault phenomenon and the like corresponding to the electric energy meters with faults on the use site in a certain batch of intelligent electric energy meters.
The installation data is: and the arrival time and the installation time corresponding to all installed electric energy meters in a certain batch of intelligent electric energy meters.
Step 2: and processing the fault data and the installation data, wherein the processing comprises abnormal data elimination and data transformation.
The abnormal data elimination is as follows: and (4) identifying and eliminating error entries in the data, wherein the installation time is earlier than the arrival time, and the fault occurrence time is earlier than the installation time.
The data transformation is that: setting time T in fault data s And time of occurrence of failure T f Into a running time t u . The transformation formula is: t is t u ＝T f -T s 。
And step 3: and carrying out reliability data statistics on the batch electric energy meters based on the processed fault data and the installation data.
The operation dates of the electric energy meters in the same batch are different and may be different by months or even longer. In order to reflect the failure data of the electric energy meters in different operation time periods in the same batch, the batch faults and the installation data are counted according to the following method.
The installation time of the installed part in a certain batch of electric energy meters can be divided into n sections: (T) 0 ,T 1 ],(T 1 ,T 2 ],…,(T n-1 ,T n ]. Counting the total installation number N in each installation time period i (i＝1,2,…,n)。
Dividing the running time of the electric energy meter into m sections: (t) 0 ,t 1 ],(t 1 ,t 2 ],…,(t m-1 ,t m ]. Wherein: t is t 0 0，(t m-1 ,t m ]And the time interval containing the longest operation time length in the batch of electric energy meters is obtained. Statistics (T) i-1 ,T i ]And (t) j-1 ,t j ]Corresponding number r of faulty meters i,j (i＝1,2,…,n；j＝1,2,…,m)。
And counting to obtain a failure distribution rule table of the operated electric energy meters in the batch of electric energy meters, wherein the failure distribution rule table is shown in table 1.
Meter 1 electric energy meter reliability data statistical meter
Figure BDA0002981004150000111
In Table 1, total number of failures
Figure BDA0002981004150000121
At an observation time t m For a certain time period of delivery (T) a-1 ,T a ](a =1,2, \8230;, n) if the longest operating time of the electric energy meter is less than a certain time t b (b =1,2, \8230;, m), meters commissioned due to the commissioning period are running for a length of time t b The latter state is unknown, then at t b Is still normal at the same timeThe electric energy meter is called in the interval (t) b-1 ,t b ]Right deletion data of (1), number l b Can be expressed as:
Figure BDA0002981004150000122
Figure BDA0002981004150000123
according to the statistical method, original entry data is converted into deleted failure data R from the interval j And right-deleted data l j (j =1,2, \8230;, m) is calculated.
And S20, performing parameter estimation on the reliability data according to the failure distribution model of the electric energy meter to obtain failure distribution function parameters.
In a specific embodiment, the S20, performing parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain a failure distribution function parameter, includes:
and S21, selecting two parameters of Weibull distribution as the failure distribution model.
Wherein the failure distribution model is:
Figure BDA0002981004150000124
wherein e is a natural constant, t is time, β is a shape parameter, and η is a characteristic lifetime or a true scale parameter.
And S22, establishing a likelihood function model of the two-parameter Weibull distribution model according to the shape parameters and the characteristic service life or the true scale parameters.
Wherein, the likelihood function model of the two-parameter Weibull distribution model is as follows:
Figure BDA0002981004150000125
Figure BDA0002981004150000131
wherein, F (t) j ) Is t j Cumulative failure rate at time, F (t) j -1) is t j-1 The accumulative failure rate at the moment, m is the grouping number of the running time of the electric meters in a batch, t j-1 And t j For batch meter run length, R j Is [ t ] j-1 ,t j ]Total number of failures in time interval, l j The number of failures of right-deleted data, e is a natural constant, and C is a constant independent of parameters β and η; beta is a shape parameter and eta is a characteristic lifetime or true scale parameter.
And S23, processing the likelihood function model of the two parameter Weibull distribution models to obtain failure distribution function parameters.
