阅读说明：本技术 一种智能电能表可靠性预测方法及装置 (Intelligent electric energy meter reliability prediction method and device ) 是由 陈叶 曹敏 韩彤 朱梦梦 廖耀华 程富勇 王恩 刘光界 于 2020-11-11 设计创作，主要内容包括：本申请公开了一种智能电能表可靠性预测方法及装置,将智能电能表中每个组成模块的故障率转化为年故障率；利用统计模拟算法对智能电能表中每个组成模块进行时间抽样,确定每个组成模块的故障时间；利用故障树模型将每个组成模块的故障时间转变为智能电能表的整体故障时间；利用区间统计法,计算智能电能表的整体故障时间在每个时间区间的可靠度数值,以及,基于每个可靠度数值,计算智能电能表的可靠度函数曲线。可见,本发明实施例提供的方法及装置,利用统计模拟算法和故障树模型,可以准确预测智能电能表的可靠性,抽样丰富,使得模块故障率转化为整体故障率更加接近实际工作情况。(The application discloses a method and a device for predicting the reliability of an intelligent electric energy meter, which convert the failure rate of each component module in the intelligent electric energy meter into the annual failure rate; time sampling is carried out on each component module in the intelligent electric energy meter by utilizing a statistical simulation algorithm, and the fault time of each component module is determined; converting the fault time of each component module into the integral fault time of the intelligent electric energy meter by using a fault tree model; and calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value. Therefore, the method and the device provided by the embodiment of the invention can accurately predict the reliability of the intelligent electric energy meter by utilizing the statistical simulation algorithm and the fault tree model, and have rich sampling, so that the module fault rate is converted into the integral fault rate which is closer to the actual working condition.)

1. The reliability prediction method for the intelligent electric energy meter is characterized by comprising the following steps of:

acquiring the fault rate of each component module in the intelligent electric energy meter, and converting the fault rate of each component module into an annual fault rate;

based on the annual fault rate of each component module in the intelligent electric energy meter, performing time sampling on each component module in the intelligent electric energy meter by using a statistical simulation algorithm, and determining the fault time of each component module in the intelligent electric energy meter;

constructing a fault tree model based on the actual structure corresponding to each component module in the intelligent electric energy meter;

converting the fault time of each component module in the intelligent electric energy meter into the integral fault time of the intelligent electric energy meter by using the fault tree model;

and calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value.

2. The method of claim 1, wherein the composition modules include a display module, a clock module, a fee control module, a storage module, a time period and rate module, an alarm control module, and a 485 communication module; and, the obtaining the fault rate of each component module in the intelligent electric energy meter comprises:

acquiring the work failure rate of each component corresponding to each composition module in the intelligent electric energy meter;

according to the formulaCalculating the fault rate lambda of each component module in the intelligent electric energy meter_Si；

In the formula, λ_SiThe fault rate of the ith component module in the intelligent electric energy meter is obtained; lambda [ alpha ]_ijThe working failure rate of the jth component of the ith component module in the intelligent electric energy meter is determined, N is the number of the component modules in the intelligent electric energy meter, and M is the working failure rate of the jth component of the ith component module in the intelligent electric energy meter_iThe number of components in the ith component module in the intelligent electric energy is determined.

3. The method of claim 1, wherein the composition modules comprise a power module and a metering module; and, the obtaining the fault rate of each component module in the intelligent electric energy meter comprises:

obtaining the acceleration test voltage, the normal working voltage and the acceleration rate constant of the voltage of each component module according to the formula V_AF＝exp[β×(V_s-V_n)]Calculating an electrical stress acceleration factor;

obtaining the percent relative humidity and absolute temperature of each of the constituent modules under normal operating conditions, the percent relative humidity and absolute temperature under accelerated stress conditions, and activation energy according to the formulaCalculating a temperature-humidity acceleration factor;

obtaining the accelerated test time of the electrical stress, the accelerated test time of the temperature-humidity and the number of the accelerated test samples according to the formula T_st＝m(t_v×V_AF+t_th×TH_AF) Calculating the equivalent test time T of the acceleration test_stTaking the equivalent test time as the failure rate of each component module;

in the formula, V_AFIs an electrical stress acceleration factor, V_sTo accelerate the test voltage, V_nIs a normal working voltage, beta is an acceleration rate constant of the voltage; TH_AFAs a temperature-humidity accelerating factor, RH_μPercent relative humidity, RH, under normal operating conditions_SPercent relative humidity, T, for accelerated stress conditions_uIs the absolute temperature under normal operating conditions, T_SK is a Boltzmann constant, Ea is activation energy, n is a constant, and the value range is 1-12; t is_stEquivalent test time for accelerated test, m is the number of samples for accelerated test, t_vAccelerated test time for electrical stress, t_thThe test time is accelerated for temperature-humidity.

4. The method of claim 2 or 3, wherein converting the failure rate of each of the component modules to an annual failure rate comprises:

according to the formula lambda_a＝8760×λ_SiConverting the failure rate of each of the component modules into an annual failure rate;

or according to formulaConverting the failure rate of each of the component modules into an annual failure rate.

5. The method of claim 1, wherein the determining the fault time of each component module in the intelligent electric energy meter by time sampling each component module in the intelligent electric energy meter based on the annual fault rate of each component module in the intelligent electric energy meter by using a statistical simulation algorithm comprises:

setting a distribution function of each bottom event of the statistical simulation algorithm, and calculating an inverse function of each distribution function by utilizing the annual fault rate of each component module in the intelligent electric energy meter; each bottom event corresponds to one composition module;

and calculating the sampling time of each bottom event with faults based on the preset simulation times and the inverse function of each distribution function, and taking the sampling time of each bottom event with faults as the fault time of each component module in the intelligent electric energy meter.