The failure distribution function parameters are a maximum likelihood estimation result of a shape parameter which enables the likelihood function to take the maximum value and a maximum likelihood estimation result of a characteristic service life or a true scale parameter.
In a specific embodiment, a batch reliability risk level assessment method for smart meters includes:
and 4, step 4: and estimating the failure distribution function parameters of the batch of electric energy meters based on the statistical reliability data.
Selecting two parameters Weibull distribution as a failure distribution function of the intelligent electric energy meter, wherein the function form is as follows:
Figure BDA0002981004150000132
in the above formula: e is a natural constant, t is time, β is a shape parameter, and η is a characteristic lifetime or true scale parameter.
The maximum likelihood estimation is a parameter estimation method based on the maximum probability principle, has high algorithm precision and wide adaptability, plays an important role in the parameter estimation problem, and has obvious advantages particularly under the condition of processing incomplete life data (namely, life data containing deleted data).
For the batch intelligent electric energy meter with the reliability data shown in table 1, the likelihood function of the weibull distribution parameter is as follows:
Figure BDA0002981004150000141
where C is a constant independent of the parameters β and η, F (t) j ) Is t j Cumulative failure rate at time F (t) j -1) is t j-1 The accumulative failure rate at the moment, m is the grouping number of the running time of the electric meters in a batch, t j-1 And t j For batch meter run length, R j Is [ t ] j-1 ,t j ]The total number of failures in the time interval, lj is the number of failures of right deleted data, and e is a natural constant. Taking logarithm of the above formula and deriving, and iterative solving by numerical method to obtain the maximum value of L (beta, eta)
Figure BDA0002981004150000151
And
Figure BDA0002981004150000152
i.e., the maximum likelihood estimates of β and η.
And 5: and estimating the service life of the batch of electric energy meters according to the estimated failure distribution function parameters.
At an observation time t m Based on the obtained maximum likelihood estimation result of Weibull distribution parameters
Figure BDA0002981004150000153
And
Figure BDA0002981004150000154
calculating the time of the specified reliability R of the batch power expression by the following formula, i.e. the estimated lifetime
Figure BDA0002981004150000155
Figure BDA0002981004150000156
And S30, estimating the service life of the intelligent electric meter and evaluating the reliability risk level according to the failure distribution function parameters.
In a specific embodiment, the S40, performing life estimation and reliability risk level assessment of the smart meter according to the failure distribution function parameter, includes:
and S31, estimating the service life of the intelligent electric meter through a service life model according to the failure distribution function parameters.
Wherein the lifetime model is:
Figure BDA0002981004150000157
wherein the content of the first and second substances,
Figure BDA0002981004150000158
in order for the lifetime to be as long as it is,
Figure BDA0002981004150000159
for maximum likelihood estimates of feature lifetime or true scale parameters,
Figure BDA00029810041500001510
and R is the reliability of the maximum likelihood estimation result of the shape parameter.
And S32, obtaining the factory service life of the intelligent electric meter and the maximum value of the intelligent electric meter operation time.
And S33, evaluating the intelligent electric meter through a preset intelligent electric meter reliability risk grade evaluation rule according to the factory service life of the intelligent electric meter, the maximum value of the intelligent electric meter operation duration and the service life.
In a specific embodiment, a batch reliability risk level assessment method for smart meters includes:
step 6: and carrying out reliability risk level evaluation on the intelligent electric energy meters in batch.
According to the specified service life t of the intelligent electric meter R Batch intelligent electric energy meter maximum operation time length t m At the current observation timeLife expectancy
Figure BDA0002981004150000161
And the estimated lifetime of the last observed time
Figure BDA0002981004150000162
And evaluating the reliability risk level of the intelligent electric energy meters in batch operation. The reliability risk level evaluation rules are shown in table 2.
Table 2 batch-in-transit intelligent electric energy meter reliability risk grade evaluation rule
Figure BDA0002981004150000163
Figure BDA0002981004150000171
Figure BDA0002981004150000181
In a specific embodiment, the invention provides a batch reliability risk level evaluation method for smart meters.