6. The method according to claim 1, wherein the converting the fault time of each component module in the intelligent electric energy meter into the overall fault time of the intelligent electric energy meter by using the fault tree model comprises:

converting the fault tree model into a plurality of minimal cut sets, wherein each minimal cut set comprises a plurality of bottom events; each bottom event corresponds to one composition module;

acquiring the fault time of the bottom event which has the latest fault in each minimal cut set, and taking the fault time of the bottom event which has the latest fault as the fault time corresponding to the minimal cut set;

and sequencing each minimal cut set with faults according to the fault time of each minimal cut set, and taking the fault time of the minimal cut set with the first bit in the bit column as the integral fault time of the intelligent electric energy meter.

7. The method according to claim 1, wherein the calculating the reliability value of the overall fault time of the intelligent electric energy meter in each time interval by using an interval statistic method comprises:

sampling the whole fault time of the intelligent electric energy meter to obtain a plurality of sub-time samples, wherein one sub-time sample corresponds to the whole fault time;

determining a time range required by interval statistics based on time intermediate values corresponding to a plurality of sub-time samples;

equally dividing the time range into a plurality of time intervals according to a preset time interval;

counting the total number of times that a plurality of said sub-time samples fall into each of said time intervals;

according to the formula R (t)_r)＝1-m_rCalculating the reliability value R (t) of the overall fault time of the intelligent electric energy meter in each time interval_r)；

In the formula, m_rThe total number of times that the sub-time samples fall into the r-th time interval is N, which is a preset statistical number.

8. The method according to claim 1, wherein said calculating a reliability function curve of said intelligent electric energy meter based on each of said reliability values comprises:

performing curve fitting on the reliability numerical value corresponding to each time interval to obtain a reliability function curve of the intelligent electric energy meter, wherein the expression of the reliability function curve is R (t) ═ 1.001exp (-0.00503 t);

wherein t is the age time.

9. The method of claim 8, further comprising:

obtaining the fitting degree of curve fitting, and judging whether the fitting degree accords with a fitting required value;

if the fitting degree does not accord with the fitting required value, increasing the number of time samples and the simulation times;

and executing the calculation step of the reliability function curve of the intelligent electric energy meter based on the new time sampling quantity and the new simulation times.

10. An intelligent electric energy meter reliability prediction device is characterized by comprising:

the system comprises an annual fault rate determining module, a fault rate judging module and a fault rate judging module, wherein the annual fault rate determining module is used for acquiring the fault rate of each component module in the intelligent electric energy meter and converting the fault rate of each component module into the annual fault rate;

the time sampling module is used for sampling time of each component module in the intelligent electric energy meter by utilizing a statistical simulation algorithm based on the annual fault rate of each component module in the intelligent electric energy meter, and determining the fault time of each component module in the intelligent electric energy meter;

the fault tree model building module is used for building a fault tree model based on the actual structure corresponding to each component module in the intelligent electric energy meter;

the fault time conversion module is used for converting the fault time of each component module in the intelligent electric energy meter into the integral fault time of the intelligent electric energy meter by using the fault tree model;

and the reliability function determining module is used for calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value.

Technical Field

The application relates to the field of intelligent electric energy meter service life prediction, in particular to a method and a device for predicting reliability of an intelligent electric energy meter.

Background

The intelligent electric energy meter is an important component of an intelligent power grid, has the characteristics of large installation amount and wide distribution area, and plays an important role in aspects of trade settlement and management, power utilization information acquisition, intelligent power utilization and the like. The stable and reliable operation of the intelligent electric energy meter is not only related to the construction progress of an intelligent power grid, but also related to the safe power utilization of vast power customers, so that the method has very important significance for the research on the reliability of the intelligent electric energy meter.

However, because the reliability evaluation standard quoted by the existing intelligent electric energy meter technical specification is too old, the reliability service life of the intelligent electric energy meter can only be judged whether to be qualified or not, the failure judgment principle in the test process is greatly different from the failure mode of the actual intelligent electric energy meter, and the reliability prediction sampling of the electric energy meter is not abundant, so that the prediction result is not close to the actual working condition.

Disclosure of Invention

The application provides a method and a device for predicting the reliability of an intelligent electric energy meter, which aim to solve the problem that the reliability of the intelligent electric energy meter is difficult to accurately predict by the existing method.

In a first aspect, the present application provides a method for predicting reliability of an intelligent electric energy meter, including the following steps:

acquiring the fault rate of each component module in the intelligent electric energy meter, and converting the fault rate of each component module into an annual fault rate;

based on the annual fault rate of each component module in the intelligent electric energy meter, performing time sampling on each component module in the intelligent electric energy meter by using a statistical simulation algorithm, and determining the fault time of each component module in the intelligent electric energy meter;

constructing a fault tree model based on the actual structure corresponding to each component module in the intelligent electric energy meter;

converting the fault time of each component module in the intelligent electric energy meter into the integral fault time of the intelligent electric energy meter by using the fault tree model;

and calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value.

In some embodiments of the present application, the composition module includes a display module, a clock module, a fee control module, a storage module, a time period and rate module, an alarm control module, and a 485 communication module; and, the obtaining the fault rate of each component module in the intelligent electric energy meter comprises:

acquiring the work failure rate of each component corresponding to each composition module in the intelligent electric energy meter;

according to the formulaCalculating the fault rate lambda of each component module in the intelligent electric energy meter_Si；

In the formula, λ_SiThe fault rate of the ith component module in the intelligent electric energy meter is obtained; lambda [ alpha ]_ijThe working failure rate of the jth component of the ith component module in the intelligent electric energy meter is determined, N is the number of the component modules in the intelligent electric energy meter, and M is the working failure rate of the jth component of the ith component module in the intelligent electric energy meter_iThe number of components in the ith component module in the intelligent electric energy is determined.

In some embodiments of the present application, the composition module comprises a power supply module and a metering module; and, the obtaining the fault rate of each component module in the intelligent electric energy meter comprises:

obtaining the acceleration test voltage, the normal working voltage and the acceleration rate constant of the voltage of each component module according to the formula V_AF＝exp[β×(V_s-V_n)]Calculating an electrical stress acceleration factor;

obtaining the percent relative humidity and absolute temperature of each of the constituent modules under normal operating conditions, the percent relative humidity and absolute temperature under accelerated stress conditions, and activation energy according to the formulaCalculating a temperature-humidity acceleration factor;

obtaining the accelerated test time of the electrical stress, the accelerated test time of the temperature-humidity and the number of the accelerated test samples according to the formula T_st＝m(t_v×V_AF+t_th×TH_A)_FCalculating the equivalent test time T of the acceleration test_stTaking the equivalent test time as the failure rate of each component module;

in the formula, V_AFIs an electrical stress acceleration factor, V_sTo accelerate the test voltage, V_nIs a normal working voltage, beta is an acceleration rate constant of the voltage; TH_AFAs a temperature-humidity accelerating factor, RH_μPercent relative humidity, RH, under normal operating conditions_SPercent relative humidity, T, for accelerated stress conditions_uIs the absolute temperature under normal operating conditions, T_SK is a Boltzmann constant, Ea is activation energy, n is a constant, and the value range is 1-12; t is_stEquivalent test time for accelerated test, m is the number of samples for accelerated test, t_vAccelerated test time for electrical stress, t_thThe test time is accelerated for temperature-humidity.

In some embodiments of the present application, the converting the failure rate of each of the component modules into an annual failure rate includes:

according to the formula lambda_a＝8760×λ_SiConverting the failure rate of each of the component modules into an annual failure rate;

or according to formulaConverting the failure rate of each of the component modules into an annual failure rate.

In some embodiments of the present application, the determining the failure time of each component module in the intelligent electric energy meter by sampling time of each component module in the intelligent electric energy meter based on the annual failure rate of each component module in the intelligent electric energy meter by using a statistical simulation algorithm includes:

setting a distribution function of each bottom event of the statistical simulation algorithm, and calculating an inverse function of each distribution function by utilizing the annual fault rate of each component module in the intelligent electric energy meter; each bottom event corresponds to one composition module;

and calculating the sampling time of each bottom event with faults based on the preset simulation times and the inverse function of each distribution function, and taking the sampling time of each bottom event with faults as the fault time of each component module in the intelligent electric energy meter.

In some embodiments of the present application, the converting the fault time of each component module in the intelligent electric energy meter into the overall fault time of the intelligent electric energy meter by using the fault tree model includes:

converting the fault tree model into a plurality of minimal cut sets, wherein each minimal cut set comprises a plurality of bottom events; each bottom event corresponds to one composition module;

acquiring the fault time of the bottom event which has the latest fault in each minimal cut set, and taking the fault time of the bottom event which has the latest fault as the fault time corresponding to the minimal cut set;

and sequencing each minimal cut set with faults according to the fault time of each minimal cut set, and taking the fault time of the minimal cut set with the first bit in the bit column as the integral fault time of the intelligent electric energy meter.

In some embodiments of the present application, the calculating the reliability value of the overall fault time of the intelligent electric energy meter in each time interval by using an interval statistical method includes:

sampling the whole fault time of the intelligent electric energy meter to obtain a plurality of sub-time samples, wherein one sub-time sample corresponds to the whole fault time;

determining a time range required by interval statistics based on time intermediate values corresponding to a plurality of sub-time samples;

equally dividing the time range into a plurality of time intervals according to a preset time interval;

counting the total number of times that a plurality of said sub-time samples fall into each of said time intervals;

according to the formula R (t)_r)＝1-m_rCalculating the reliability value R (t) of the overall fault time of the intelligent electric energy meter in each time interval_r)；

In the formula, m_rThe total number of times that the sub-time samples fall into the r-th time interval is N, which is a preset statistical number.

In some embodiments of the present application, said calculating a reliability function curve of the intelligent electric energy meter based on each of the reliability values includes:

performing curve fitting on the reliability value corresponding to each time interval to obtain a reliability function curve of the intelligent electric energy meter, wherein an expression of the reliability function curve is R (t) ═ 1.001exp (-0.0050t 3);

wherein t is the age time.

In some embodiments of the present application, the method further comprises:

obtaining the fitting degree of curve fitting, and judging whether the fitting degree accords with a fitting required value;

if the fitting degree does not accord with the fitting required value, increasing the number of time samples and the simulation times;

and executing the calculation step of the reliability function curve of the intelligent electric energy meter based on the new time sampling quantity and the new simulation times.

In a second aspect, the present application further provides an apparatus for predicting reliability of an intelligent electric energy meter, including:

the system comprises an annual fault rate determining module, a fault rate judging module and a fault rate judging module, wherein the annual fault rate determining module is used for acquiring the fault rate of each component module in the intelligent electric energy meter and converting the fault rate of each component module into the annual fault rate;

the time sampling module is used for sampling time of each component module in the intelligent electric energy meter by utilizing a statistical simulation algorithm based on the annual fault rate of each component module in the intelligent electric energy meter, and determining the fault time of each component module in the intelligent electric energy meter;

the fault tree model building module is used for building a fault tree model based on the actual structure corresponding to each component module in the intelligent electric energy meter;

the fault time conversion module is used for converting the fault time of each component module in the intelligent electric energy meter into the integral fault time of the intelligent electric energy meter by using the fault tree model;

and the reliability function determining module is used for calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value.

According to the technical scheme, the method and the device for predicting the reliability of the intelligent electric energy meter convert the fault rate of each component module in the intelligent electric energy meter into the annual fault rate; time sampling is carried out on each component module in the intelligent electric energy meter by utilizing a statistical simulation algorithm, and the fault time of each component module is determined; converting the fault time of each component module into the integral fault time of the intelligent electric energy meter by using a fault tree model; and calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value. Therefore, the method and the device provided by the embodiment of the invention can accurately predict the reliability of the intelligent electric energy meter by utilizing the statistical simulation algorithm and the fault tree model, and have rich sampling, so that the module fault rate is converted into the integral fault rate which is closer to the actual working condition.