The method comprises the following specific steps:
step 1: taking a certain batch of single-phase intelligent electric energy meters in a certain area as an example, fault and installation data between 2016 and 2018 and 3 are collected. The fault data is: and the information of the installation time, the fault occurrence time, the fault phenomenon and the like corresponding to the electric energy meters with faults on the use site in a certain batch of intelligent electric energy meters. The installation data is: and the arrival time and the installation time corresponding to all installed electric energy meters in a certain batch of intelligent electric energy meters.
Step 2: and processing the fault data and the installation data, wherein the processing comprises abnormal data elimination and data transformation.
The abnormal data elimination is as follows: and (4) identifying and eliminating error entries in the data, wherein the installation time is earlier than the arrival time, and the fault occurrence time is earlier than the installation time.
The data transformation is as follows: setting time T in fault data s And time of occurrence of failure T f Into a running time t u . The transformation formula is: t is t u ＝T f -T s 。
And step 3:
the installation time of the installed part in a certain batch of electric energy meters can be divided into n sections: (T) 0 ,T 1 ],(T 1 ,T 2 ],…,(T n-1 ,T n ]. Counting the total installation number N in each installation time period i (i＝1,2,…,n)。
Dividing the running time of the electric energy meter into m sections: (t) 0 ,t 1 ],(t 1 ,t 2 ],…,(t m-1 ,t m ]. Wherein: t is t 0 0，(t m-1 ,t m ]The time interval includes the longest operation time length in the batch of electric energy meters. Statistics (T) i-1 ,T i ]And (t) j-1 ,t j ]Corresponding number r of faulty meters i,j (i＝1,2,…,n；j＝1,2,…,m)。
And counting to obtain a failure distribution rule table of the operated electric energy meters in the batch of electric energy meters, as shown in table 3.
Meter 3 electric energy meter reliability data statistical meter
Figure BDA0002981004150000201
In Table 3, total number of failures
Figure BDA0002981004150000202
And after the data processing is finished, segmenting the installation time by taking 3 months as an interval, segmenting the operation time by taking 91 days as an interval, and counting the faults and the installation data of the batch of electric energy meters according to the method.
At an observation time t m For a certain time period of delivery (T) a-1 ,T a ]Electric energy meter in (a =1,2, \8230;, n)If its longest running time is less than a certain time t b (b =1,2, \8230;, m), meters commissioned due to the commissioning period are running for a length of time t b The latter state is unknown, then at t b The electric energy meter which is normal at the moment is called in the interval (t) b-1 ,t b ]Right deletion data of (1), number l b Can be expressed as:
Figure BDA0002981004150000203
Figure BDA0002981004150000204
according to the method, the reliability data statistics of the batch electric energy are completed, and the statistical result is shown in table 4.
TABLE 4 statistical results of reliability data of single-phase intelligent electric energy meters in a certain batch
Figure BDA0002981004150000205
Figure BDA0002981004150000211
Figure BDA0002981004150000221
And 4, step 4: selecting 10 years as the specified life t R The allowable cumulative failure rate was taken to be 4.25% (reliability 95.75%).
Selecting two parameters Weibull distribution as a failure distribution function of the intelligent electric energy meter, wherein the function form is as follows:
Figure BDA0002981004150000222
based on the data of table 3, the corresponding maximum likelihood function is solved:
Figure BDA0002981004150000223
maximum likelihood estimates of the weibull distribution parameters at observation times t =1092 and t =1183 days can be obtained, as shown in table 5.
TABLE 5 Weibull distribution parameter estimation results
Figure BDA0002981004150000224
And 5: according to the Weibull distribution parameter estimation result and formula
Figure BDA0002981004150000225
Figure BDA0002981004150000226
The 95.75% reliable life of the batch of meters was estimated and the results are shown in table 6.
TABLE 6 Life estimate
Figure BDA0002981004150000227
Figure BDA0002981004150000231
And 6: and carrying out reliability risk level evaluation on the intelligent electric energy meters in batch.
From the statistics and lifetime estimates: at the observation time t =1183 days, the batch of electric energy meters t R In a period of not less than 10 years,
Figure BDA0002981004150000241
maximum time of delivery t m ＝1183，
Figure BDA0002981004150000242
Figure BDA0002981004150000243
With reference to the evaluation rules listed in Table 7, correspond toWherein item 9, the risk rating is high.