Drawings

In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a flowchart of a method for predicting reliability of an intelligent electric energy meter according to an embodiment of the present invention;

fig. 2 is a data flow diagram of a reliability prediction method for an intelligent electric energy meter according to an embodiment of the present invention;

FIG. 3 is a flowchart of a method for determining a failure time for each component module according to an embodiment of the present invention;

fig. 4 is an algorithm flowchart of a method for predicting reliability of an intelligent electric energy meter according to an embodiment of the present invention;

FIG. 5 is a block diagram of a fault tree model provided by an embodiment of the present invention;

FIG. 6 is a flowchart of a method for determining overall failure time according to an embodiment of the present invention;

FIG. 7 is a flowchart of a method for calculating a reliability value for each time interval according to an embodiment of the present invention;

FIG. 8 is a diagram illustrating a section statistic method according to an embodiment of the present invention;

FIG. 9 is a diagram illustrating a simulation result graph according to an embodiment of the present invention;

FIG. 10 is a graph of a curve fit of simulation results provided by an embodiment of the present invention;

FIG. 11 is a flowchart of a method for determining a degree of fitting of curve fitting according to an embodiment of the present invention;

fig. 12 is a block diagram of a structure of an intelligent electric energy meter reliability prediction apparatus according to an embodiment of the present invention.

Detailed Description

Because the reliability evaluation standard quoted by the technical specification of the existing intelligent electric energy metering equipment is too old, the reliability service life of the electric energy meter can only be judged to be qualified or not, and the judgment principle of failure in the test process is greatly different from the failure mode of the actual intelligent electric energy meter, the reliability evaluation method quoted by the technical specification of the intelligent electric energy metering equipment (the intelligent electric energy meter) has a difficult expected effect on the reliability quality guarantee of the intelligent electric energy metering equipment, and has extremely limited effect on the reliability prediction of the intelligent electric energy metering equipment and the deployment of the maintenance and replacement work.

Therefore, in order to solve the problem that the reliability of the intelligent electric energy meter is difficult to predict and to improve the limitation that the conventional prediction is limited due to the fact that sampling is not abundant and sample randomness is small, the embodiment of the invention provides the reliability prediction method of the intelligent electric energy meter, which can accurately predict the reliability of the intelligent electric energy meter, can effectively solve the difficulty that the reliability of the intelligent electric energy metering device is difficult to predict, and solves the problem that the module failure rate is converted into the whole failure rate which is not close to the actual working condition due to the fact that the conventional reliability prediction of the electric energy meter is not abundant. In addition, according to the reliability prediction of the intelligent electric energy meter, the maintenance and the cycle rotation can be scientifically and effectively arranged.

Fig. 1 is a flowchart of a method for predicting reliability of an intelligent electric energy meter according to an embodiment of the present invention; fig. 2 is a data flow diagram of the reliability prediction method for the intelligent electric energy meter according to the embodiment of the invention. Referring to fig. 1 and fig. 2, a method for predicting reliability of an intelligent electric energy meter according to an embodiment of the present invention uses a prediction presentation to predict reliability of the intelligent electric energy meter, where the prediction method includes the following steps:

and S1, acquiring the fault rate of each component module in the intelligent electric energy meter, and converting the fault rate of each component module into an annual fault rate.

In order to facilitate the prediction of the reliability of the intelligent electric energy meter, a reliability value needs to be calculated based on the fault rates of a plurality of constituent modules in the intelligent electric energy meter, and the reliability function curve of the intelligent electric energy meter is simulated by the obtained reliability value so as to represent the reliability of the intelligent electric energy meter.

The intelligent electric energy meter comprises an MCU module, a power supply module and a metering module, wherein the MCU module comprises a display module, a clock module, a charge control module, a storage module, a time period and rate module, an alarm control module and a 485 communication module. Based on different composition modules, the corresponding failure rate acquisition modes are different.

In some embodiments, when the failure rates of the display module, the clock module, the fee control module, the storage module, the time period and rate module, the alarm control module and the 485 communication module in the intelligent electric energy meter are obtained, the SR-332 manual is consulted according to element stress. Specifically, the process of acquiring the failure rate of each component module in the intelligent electric energy meter is executed by the prediction program, and the process comprises the following steps:

and 111, acquiring the work failure rate of each component corresponding to each component module in the intelligent electric energy meter.

Step 112, according to formulaCalculating the fault rate lambda of each component module in the intelligent electric energy meter_Si。

In the formula, λ_SiThe fault rate of the ith component module in the intelligent electric energy meter is obtained; lambda [ alpha ]_ijThe working failure rate of the jth component of the ith component module in the intelligent electric energy meter is determined, N is the number of the component modules in the intelligent electric energy meter, and M is the working failure rate of the jth component of the ith component module in the intelligent electric energy meter_iThe number of components in the ith component module in the intelligent electric energy is determined.

The intelligent electric energy meter comprises a plurality of component modules, and each component module consists of a plurality of components, so that the fault rate of the corresponding component module can be represented by the working failure rate of each component.

According to operation statistics or element stress method, working failure rate lambda of display module, clock module, fee control module, storage module, time period and rate module, alarm control module and 485 communication module is obtained_ij。

According to an element stress method, an SR-332 handbook is consulted, and a work failure rate prediction model of each element is as follows:

in the formula, λ_ijFor the work failure rate of the jth component of the ith component module in the intelligent electric energy meter,the general steady-state failure rate of the jth component of the ith component module in the intelligent electric energy meter can be obtained based on a component type manual,the environmental stress factor of the jth component of the ith component module in the intelligent electric energy meter,the quality grade factor of the jth component of the ith component module in the intelligent electric energy meter,the electric stress factor of the jth component of the ith component module in the intelligent electric energy meter,and the temperature stress factor of the jth component of the ith component module in the intelligent electric energy meter.

After the working failure rate of each component in each component module in the intelligent electric energy meter is determined, the working failure rate can be determined according to the formulaCalculating the fault rate lambda of the ith component module in the intelligent electric energy meter_Si。

In some embodiments, when the failure rates of the power supply module and the metering module in the intelligent electric energy meter are obtained, the failure rates of the power supply module and the metering module are equivalent by obtaining the equivalent test time of the accelerated test in a life accelerated test mode. Specifically, the prediction program, when executing a process of acquiring the failure rate of each component module in the intelligent electric energy meter, includes the following steps:

step 121, obtaining the acceleration test voltage, the normal working voltage and the acceleration rate of the voltage of each component moduleNumber according to formula V_AF＝exp[β×(V_s-V_n)]And calculating the electric stress acceleration factor.

Step 122, obtaining the percent relative humidity and absolute temperature of each component module under normal operating conditions, the percent relative humidity and absolute temperature under accelerated stress conditions, and the activation energy for each component module according to the formulaA temperature-humidity acceleration factor is calculated.

Step 123, obtaining the electric stress accelerated test time, the temperature-humidity accelerated test time and the number of accelerated test samples according to the formula T_st＝m(t_v×V_AF+t_th×TH_AF) Calculating the equivalent test time T of the acceleration test_stAnd taking the equivalent test time as the failure rate of each component module.

In the formula, V_AFIs an electrical stress acceleration factor, V_sTo accelerate the test voltage, V_nIs a normal working voltage, beta is an acceleration rate constant of the voltage; TH_AFAs a temperature-humidity accelerating factor, RH_μPercent relative humidity, RH, under normal operating conditions_SPercent relative humidity, T, for accelerated stress conditions_uIs the absolute temperature under normal operating conditions, T_SK is a Boltzmann constant, Ea is activation energy, n is a constant, and the value range is 1-12; t is_stEquivalent test time for accelerated test, m is the number of samples for accelerated test, t_vAccelerated test time for electrical stress, t_thThe test time is accelerated for temperature-humidity.

When the failure rates of the power supply module and the metering module are calculated, a service life acceleration test mode is adopted, and the service life acceleration test is specifically carried out under the temperature and humidity condition and the voltage condition. Therefore, it is necessary to determine an electrical stress acceleration factor, which is an acceleration factor required for the life acceleration test under the voltage condition, and a temperature-humidity acceleration factor, which is an acceleration factor required for the life acceleration test under the temperature and humidity conditions.

When determining the electric stress acceleration factors of the power supply module and the metering module, calculation is performed by using an electric stress model, such as an Eying model. Acquiring accelerated test voltages V respectively corresponding to a power supply module and a metering module under a voltage condition_sNormal operating voltage V_nAnd an acceleration rate constant beta of the voltage according to formula V_AF＝exp[β×(V_s-V_n)]Calculating the electrical stress acceleration factor V of the power supply module_AFAnd electrical stress acceleration factor V of the metering module_AF。

In determining the temperature-humidity acceleration factors of the power supply module and the metering module, an accelerated life test is performed using a temperature-humidity model, such as a Peck module. Acquiring the percentage relative humidity RH of the power supply module and the metering module under the normal working condition respectively corresponding to the temperature-humidity condition_μAnd absolute temperature T_uPercent relative humidity RH under accelerated stress conditions_SAnd absolute temperature T_SAnd activation energy Ea according to formulaCalculating temperature-humidity acceleration factor TH of power supply module_AFAnd a temperature-humidity acceleration factor TH of the metering module_AF。

Wherein the boltzmann constant k is 8.617 × 10^-5V/K; ea is activation energy expressed in electron volts (Ea is in the range of 0.3 to 1.5, and a typical value Ea is 0.9); n is a constant (the value range of n is usually 1-12, and the typical value n is 3); ea and n are two coefficients of the model.

After determining the electric stress acceleration factor of the power supply module and the temperature-humidity acceleration factor of the metering module under the voltage condition and the temperature-humidity acceleration factor under the temperature-humidity condition respectively, the method can be carried out according to the formula T_st＝m(t_v×V_AF+t_th×TH_AF) Calculating the equivalent test time T of the power supply module in the acceleration test_stAnd equivalent test time T of the metering module in the acceleration test_st. Equivalent test time T of power supply module in accelerated test_stAs the failure rate of the power supply module, the equivalent test of the metering module in the acceleration test is carried outTest time T_stAs the failure rate of the metering module.

In order to accurately determine the reliability function curve of the intelligent electric energy meter, in this embodiment, the age time is used as a time condition, and the reliability function curve is determined based on a plurality of age times. Therefore, the fault rate required for determining the reliability value is converted into the annual fault rate by taking the annual fault rate as a basic data source, namely determining the fault rate of each component module in the intelligent electric energy meter according to the embodiment.

Because the failure rate of different component modules is calculated in different manners, the conversion formula according to which the failure rate is converted into the annual failure rate is different. Specifically, the prediction program, in executing a process of converting the failure rate of each component module into the annual failure rate, includes the steps of:

step 131, according to the formula lambda_a＝8760×λ_SiThe failure rate of each component module is converted into an annual failure rate.

Step 132, alternatively, according to formulaThe failure rate of each component module is converted into an annual failure rate.

When the failure rates of the display module, the clock module, the charge control module, the storage module, the time period and rate module, the alarm control module and the 485 communication module are obtained based on operation statistics or an element stress method, the conversion formula of the annual failure rate is lambda_a＝8760×λ_SiI.e. failure rate lambda according to the ith component module_SiConverting the failure rate of the ith component module into an annual failure rate lambda_a。

When the failure rates of the power supply module and the metering module are obtained based on the accelerated life test, the conversion formula of the annual failure rate isI.e. according to the equivalent test time T of the power supply module and the metering module_stRespectively converting the fault rates of the power supply module and the metering module into annual fault rate lambda_a。

And S2, based on the annual fault rate of each component module in the intelligent electric energy meter, performing time sampling on each component module in the intelligent electric energy meter by using a statistical simulation algorithm, and determining the fault time of each component module in the intelligent electric energy meter.