Table 7 batch-in-transit intelligent electric energy meter reliability risk grade evaluation rule
Figure BDA0002981004150000244
Figure BDA0002981004150000251
Figure BDA0002981004150000261
A second aspect.
Referring to fig. in the drawings, an embodiment of the present invention provides a batch reliability risk level evaluation system for smart meters, including:
the data processing module 10 is used for acquiring fault data and installation data of the intelligent ammeter and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: and eliminating noise data in the fault data and the installation data.
In a specific embodiment, the pre-processing further comprises:
and converting the installation time and the fault occurrence time in the fault data into operation time.
Specifically, the conversion is by the following formula:
t u ＝T f -T s ；
wherein, T s For installation time, T f Time of occurrence of failure, t u Is the length of the run.
In a specific embodiment, the data processing module 10 includes:
and the data denoising submodule 11 is configured to preprocess the fault data and the installation data to obtain fault reliable data and installation reliable data from which noise data in the fault data and the installation data are removed.
And the data statistics submodule 12 is used for carrying out statistics on the fault reliable data and the installation reliable data to obtain the reliability data.
And the parameter estimation module 20 is configured to perform parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain a failure distribution function parameter.
In a specific embodiment, the parameter estimation module 20 includes:
and the failure distribution model calculation submodule 21 is used for selecting two parameters Weibull distribution as the failure distribution model.
The failure distribution model is as follows:
Figure BDA0002981004150000271
wherein beta is a shape parameter and eta is a characteristic life or true scale parameter.
And the likelihood function model establishing submodule 22 is used for establishing a likelihood function model of the two-parameter Weibull distribution model according to the shape parameters and the characteristic service life or the true scale parameters.
Wherein, the likelihood function model of the two-parameter Weibull distribution model is as follows:
Figure BDA0002981004150000272
Figure BDA0002981004150000281
wherein C is a constant independent of parameters β and η; beta is a shape parameter and eta is a characteristic lifetime or true scale parameter.
And the likelihood function model processing submodule 23 is configured to process the likelihood function model of the two-parameter weibull distribution model to obtain a failure distribution function parameter.
The failure distribution function parameters are a maximum likelihood estimation result of a shape parameter which enables the likelihood function to take the maximum value and a maximum likelihood estimation result of a characteristic service life or a true scale parameter.
And the evaluation module 30 is configured to perform life estimation and reliability risk level evaluation of the smart meter according to the failure distribution function parameter.
In a specific embodiment, the evaluation module 30 includes:
and the service life evaluation submodule 31 is configured to estimate the service life of the smart meter through a service life model according to the failure distribution function parameter.
Wherein the lifetime model is:
Figure BDA0002981004150000282
wherein the content of the first and second substances,
Figure BDA0002981004150000283
in order for the lifetime to be as long as it is,
Figure BDA0002981004150000284
for maximum likelihood estimates of feature lifetime or true scale parameters,
Figure BDA0002981004150000285
and R is the reliability of the maximum likelihood estimation result of the shape parameter.
And the numerical value obtaining submodule 32 is used for obtaining the factory service life of the intelligent electric meter and the maximum value of the running time of the intelligent electric meter.
And the evaluation submodule 33 is configured to evaluate the smart electric meter according to the factory life of the smart electric meter, the maximum value of the operating time of the smart electric meter, and the life of the smart electric meter by presetting a reliability risk level evaluation rule of the smart electric meter.
In a third aspect.
The present invention provides an electronic device, including:
a processor, a memory, and a bus;
the bus is used for connecting the processor and the memory;
the memory is used for storing operation instructions;
the processor is used for calling the operation instruction, and the executable instruction enables the processor to execute the operation corresponding to the batch reliability risk level assessment method of the smart electric meter in the first aspect of the application.
In an alternative embodiment, an electronic device is provided, as shown in fig. 7, the electronic device 5000 shown in fig. 7 includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are coupled, such as via a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that the transceiver 5004 is not limited to one in practical application, and the structure of the electronic device 5000 does not limit the embodiments of the present application.