And continuously sampling for multiple times to obtain the fault time of each component module by using the obtained annual fault rate of each component module. In some embodiments, in time sampling, the time sampling is performed a plurality of times based on a statistical simulation algorithm (Monte-Carlo).

FIG. 3 is a flowchart of a method for determining a failure time for each component module according to an embodiment of the present invention; fig. 4 is an algorithm flowchart of the reliability prediction method for the intelligent electric energy meter according to the embodiment of the present invention. Specifically, referring to fig. 3 and 4, in the process of executing the prediction program, based on the annual fault rate of each component module in the intelligent electric energy meter, using a statistical simulation algorithm to perform time sampling on each component module in the intelligent electric energy meter, and determining the fault time of each component module in the intelligent electric energy meter, the method includes the following steps:

s21, setting a distribution function of each bottom event of the statistical simulation algorithm, and calculating an inverse function of each distribution function by utilizing the annual fault rate of each component module in the intelligent electric energy meter; each bottom event corresponds to a component module.

And S22, calculating the sampling time of each bottom event fault based on the preset simulation times and the inverse function of each distribution function, and taking the sampling time of each bottom event fault as the fault time of each component module in the intelligent electric energy meter.

When a fault occurs, the faults which can be generated by the display module, the clock module, the charge control module, the storage module, the time period and rate module, the alarm control module, the 485 communication module, the power supply module and the metering module of each component module in the intelligent electric energy meter respectively correspond to a display fault, a clock fault, a charge control fault, a storage fault, a time period and rate fault, an alarm control fault, a 485 communication fault, a power supply fault and a metering fault.

The faults of the above-mentioned component modules are cyclically time-sampled a plurality of times based on a statistical simulation algorithm (Monte-Carlo) to determine the fault time of each fault.

First, a bottom event G is set_iThe distribution function of (a) is: f_i(t)＝1-exp(-λ_at). Each bottom event corresponds to one composition module, namely, the bottom event replaces the corresponding composition module to serve as a calculation unit of the statistical simulation algorithm.

The annual fault rate lambda of each component module_aSubstituting the above formula and solving each distribution function F_i(t) inverse function t ═ F_i ^-1(y) successive multiple introductions of [0,1 ]]Distributed random number Z, bottom event G_iSampling time t of occurrence of failure_i，t_i＝F_i ^-1(Z). The number i of times of the random number is a preset simulation number, and i is 1-N.

And (3) sampling time of preset simulation times based on a statistical simulation algorithm (Monte-Carlo), and taking the calculated sampling time of each bottom event as the fault time of the corresponding component module.

And S3, constructing a fault tree model based on the actual structure corresponding to each component module in the intelligent electric energy meter.

According to the working principle and the basic module of the intelligent electric energy meter, common fault types of the electric energy meter take element and module faults as event carriers, the failure of the intelligent electric energy meter is set as a top event T, an intermediate event is S, and bottom events are respectively G₁，G₂，G₃……G_nAnd constructing a fault tree model.

Fig. 5 is a structural diagram of a fault tree model according to an embodiment of the present invention. Referring to fig. 5, T: the intelligent electric energy meter is invalid; s: MCU module failure; g₁: failure of metering module, G₂: failure of power supply module, G₃: display failure, G₄: clock failure, G₅: charge control failure, G₆: storage failure, G₇: period and rate of failure, G₈: alarm control of failure, G₉: zero clearing fault, G₁₀: a communication failure.

And S4, converting the fault time of each component module in the intelligent electric energy meter into the whole fault time of the intelligent electric energy meter by using the fault tree model.

After the fault time of each component module in the intelligent electric energy meter is determined, the fault time of each component module needs to be converted into the whole fault time of the intelligent electric energy meter, and the reliability of the intelligent electric energy meter is convenient to predict.

Fig. 6 is a flowchart of a method for determining an overall failure time according to an embodiment of the present invention. Referring to fig. 6, the prediction program, in executing a process of converting the fault time of each component module in the intelligent electric energy meter into the overall fault time of the intelligent electric energy meter by using the fault tree model, includes the following steps:

s41, converting the fault tree model into a plurality of minimum cut sets, wherein each minimum cut set comprises a plurality of bottom events; each bottom event corresponds to a component module.

And S42, acquiring the fault time of the bottom event which has the latest fault in each minimum cut set, and taking the fault time of the bottom event which has the latest fault as the fault time corresponding to the minimum cut set.

And S43, sorting the minimum cut sets with faults according to the fault time of each minimum cut set, and taking the fault time of the minimum cut set with the first bit in the bit column as the whole fault time of the intelligent electric energy meter.

To facilitate the transformation, the fault tree model is functionalized into a form consisting of a minimal cut set M. Each minimal cut set M includes a number of base events, i.e., includes a number of constituent modules. Then, the minimal cut set M will be determined to be failed after the latest failed bottom event in each minimal cut set M.

Therefore, the failure time t of the minimum cut set M can be determined_mEqual to the time of occurrence of the bottom event of the latest fault in the cut set: t is t_m＝max(t₁,t₂,t₃,…t_g). Respectively obtaining the occurrence time t of each bottom event in a random sampling mode₁～t_gThe sampling time t of the bottom event is realized by using the fault time of the bottom event which occurs latest as the fault time of the corresponding minimal cut set_iTime of failure to minimum cut set t_mThe transformation of (3). Based onIn the above manner, it can be determined that the failure time of each minimal cut set is t_m1～t_mh。

In the formula, if a certain minimal cut set M contains g bottom events, the corresponding failure time t_mContaining g time components, i.e. t_gThe occurrence time of the g-th bottom event in the minimal cut set M.

Under the fault tree model, as any one minimal cut set M corresponding to the intelligent electric energy meter fails, the failure of the intelligent electric energy meter corresponding to the top event T can be determined to be inevitable, namely the intelligent electric energy meter fails, so that the failure time (integral failure time) T of the intelligent electric energy meter_sDetermined by the smallest cut set that failed earliest.