The processor 5001 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 5001 may also be a combination of processors implementing computing functionality, e.g., a combination comprising one or more microprocessors, a combination of DSPs and microprocessors, or the like.
Bus 5002 may include a path that conveys information between the aforementioned components. The bus 5002 may be a PCI bus or EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 7, but that does not indicate only one bus or one type of bus.
Memory 5003 may be, but is not limited to, ROM or other type of static storage device that can store static information and instructions, RAM or other type of dynamic storage device that can store information and instructions, EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 5003 is used for storing application code that implements aspects of the present application and is controlled in execution by the processor 5001. The processor 5001 is configured to execute application program code stored in the memory 5003 to implement aspects illustrated in any of the method embodiments described previously.
Wherein, the electronic device includes but is not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like.
A fourth aspect.
The invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer-readable storage medium implements the batch reliability risk level assessment method for the smart meters disclosed in the first aspect of the application.
Yet another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when run on a computer, enables the computer to perform the corresponding content in the aforementioned method embodiments.

Claims (8)

1. A batch reliability risk level evaluation method of smart meters is characterized by comprising the following steps:
acquiring fault data and installation data of the intelligent electric meter, and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: rejecting noise data in the fault data and the installation data;
performing parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain failure distribution function parameters;
according to the failure distribution function parameters, life prediction and reliability risk grade evaluation of the intelligent ammeter are carried out;
the parameter estimation of the reliability data according to the failure distribution model of the electric energy meter to obtain failure distribution function parameters comprises the following steps:
selecting two parameters of Weibull distribution as the failure distribution model, wherein the failure distribution model is as follows:
Figure FDA0004013700240000011
wherein e is a natural constant, t is time, beta is a shape parameter, and eta is a characteristic life or a true scale parameter;
establishing a likelihood function model of a two-parameter Weibull distribution model according to the shape parameters and the characteristic life or the true scale parameters;
wherein, the likelihood function model of the two-parameter Weibull distribution model is as follows:
Figure FDA0004013700240000012
Figure FDA0004013700240000021
wherein, F (t) j ) Is t j Cumulative failure rate at time F (t) j-1 ) Is t j-1 Cumulative failure rate at time t j-1 And t j The batch electric meter operation time length is represented by e as a natural constant, m is the grouping number of the batch electric meter operation time length, R j Is [ t ] j-1 ,t j ]Total number of failures in time interval, l j C is a constant independent of parameters beta and eta; beta is a shape parameter, eta is a characteristic life or a true scale parameter; j is a positive integer;
processing a likelihood function model of the two-parameter Weibull distribution model to obtain failure distribution function parameters;
the failure distribution function parameters are a maximum likelihood estimation result of a shape parameter which enables the likelihood function to take the maximum value and a maximum likelihood estimation result of a characteristic service life or a true scale parameter.
2. The method as claimed in claim 1, wherein said performing a lifetime estimation and a reliability risk level estimation for said smartmeters according to said failure distribution function parameters comprises:
according to the failure distribution function parameters, estimating the service life of the intelligent ammeter through a service life model;
wherein the lifetime model is:
Figure FDA0004013700240000022
wherein the content of the first and second substances,
Figure FDA0004013700240000023
in order for the lifetime to be as long as it is,
Figure FDA0004013700240000024
for maximum likelihood estimates of feature lifetime or true scale parameters,
Figure FDA0004013700240000025
the maximum likelihood estimation result of the shape parameter is obtained, and R is the reliability;
acquiring the factory service life of the intelligent electric meter and the maximum value of the running time of the intelligent electric meter;
and according to the factory service life of the intelligent electric meter, the maximum value of the running time of the intelligent electric meter and the service life, evaluating the intelligent electric meter by presetting an intelligent electric meter reliability risk grade evaluation rule.
3. The batch reliability risk level assessment method for smart meters of claim 1, wherein said preprocessing further comprises:
converting the installation time and the fault occurrence time in the fault data into operation duration;
specifically, the conversion is by the following formula:
t u ＝T f -T s ；
wherein, T s For installation time, T f Time of occurrence of failure, t u Is the length of the run.
4. The batch reliability risk level evaluation method for the smart meters according to claim 1, wherein the obtaining fault data and installation data of the smart meters and preprocessing the fault data and the installation data to obtain reliability data comprises:
preprocessing the fault data and the installation data to obtain fault reliable data and installation reliable data with noise data in the fault data and the installation data removed;
and counting the fault reliable data and the installation reliable data to obtain the reliability data.
5. The utility model provides a batch reliability risk level evaluation system of smart electric meter which characterized in that includes:
the data processing module is used for acquiring fault data and installation data of the intelligent ammeter and preprocessing the fault data and the installation data to obtain reliability data; wherein the pre-processing comprises: rejecting noise data in the fault data and the installation data;
the parameter estimation module is used for carrying out parameter estimation on the reliability data according to a failure distribution model of the electric energy meter to obtain failure distribution function parameters;
the evaluation module is used for carrying out life prediction and reliability risk grade evaluation on the intelligent ammeter according to the failure distribution function parameters;
the parameter estimation module comprises:
the failure distribution model calculation submodule is used for selecting two parameters Weibull distribution as the failure distribution model, and the failure distribution model is as follows:
Figure FDA0004013700240000041
wherein e is a natural constant, t is time, beta is a shape parameter, and eta is a characteristic life or a true scale parameter;
the likelihood function model establishing submodule is used for establishing a likelihood function model of a two-parameter Weibull distribution model according to the shape parameters and the characteristic service life or the true scale parameters;
wherein, the likelihood function model of the two-parameter Weibull distribution model is as follows:
Figure FDA0004013700240000042
Figure FDA0004013700240000051
wherein, F (t) j ) Is t j Cumulative failure rate at time, F (t) j-1 ) Is t j-1 The accumulative failure rate at the moment, m is the grouping number of the running time of the electric meters in a batch, t j-1 And t j For batch meter run length, R j Is [ t ] j-1 ,t j ]Total number of failures in time interval, l j The number of failures of the right deleted data is e, a natural constant and C, a constant which is irrelevant to the parameters beta and eta; beta is a shape parameter, eta is a characteristic life or a true scale parameter; j is a positive integer;
the likelihood function model processing submodule is used for processing a likelihood function model of the two-parameter Weibull distribution model to obtain failure distribution function parameters;
the failure distribution function parameters are a maximum likelihood estimation result of a shape parameter which enables the likelihood function to take the maximum value and a maximum likelihood estimation result of a characteristic service life or a true scale parameter.
6. The batch reliability risk rating system of smart meters of claim 5, wherein said evaluation module comprises:
the service life evaluation sub-module is used for predicting the service life of the intelligent ammeter through a service life model according to the failure distribution function parameters;
wherein the lifetime model is:
Figure FDA0004013700240000052
wherein the content of the first and second substances,
Figure FDA0004013700240000053
in order for the lifetime to be as long as it is,
Figure FDA0004013700240000054
for maximum likelihood estimates of feature lifetime or true scale parameters,
Figure FDA0004013700240000055
the maximum likelihood estimation result of the shape parameter is obtained, and R is the reliability;
the numerical value acquisition submodule is used for acquiring the factory service life of the intelligent electric meter and the maximum value of the operation time of the intelligent electric meter;
and the evaluation submodule is used for evaluating the intelligent electric meter according to the delivery life of the intelligent electric meter, the maximum value of the intelligent electric meter operation time and the service life by presetting an evaluation rule of the reliability risk grade of the intelligent electric meter.
7. The batch reliability risk rating system of smart meters of claim 5, wherein said preprocessing further comprises:
converting the installation time and the fault occurrence time in the fault data into operation duration;
specifically, the conversion is by the following formula:
t u ＝T f -T s ；
wherein, T s For installation time, T f Time of occurrence of failure, t u Is the length of the run.
8. The batch reliability risk rating system of smart meters of claim 5, wherein said data processing module comprises:
the data denoising submodule is used for preprocessing the fault data and the installation data to obtain fault reliable data and installation reliable data of noise data in the fault data and the installation data;
and the data statistics submodule is used for carrying out statistics on the fault reliable data and the installation reliable data to obtain the reliability data.
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