The method comprises the steps that h minimum cut sets M are assumed to correspond to the intelligent electric energy meter, and the fault time of the h minimum cut set M is t_mhDetermining the failure time (integral failure time) t of the intelligent electric energy meter_s＝min(t_m1,t_m2,t_m3…t_mh). The fault time of the minimum cut set corresponding to the earliest fault time is used as the whole fault time of the intelligent electric energy meter, so that the sampling time t of the minimum cut set is realized_mSampling time t to system fault_sThe transformation of (3).

When the fault time of the minimal cut set with the earliest fault is determined, each minimal cut set can be sorted according to the fault time of each minimal cut set, and the fault time of the minimal cut set with the first bit of the bit column is used as the whole fault time of the intelligent electric energy meter.

According to the mode, based on the preset simulation times, the integral fault time of the intelligent electric energy meter is sampled for multiple times in a circulating mode in sequence until the current execution times i reach the preset simulation times N, and at the moment, the integral fault time obtained in multiple simulation processes is used as an integral fault time sampling vector t_s。

S5, calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value.

The integral fault time of the intelligent electric energy meters can be obtained by sampling the time of the preset simulation times based on a statistical simulation algorithm (Monte-Carlo), so that interval statistics can be carried out on the integral fault time to determine the reliability value in each time interval, and then a reliability function curve of the intelligent electric energy meter is obtained by fitting.

Fig. 7 is a flowchart of a method for calculating a reliability value for each time interval according to an embodiment of the present invention. Referring to fig. 7, in particular, the process of the prediction program in executing the process of calculating the reliability value of the overall fault time of the intelligent electric energy meter in each time interval by using the interval statistics method includes the following steps:

s51, sampling the whole fault time of the intelligent electric energy meter to obtain a plurality of sub-time samples, wherein one sub-time sample corresponds to the whole fault time.

And S52, determining a time range required by interval statistics based on the time intermediate values corresponding to the plurality of sub time samples.

And S53, equally dividing the time range into a plurality of time intervals according to the preset time interval.

And S54, counting the total times of the sub time samples falling into each time interval.

S55, according to formula R (t)_r)＝1-m_rCalculating the reliability value R (t) of the overall fault time of the intelligent electric energy meter in each time interval_r) In the formula, m_rThe total number of times that the sub-time samples fall into the r-th time interval is N, which is a preset statistical number.

When time sampling of preset simulation times (N times) is carried out based on a statistical simulation algorithm (Monte-Carlo), the obtained integral fault time of a plurality of intelligent electric energy meters is used as a prediction sample, namely N sub-time samples are obtained and are respectively t_s1，t_s2，t_s3，…t_snOne sub-time sample corresponds to one overall time to failure.

Sequencing the whole fault time corresponding to each sub-time sample from small to large, and taking the whole fault time positioned in the middle as the median t of the N sub-time samples_med. Based onTime mean value t_medAccording to the formula T_max＝3t_medDetermining the time range (0, T) required for interval statistics_max)。

When the interval statistics method is used for interval statistics, the time range (0, T)_max) W is equally divided according to a preset time interval, wherein the preset time interval is delta f ═ T_maxW time intervals are obtained.

Fig. 8 is a schematic diagram of a section statistical method according to an embodiment of the present invention. Referring to fig. 8, the start point and the end point of the r-th time interval are 0 and t, respectively_rR × Δ f, and t_r-t_r-1Δ f. That is, the starting point of each time interval is equal to 0, and the data range of the next time interval includes the data range of the previous time interval.

From a first time interval t₁(r 1) starting to make cycle statistics, counting several sub-time samples t_s1，t_s2，t_s3，…t_snTotal number of times of falling into each time interval in m_rIndicates that a certain sub-time sample falls into (0, t)_r) The total number of time intervals, i.e. a certain sub-time sample t_snIn total, fall into how many time intervals.

Using the total degree corresponding to each sub-time sample and the formula R (t)_r)＝1-m_rCalculating the reliability value R (t) of the whole fault time of the intelligent electric energy meter in the corresponding time interval_r) I.e. nth sub-time sample t_snThe integral fault time of the corresponding intelligent electric energy meter is in the corresponding time interval t_rnReliability value of R (t)_rn)＝1-m_rnand/N. Wherein N is a predetermined number of simulation times, m_rnFor the nth sub-time sample t_snFalls into the nth t_rTotal number of time intervals.

The mark of the counting end is that counting is carried out to the last time interval, namely the time interval r of the current counting is greater than or equal to the total number W of the time intervals.

Fig. 9 is a schematic diagram of a simulation result graph provided in the embodiment of the present invention. In FIG. 9, the horizontal axis represents the year time t, the unit is year, and the central axis represents the reliability value R (t_r). Based on the reliability value of the overall fault time of the N intelligent electric energy meters in each time interval determined by the method, a simulation result curve graph can be drawn.

After the reliability value of the overall fault time of the N intelligent electric energy meters in each time interval is determined, curve fitting can be carried out according to the N reliability data, so that the reliability function curves of the straight male electric energy meters in different operation years can be fitted.

Specifically, the process of calculating the reliability function curve of the intelligent electric energy meter based on each reliability value by the prediction program comprises the following steps: performing curve fitting based on the reliability value corresponding to each time interval to obtain a reliability function curve of the intelligent electric energy meter, wherein the expression of the reliability function curve is R (t) ═ 1.001exp (-0.0050t 3); wherein t is the age time.

In this embodiment, MATLAB is used to perform curve fitting on the simulation result (as shown in fig. 8), and the fitting is performed by using an Exponential (Exponential) fitting, so as to obtain a reliability function curve of the intelligent electric energy meter, where an expression of the reliability function curve is r (t) ═ 1.001exp (-0.0050t 3).

Fig. 10 is a curve fitting graph of simulation results provided by the embodiment of the present invention. The curve fitting graph shown in fig. 10 is a fitting graph, i.e., a reliability function curve, obtained by curve fitting the simulation result curve graph shown in fig. 9.

In order to ensure whether the predicted reliability function curve of the intelligent electric energy meter is accurate or not, whether the fitting degree of the reliability function curve meets the fitting requirement value or not needs to be judged.

Fig. 11 is a flowchart of a method for determining a degree of fitting of curve fitting according to an embodiment of the present invention. Specifically, referring to fig. 11, the method for predicting the reliability of the intelligent electric energy meter by the prediction program further includes:

and S61, obtaining the fitting degree of curve fitting, and judging whether the fitting degree accords with the fitting requirement value.

And S62, if the fitting degree does not accord with the fitting requirement value, increasing the number of time samples and the simulation times.

And S63, executing the calculation step of the reliability function curve of the intelligent electric energy meter based on the new time sampling quantity and the new simulation times.

Based on the simulation result curve fitting graph shown in fig. 10, the fitting degree of the curve fitting can be read, and whether the fitting degree meets the fitting requirement value or not is judged. If not, the number of time samples and the simulation times are increased, namely the preset simulation times N and the number of the segments W of the time range are increased, and a new number of time samples (the number of the segments of the time range) W 'and a new simulation times N' are obtained.

And executing the calculation step of the reliability function curve of the intelligent electric energy meter based on the new time sampling quantity and the new simulation times, namely repeating the steps of the previous embodiment, and calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval again and fitting to obtain the reliability function curve.

For example, from the curve fitting graph of the simulation result shown in fig. 10, it can be read that R-square:0.9998 and RMSE:0.003825 satisfy the exponential distribution, that is, meet the fitting requirement value, and the fitting degree is high, and the reliability function curve R (t) is 1.001exp (-0.0050t3) without increasing the simulation number N and the interval segment number W.

According to the simulation result, the working reliability of the intelligent electric energy meter can be fitted into an exponential distribution function, and the reliability of the intelligent electric energy meter in different working years can be obtained by utilizing the function. If the reliability of the intelligent electric meter which is put into operation for 10 years is 0.9519, it is indicated that 4.8% of the intelligent electric meters in the intelligent electric meters counted in the batch may have faults when the working life reaches 10 years. Based on the method, the maintenance period Tp of the intelligent electric energy meter can be predicted according to the reliability function curve.

Therefore, the method provided by the embodiment aims at solving the problem that the reliability and the service life of the existing intelligent electric energy metering equipment are difficult to accurately predict due to the technical specification of the existing intelligent electric energy metering equipment, and firstly, the method simulates the occurrence time of a sampling bottom event by using a Monte-Carlo method (a statistical simulation algorithm) on the basis of the annual fault rate of each component module of the intelligent electric energy meter acquired by an element stress method or a service life acceleration test; then constructing a fault tree model of the intelligent electric energy meter, and converting the fault time of each component module into the integral fault time of the intelligent electric energy meter by using the fault tree model; and counting the use interval of the simulation sample to obtain the reliability value of each time interval of the intelligent electric energy meter, and fitting to obtain a reliability function curve R (t). The invention uses Monte-Carlo method, enriches sampling, makes sampling more random, provides guarantee for model accuracy, and makes the influence of each component module fault in fault tree model to system whole more approximate to actual situation by combining with fault tree analysis method. The method provides reference for power grid enterprises to modularly analyze the fault condition of the intelligent electric energy meter and scientifically make a maintenance decision.

According to the technical scheme, the reliability prediction method for the intelligent electric energy meter provided by the embodiment of the invention converts the fault rate of each component module in the intelligent electric energy meter into the annual fault rate; time sampling is carried out on each component module in the intelligent electric energy meter by utilizing a statistical simulation algorithm, and the fault time of each component module is determined; converting the fault time of each component module into the integral fault time of the intelligent electric energy meter by using a fault tree model; and calculating the reliability value of the whole fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculating the reliability function curve of the intelligent electric energy meter based on each reliability value. Therefore, the method provided by the embodiment of the invention can accurately predict the reliability of the intelligent electric energy meter by utilizing the statistical simulation algorithm and the fault tree model, has rich sampling, and enables the module fault rate to be converted into the overall fault rate which is closer to the actual working condition.

Fig. 12 is a block diagram of a structure of an intelligent electric energy meter reliability prediction apparatus according to an embodiment of the present invention. Referring to fig. 12, the present application further provides an intelligent electric energy meter reliability prediction apparatus for executing the intelligent electric energy meter reliability prediction method shown in fig. 1, where the apparatus includes:

the system comprises an annual fault rate determining module 10, a fault rate determining module and a fault rate judging module, wherein the annual fault rate determining module is used for acquiring the fault rate of each component module in the intelligent electric energy meter and converting the fault rate of each component module into the annual fault rate;

the time sampling module 20 is used for sampling time of each component module in the intelligent electric energy meter by using a statistical simulation algorithm based on the annual fault rate of each component module in the intelligent electric energy meter, and determining the fault time of each component module in the intelligent electric energy meter;

the fault tree model building module 30 is used for building a fault tree model based on the actual structure corresponding to each component module in the intelligent electric energy meter;

the fault time conversion module 40 is used for converting the fault time of each component module in the intelligent electric energy meter into the integral fault time of the intelligent electric energy meter by using the fault tree model;

the reliability function determining module 50 is configured to calculate a reliability value of the overall fault time of the intelligent electric energy meter in each time interval by using an interval statistical method, and calculate a reliability function curve of the intelligent electric energy meter based on each reliability value.

In a specific implementation, the present invention further provides a computer storage medium, where the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment of the method for predicting reliability of an intelligent electric energy meter provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).

Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.

The same and similar parts in the various embodiments in this specification may be referred to each other. Particularly, for the embodiment of the reliability prediction device of the intelligent electric energy meter, since the embodiment is basically similar to the embodiment of the method, the description is simple, and the relevant points can be referred to the description in the embodiment of the method.

The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention.