A method for evaluating the reliability of intelligent circuit breaker control circuit

Abstract

The present invention discloses a method for evaluating the reliability of an intelligent circuit breaker control circuit. Through experimental analysis, a failure rate calculation model for each component and module is established, and the key components and key modules of the product are determined by comparing the failure rates. A multidimensional evaluation index model is used to analyze multiple characteristic parameters in the product closing process, and finally the performance degradation monitoring parameters of the control circuit are selected. Then, an accelerated degradation test with constant temperature stress is used to analyze the two-stage degradation trend and degradation change point, and a control circuit performance degradation model based on a two-stage Wiener process is established. The degradation model distribution parameters and degradation change points are estimated by maximum likelihood estimation and Schwarz criterion, respectively, and the degradation model distribution parameters of normal use conditions are derived according to the Arrhenius model to obtain a more realistic degradation situation.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment, and in particular to a method for evaluating the reliability of a control circuit of an intelligent circuit breaker. Background Art

[0002] At present, intelligent circuit breakers are one of the most widely used low-voltage control electrical products in power systems. They can quickly cut off the source of faults, ensure the reliability of power supply, and reduce the operating costs of distribution networks, thereby ensuring the reliability and safety of power systems.

[0003] The internal structure of the intelligent circuit breaker is mainly composed of two parts: the traditional contact mechanical system and the intelligent electronic system. The intelligent electronic system includes the control circuit and the gear motor. The intelligent circuit breaker will not be frequently operated during normal use. The degradation process of the components of the contact mechanical system is slow and basically not affected by the environment. The control circuit works under stress such as temperature cycle and power cycle for a long time. The components that are more sensitive to these stresses will inevitably have parameter drift or even degradation failure, affecting the performance of the control circuit and thus the overall reliability of the circuit breaker.

[0004] At present, the relevant research on performance degradation and reliability analysis of low-voltage circuit breaker components, especially control circuits, is still immature. Most performance degradation modeling methods only consider that electronic products obey a single-stage degradation process, that is, they assume that the performance degradation rate of electronic products remains unchanged. However, in engineering practice, due to the complexity of the internal failure mechanism of electronic products and the dynamic nature of external environmental factors, the macroscopic degradation process of electronic products is more likely to present a two-stage or even multi-stage degradation characteristic. This can easily lead to the inability to timely monitor the current performance degradation state of the control circuit and potential fault hazards, affecting the operating reliability of the intelligent circuit breaker and load equipment. Summary of the invention

[0005] In order to overcome the shortcomings of the above technologies, the present invention provides a method for evaluating the reliability of a control circuit of an intelligent circuit breaker. The key circuits and key components are determined by a failure rate calculation model, and the degradation trend and degradation change point of the control circuit are analyzed by degradation tests. A control circuit performance degradation model based on a two-stage Wiener process is established, and the distribution parameters of the degradation model and the degradation change point are estimated. The distribution parameters of the degradation model under normal operating conditions are derived according to the Arrhenius model, and a more realistic degradation situation is obtained, thereby evaluating the reliability of the intelligent circuit breaker and making the evaluation result reliable.

[0006] The technical solution adopted by the present invention to overcome its technical problems is: the present invention proposes a method for evaluating the reliability of the control circuit of an intelligent circuit breaker, the intelligent circuit breaker at least includes a control circuit and a contact mechanical system, and the method for evaluating the reliability of the control circuit of the intelligent circuit breaker includes: S1, dividing the control circuit into several functional modules and establishing a failure rate calculation model, thereby determining the key functional modules and key failure elements of the control circuit; S2, selecting a monitoring signal for evaluating the degradation state of the control circuit based on the key functional modules, and extracting multidimensional candidate feature quantities based on the monitoring signals to obtain the optimal degradation feature parameters; S3, analyzing the degradation evolution trend and degradation change point of the control circuit based on the accelerated degradation test and the optimal degradation feature parameters. S4, establishing a control circuit performance degradation model of a two-stage Wiener process based on the analysis of the degradation evolution trend and the degradation change point, and estimating the control circuit performance degradation model parameters and degradation change points of the accelerated degradation test based on the maximum likelihood method and the Schwarz information criterion; S5, obtaining the reliability function and average life of the normal use condition according to the Arrhenius model and the control circuit performance degradation model.

[0007] Furthermore, the failure rate calculation model is as follows:

[0008] ,

[0009] ,

[0010] ,

[0011] in, To control the failure rate of the circuit, is the failure rate of the ith functional module or the failure rate of the ith component of the circuit of the functional module, is the failure rate of the jth component of each functional module, is the mean time between failures of the control circuit, is the reliability function, where n is the number of functional modules and m is the number of components of the i-th functional module.

[0012] Furthermore, the S2 specifically includes: S21, selecting a monitoring signal for evaluating the degradation state of the control circuit based on the closing process of the intelligent circuit breaker and the key functional modules; S22, evaluating and scoring the multidimensional candidate feature quantities extracted from the collected monitoring signals based on the feature evaluation model to obtain the optimal degradation feature parameters.

[0013] The priority of the degradation state monitoring signal is considered in combination with the circuit topology, the electrical characteristics of the monitoring signal, the measurement difficulty and the measurement cost, and the monitoring signal that can best characterize the degradation state of the control circuit performance is selected.

[0014] Furthermore, the S3 specifically includes: S31, subjecting a number of samples to accelerated degradation tests under different temperature stresses to obtain the degradation evolution trend of the optimal degradation characteristic parameters; S32, using the least squares method to fit the degradation evolution trend; S33, using the characteristic sequence important point fixed segmentation algorithm of the trend turning distance to divide the fitted degradation evolution trend into a first degradation stage, a second degradation stage, and obtain the degradation change point.

[0015] Furthermore, the two-stage Wiener process degradation model is as follows:

[0016] ,

[0017] In the formula, X 01、 X 02 are the initial degradation amounts of the first and second degradation stages, respectively; , are the drift parameters of the first and second degradation stages, respectively, indicating the degradation rate of each stage; , are the diffusion parameters of the first and second degradation stages respectively, and B(t) is the standard Brownian motion.

[0018] Furthermore, the maximum likelihood method is used to estimate the parameters of the two-stage Wiener process degradation model. , , , Make an estimate.

[0019] Furthermore, the change point estimation method based on the Schwarz information criterion is used to estimate the model distribution parameters used to estimate the parameters of the two-stage Wiener process degradation model, and the change point position is obtained.

[0020] Furthermore, the change point estimation method based on the Schwarz information criterion is used to estimate the model distribution parameters used to estimate the parameters of the two-stage Wiener process degradation model, and obtain the change point position, specifically including: S431, defining the Schwarz information criterion; S432, for each sample degradation data, determining the interval where the degradation change point is located based on the Schwarz information criterion; S433, determining the estimated value of the degradation change point based on the minimum residual sum of squares criterion; S434, using the maximum likelihood method to estimate the estimated value of the degradation change point distribution parameter to obtain the change point position.

[0021] Furthermore, for each sample degradation data, determining the interval where the degradation change point is located based on the Schwarz information criterion specifically includes: S4321, proposing a hypothesis test in combination with the Schwarz information criterion; S4322, determining the degradation change point and estimating the interval based on the hypothesis test and the calculated Schwarz information criterion.

[0022] Furthermore, the S5 obtains the reliability function and average life under normal use conditions based on the Arrhenius model and the control circuit performance degradation model, specifically including: S51, calculating the estimated values ​​of the model parameters of the two-stage Wiener process degradation model under several temperature stresses, and reducing the parameters of the degradation model under normal use conditions based on the Arrhenius acceleration model and the least squares method S52, substituting the model parameters under normal use conditions into the two-stage Wiener process degradation model to establish a reliability function.

[0023] The degradation model distribution parameters under normal operating conditions are derived based on the Arrhenius model to obtain a more realistic degradation situation.

[0024] The beneficial effects of the present invention are:

[0025] 1. Through experimental analysis, the working principle of the intelligent circuit breaker control circuit was studied, and the failure rate calculation model of each component and module was established. By comparing the failure rates, the key components and key modules of the product were determined;

[0026] 2. Use a multi-dimensional evaluation index model to analyze multiple characteristic parameters during the product closing process, and finally select the performance degradation monitoring parameters of the control circuit;

[0027] 3. The degradation trend and degradation change point are analyzed by accelerated degradation test under constant temperature stress, and a control circuit performance degradation model based on a two-stage Wiener process is established;

[0028] 4. Estimate the distribution parameters and degradation change points of the degradation model through maximum likelihood estimation and Schwarz criterion;

[0029] 5. Based on the Arrhenius model, the degradation model distribution parameters under normal operating conditions are derived to obtain a more realistic degradation situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a method for evaluating the reliability of a control circuit of an intelligent circuit breaker provided by the present invention;

[0031] Figure 2 A bar diagram of module failure rate of an intelligent circuit breaker control circuit provided by the present invention;

[0032] Figure 3 A bar diagram of the failure rate of components of an intelligent circuit breaker control circuit provided by the present invention;

[0033] Figure 4 A control circuit monitoring signal waveform of a closing process of an intelligent circuit breaker provided by the present invention;

[0034] Figure 5 A histogram showing the comprehensive scoring of characteristic parameters of an intelligent circuit breaker control circuit provided by the present invention;

[0035] Figure 6 A schematic diagram of the evolution trend of degradation characteristics of an intelligent circuit breaker control circuit provided by the present invention at 105°C;

[0036] Figure 7 A schematic diagram of the evolution trend of degradation characteristics of an intelligent circuit breaker control circuit at 95°C provided by the present invention;

[0037] Figure 8 A schematic diagram of the evolution trend of degradation characteristics of an intelligent circuit breaker control circuit at 85°C provided by the present invention;

[0038] Fig. 9 A schematic diagram of a reliability function curve of an intelligent circuit breaker control circuit provided by the present invention. DETAILED DESCRIPTION

[0039] First, some abbreviations and key terms mentioned in the present invention are explained.

[0040] Wiener: Wiener process, Wiener process is a continuous-time random process.

[0041] Schwarz Information Criterion: Schwarz Information Criterion, a model selection criterion based on the Bayesian Information Criterion.

[0042] Arrhenius model: The Arrhenius model is particularly suitable for accelerated life testing under temperature stress, where the life characteristics of the product can be extrapolated to normal stress conditions based on its performance under high stress.

[0043] In order to facilitate those skilled in the art to better understand the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0044] like Figure 1 As shown, the method for evaluating the reliability of the control circuit of an intelligent circuit breaker described in this embodiment, the intelligent circuit breaker includes an electronic control system and a contact mechanical system. The electronic control system mainly includes a control circuit.

[0045] S1, dividing the control circuit into several functional modules, establishing a failure rate calculation model of the functional modules and the electronic components corresponding to the functional modules, so as to determine the key failure components and key models of the control circuit.

[0046] Smart circuit breakers do not operate frequently during normal use, and the components of the contact mechanical system degrade slowly and are basically not affected by the environment. However, the control circuit needs to process information in real time based on the user's power usage and make status judgments and complete action control. In addition, internal components will degrade due to the external environment, and the main degraded components of the smart circuit breaker system are determined.

[0047] According to the actual use status of the intelligent circuit breaker throughout its life cycle, the reliability mathematical model of the control circuit during its life cycle is established by comprehensively considering the actual working status of each functional module, as shown in Formula (1) to Formula (3).

[0048] (1)

[0049] (2)

[0050] (3)

[0051] In the formula, is the failure rate of the control circuit; is the failure rate of the i-th functional module, For each functional module j The failure rate of each component; T BF is the mean time between failures of the control circuit; R(t) is the reliability function. Where n is the number of functional modules, and m is the number of components of the i-th functional module.

[0052] Among them, the failure rate of each component is the non-operating failure rate multiplied by the coefficient.

[0053] In one embodiment of the present invention, the non-operational failure rate of each module and component is as shown in formula (4).

[0054] (4)

[0055] According to the standard, the non-working correction coefficient of each component can be obtained. is the calculated non-operating failure rate, To obtain the non-working basic failure rate, , , , They are the non-operating temperature coefficient, non-operating environment coefficient, non-operating quality coefficient and other influencing coefficients of the component.

[0056] By comparing the failure rates, we can get the key components corresponding to the key functional modules that affect the control circuit.

[0057] like Figure 2 As shown in the figure, it is a bar chart diagram of the module failure rate obtained after calculation. Figure 2 It can be seen that the switching power supply has the highest failure rate, and this module is selected as the key component for research.

[0058] like Figure 3 As shown in the figure, it is a bar diagram of the component failure rate obtained through calculation. Figure 3 It can be seen that an electrolytic capacitor in the key functional module has the highest failure rate, and this electrolytic capacitor is selected as the key component for research.

[0059] S2, based on the key functional modules, selects the monitoring signal for evaluating the degradation state of the control circuit, and scores the multi-dimensional candidate feature quantities extracted from the monitoring signal based on the feature evaluation model, so as to obtain the optimal degradation feature parameters.

[0060] S21, selecting a monitoring signal for evaluating a degradation state of a control circuit based on a key functional module.

[0061] By monitoring the signals of key functional modules, the monitoring signal that best represents the performance degradation state of the control circuit is selected.

[0062] According to the failure statistics of intelligent circuit breaker products, most of them are failures during the closing process. Measure the board-level electrical signals of the control circuit during the closing process of the intelligent circuit breaker: motor current, motor terminal voltage, switch power supply voltage, linear power supply voltage, closing and opening control signal and closing and opening state feedback signal, and plot them on Figure 4 Among them, the A and C stages are the closing and opening steady-state stages of the intelligent circuit breaker, respectively. In this stage, the current, voltage and power output to the motor by the control circuit are all zero, the output voltage of the switching power supply is zero, and the control circuit as a whole is in a low-power dormant state. The B stage is the closing process, which includes multiple working stages, a is the triggering moment of the closing signal, b is the starting moment of the switching power supply, cd time period is the motor starting process, de time period is the motor no-load running process, f is the moment when the moving and static contacts begin to contact, hi is the motor braking process, jk is the braking or precise positioning process, k is the closing moment, and l is the moment when the switching power supply is turned off.

[0063] The control circuit includes not only device-level degradation status monitoring signals, such as capacitance C , Equivalent Series Resistance ESRetc.; it also includes circuit board-level degradation state monitoring signals, such as input voltage signal, output voltage signal, sampling voltage and current signal, output current signal and other signals. There is a coupling redundant relationship between each signal. If all these signals are considered for measurement, on the one hand, it will increase the difficulty of data processing and analysis, and on the other hand, it will cause a waste of test resources. Therefore, it is necessary to consider the priority of the degradation state monitoring signal in combination with the circuit topology, the electrical characteristics of the monitoring signal, the measurement difficulty and the measurement cost, and select the monitoring signal that best represents the degradation state of the control circuit performance.

[0064] In one embodiment of the present invention, according to the calculation result of the failure rate of the control circuit, the main drive circuit of the control circuit is determined to be the key weak link, so it is more convenient and direct to monitor the output voltage and current signals of the key circuit as a whole, and the degradation characteristic parameters are extracted from these monitoring signals, and their evolution trend can better characterize the degradation trend of the overall performance of the control circuit. Therefore, the switching power supply voltage, the motor terminal voltage, and the motor current are selected as the monitoring signals of the degradation state of the control circuit.

[0065] S22, evaluating and scoring the multi-dimensional candidate feature quantities extracted from the collected monitoring signals based on the feature evaluation model to obtain the optimal degradation feature parameters.

[0066] In one embodiment of the present invention, the feature evaluation model is shown in formula (5) and formula (6). The multi-dimensional feature candidate parameters are comprehensively evaluated and scored according to the feature evaluation model, so that the feature candidate parameter with the highest score is used as the optimal degradation feature parameter.

[0067] (5)

[0068] (6)

[0069] Among them, z is a comprehensive evaluation index, is the weight coefficient, Tre (), Mon (), Rob () are trend index, monotonicity index and robustness index respectively. k 1、 k 2、 k 3 represents the contribution of each evaluation index to the comprehensive index. st is the constraint condition, k 1. k 2. k 3. The relationship in st must be satisfied. F =( f 1, f 2,…, f N) represents the time series of degradation characteristic parameters, T =( t 1, t 2,…, t N ) represents the sampling sequence.

[0070] In an embodiment of the present invention, k 1. k 2. k 3 Based on engineering experience, 0.5, 0.4, and 0.1 are selected.

[0071] In one embodiment of the present invention, the first accelerated degradation test is performed on the intelligent circuit breaker, that is, the monitoring signal collected by the accelerated degradation test at a temperature of 105°C is calculated using a feature evaluation model to obtain the feature candidate parameter with the highest score as the optimal degradation feature parameter: the peak-to-peak value of the motor terminal voltage. Figure 5 As shown in the figure, in order to score the multi-dimensional candidate feature quantities, the comprehensive evaluation index of the peak-to-peak value of the motor terminal voltage is the highest score of 0.715 points, so it is used as the optimal degradation feature parameter.

[0072] S3, analyzes the degradation evolution trend and degradation change point of the control circuit based on accelerated degradation test and optimal degradation characteristic parameters.

[0073] In one embodiment of the present invention, the least square method is used to fit the degradation evolution trend of the optimal degradation characteristic parameter.

[0074] The control circuit is subjected to constant stress accelerated degradation test. Three temperature stresses of 105 ℃, 95 ℃ and 85 ℃ are selected. There are 5 test samples in each group. Each test cycle is 24 h. The optimal degradation characteristic parameters are detected 5 times. Figure 6-Figure 8 They are the degradation evolution trends of the optimal degradation characteristic parameters of the five test samples at 105°C, 95°C, and 85°C, respectively, that is, the degradation evolution trends of the peak-to-peak value of the motor terminal voltage.

[0075] The characteristic sequence important point fixed segmentation algorithm of trend turning distance is adopted to divide the performance degradation process of the control circuit into the stable degradation stage, i.e. the first degradation stage, and the rapid degradation stage, i.e. the second degradation stage. The calculation formula is shown in formula (7).

[0076] (7)

[0077] In the formula, d is the degradation change point. The time series of the control circuit degradation parameters is defined as X =[ X i =( t i , xi )], i=1,…, n-1, where x i For the moment t i The degenerate characteristic value of X i represents the i-th measurement point, any three points in the degradation feature sequence X i , X k , X j ( i < k <j ) is to randomly select three points from X.

[0078] In one embodiment of the present invention, each experimental cycle is 24 hours, and the product is tested 5 times, that is, the optimal feature times are collected 5 times, and the average value is taken to reduce the influence of measurement errors. n represents n experimental cycles.

[0079] S4, based on the degradation evolution trend and degradation change point of the control circuit, a two-stage Wiener process degradation model is established, and the degradation model parameters and degradation change point under accelerated stress are estimated using the maximum likelihood method and the Schwarz information criterion, respectively. It mainly includes the following steps.

[0080] S41, a two-stage Wiener process degradation model is established based on the performance degradation process of the control circuit, as shown in formula (8).

[0081] (8)

[0082] In the formula, X 01 , X 02 are the initial degradation amounts of the first and second degradation stages, respectively; μ 1. μ 2 are the drift parameters of the first and second degradation stages, respectively, indicating the degradation rate of each stage; σ 1. σ 2 are the diffusion parameters of the first and second degradation stages, respectively; B ( t ) is the standard Brownian motion.

[0083] S42, the model parameters of the two-stage Wiener process model are estimated using the maximum likelihood estimation method.

[0084] Mainly for the two-stage Wiener process degradation model parameters μ 1. μ 2. σ 1. σ 2 is estimated, and the formula used for estimation is as follows.

[0085] (9)

[0086] Due to the existence of degradation change points, the model distribution parameters k is unknown, so the model distribution parameters need to be k Estimation is performed to obtain estimated values ​​of the model distribution parameters.

[0087] S43: Change point estimation method based on Schwarz information criterion is used to estimate the distribution parameters of the model k Estimate and find the change point location , including the following steps.

[0088] S431, Schwarz information criterion is defined as shown in formula (10).

[0089] (10)

[0090] is the maximum likelihood function, is the parameter estimate, p is the number of free parameters, m is the sample size.

[0091] S432, determining the interval where the degradation change point is located for the degradation data of each sample control circuit based on the Schwarz information criterion. Specifically, the following steps are included.

[0092] S4321: First stage degradation incremental data ; Second stage degradation incremental data Combining the Schwarz information criterion, the hypothesis test is proposed as shown in formula (11).

[0093] (11)

[0094] In the formula, μ i and σ i 2 ( i =1,2,…,m) is the μ and σ 2 value, Λ represents the change, which can be t i -t i-1 , m and n are the number of experimental cycles, is the time interval, H 0 For hypothesis testing,H 1 is the alternative hypothesis.

[0095] S4322: Calculate the SIC value and determine the change point and estimate the interval based on the hypothesis test.

[0096] Null hypothesis H 0 The values ​​of the distribution parameters of are consistent, indicating that there is no change point and the product is a single-stage Wiener degradation process. The SIC value under this assumption is shown in formula (12).

[0097] (12)

[0098] Alternative hypothesis H 1 There are inconsistencies in the distribution parameters, indicating the presence of change points , the product satisfies the two-stage Wiener degradation process, and the SIC value under this assumption is shown in formula (13).

[0099] (13)

[0100] In the formula, 2≤ k≤m-2, , we will reject the null hypothesis H 0 , it is believed that there is a change point. At the same time, the estimated value of the distribution parameter k of the model is estimated in the interval where the change point is located As shown in formula (14).

[0101] (14)

[0102] Through the above SIC, the interval of the performance degradation change point of each sample can be determined .

[0103] S433, determine the change point position using the minimum residual square sum criterion Estimated value.

[0104] The model distribution parameter estimates Substituting into formula (9) we can get the distribution parameter μ 1. μ 2. σ 1 2 , σ 2 2 Then consider using the residual sum (Sum of Squared Error, SSE) minimization criterion to estimate the estimated value of the change point location. SSE is defined as shown in the following formula (15).

[0105] (15)

[0106] Among them, the coefficients of A, B, and C are shown in formula (16). In formula (15), E is the mean calculation function, Var is the variance calculation function, and m and n are the number of experimental cycles.

[0107] (16)

[0108] According to formula (16), the function variable value corresponding to the minimum value of SSE can be obtained through the properties of binary functions: , which is the change point position The estimated value of .

[0109] S434: Determine the estimated values ​​of the model parameters at each stage by maximum likelihood estimation.

[0110] It is usually assumed that the degradation change points follow a normal distribution, that is, according to the above n The change point estimate of the sample points is obtained, and then the maximum likelihood estimation can be used to estimate the degraded change point distribution parameters, as shown in formula (17).

[0111] (17)

[0112] S5, using the Arrhenius model to obtain the reliability function and average life of the control circuit under normal use conditions, including the following steps.

[0113] S51, the model parameters of the two-stage Wiener process degradation model under three temperature stresses are estimated, and the estimated values ​​of the distribution parameters under the three temperature stresses are converted to the parameters of the degradation model under normal use conditions according to the Arrhenius model and the least squares method. That is, the parameters of the two-stage Wiener process degradation model under normal temperature 25 ℃ are obtained by converting them to normal temperature 25 ℃.

[0114] Among them, the parameters of the two-stage Wiener process degradation model include μ 1. μ 2. σ 1. σ 2 and change point .

[0115] The Arrhenius model is used to reduce the control circuit of the intelligent circuit breaker to normal operating conditions, and its definition is as shown in formula (18).

[0116] (18)

[0117] In the formula, , A is a constant, E is the activation energy, k is the Boltzmann constant, and T is the absolute temperature value (K) of the test stress level.

[0118] It should be noted that the ambient temperature of the product degradation during the accelerated degradation experiment is not the normal operating condition, so it needs to be converted to the normal operating condition.

[0119] S52, solving the reliability function of the product according to the estimated values ​​of the model parameters of the two-stage Wiener process degradation model under normal use conditions, and calculating the average life of the product.

[0120] To obtain the reliability function R(t) of the product, the specific solution method is as follows.

[0121] The reliability function of the product can be expressed as shown in the following formula (19).

[0122] (19)

[0123] In the formula, F (t) is the cumulative failure distribution function. The reliability function is calculated by first obtaining the cumulative failure distribution function.

[0124] Substitute the parameters obtained in step S42 into the model in step S52, that is, after solving the model parameter estimation value of the two-stage Wiener process degradation model through S42, solve the model parameter estimation value of the two-stage Wiener process degradation model at normal temperature according to the Arrhenius model, calculate the model distribution parameter value of each degradation stage under normal temperature stress of 25 ℃, and obtain the reliability function of the product. Substitute the obtained parameters into R(t), and the reliability curve obtained by R(t) values ​​at different t is as follows Fig. 9 shown.

[0125] Because the product obeys a two-stage degradation process and the degradation rates before and after the degradation change point are different, it is necessary to discuss the cumulative failure distribution function and reliability function of the product in the following two cases.

[0126] (1) The product is in the first degradation stage. The cumulative failure distribution function and reliability function expressions of this stage are shown in formulas (20) and (21).

[0127] (20)

[0128] (twenty one)

[0129] (2) When the product enters the second degradation stage, it means that the product is If there is no failure within the time period, s for At any time point within , the following conditions should be met, that is, formula (22) should be satisfied.

[0130] (twenty two)

[0131] In the formula, H is the failure threshold, is the standard normal distribution function.

[0132] According to the Markov property of the Wiener process, the above equation can be transformed into the description of equation (23).

[0133] (twenty three)

[0134] in,

[0135] (twenty four)

[0136] In summary, the reliability function of the two-stage Wiener process is R ( t ) is shown in formula (25).

[0137] (25)

[0138] The average lifespan is:

[0139] (26)

[0140] Therefore, the reliability function can be used to solve the reliability of the intelligent circuit breaker control circuit.

[0141] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

Claims

1. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker, wherein the intelligent circuit breaker comprises at least a control circuit and a contact mechanical system, characterized in that: Methods for evaluating the reliability of intelligent circuit breaker control circuits include: S1, divide the control circuit into several functional modules and establish a failure rate calculation model to determine the key functional modules and key failure components of the control circuit; S2, selecting a monitoring signal for evaluating the degradation state of the control circuit based on the key functional modules, and extracting a multi-dimensional candidate feature quantity based on the monitoring signal to obtain an optimal degradation feature parameter; S3, based on the accelerated degradation test and the optimal degradation characteristic parameters, analyze the degradation evolution trend and degradation change point of the control circuit, including: S31, subjecting a number of samples to accelerated degradation tests under different temperature stresses, thereby obtaining the degradation evolution trend of the optimal degradation characteristic parameters; S32, using the least squares method to fit the degradation evolution trend; S33, using a fixed segmentation algorithm of important points of a characteristic sequence of trend turning distance to divide the fitted degradation evolution trend into a first degradation stage and a second degradation stage, and obtain a degradation change point; S4, based on the analysis of degradation evolution trend and degradation change point, a two-stage Wiener process control circuit performance degradation model is established, and the control circuit performance degradation model parameters and degradation change point of the accelerated degradation test are estimated based on the maximum likelihood method and the Schwarz information criterion, wherein the two-stage Wiener process degradation model is as follows: ， Where, X 01、 X 02 are the initial degradation amounts of the first and second degradation stages, respectively; , are the drift parameters of the first and second degradation stages, respectively, indicating the degradation rate of each stage; , are the diffusion parameters of the first and second degradation stages, respectively, and B(t) is the standard Brownian motion; S5, based on the Arrhenius model and the control circuit performance degradation model, the reliability function and average life under normal use conditions are calculated.

2. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 1, characterized in that: The failure rate calculation model is as follows: ， ， ， in, To control the failure rate of the circuit, is the failure rate of the ith functional module or the failure rate of the ith component of the circuit of the functional module, is the failure rate of the jth component of each functional module, is the mean time between failures of the control circuit, is the reliability function, where n is the number of functional modules and m is the number of components of the i-th functional module.

3. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 1, characterized in that: The step S2, selecting a monitoring signal for evaluating the degradation state of the control circuit based on the key functional module, and extracting a multi-dimensional candidate feature quantity based on the monitoring signal to obtain an optimal degradation feature parameter, specifically includes: S21, selecting a monitoring signal for evaluating a degradation state of a control circuit based on a closing process of the intelligent circuit breaker and key functional modules; S22, evaluating and scoring the multi-dimensional candidate feature quantities extracted from the collected monitoring signals based on the feature evaluation model to obtain the optimal degradation feature parameters.

4. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 1, characterized in that: The maximum likelihood method is used to estimate the parameters of the two-stage Wiener process degradation model. , , , Make an estimate.

5. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 4, characterized in that: The change point estimation method based on Schwarz information criterion is used to estimate the model distribution parameters used to estimate the parameters of the two-stage Wiener process degradation model, and the change point position is obtained.

6. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 5, characterized in that: The change point estimation method based on the Schwarz information criterion is used to estimate the model distribution parameters for estimating the parameters of the two-stage Wiener process degradation model, and obtain the change point position, specifically including: S431, define Schwarz information criterion; S432, for each sample degradation data, determining the interval where the degradation change point is located based on the Schwarz information criterion; S433, determining an estimated value of the position of the degradation change point based on a minimum residual square sum criterion; S434, using the maximum likelihood method to estimate the estimated value of the degradation change point distribution parameter of each stage to obtain the change point position.

7. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 6, characterized in that: The degradation change point interval is determined based on the Schwarz information criterion for each sample degradation data, specifically including: S4321, hypothesis testing is proposed in conjunction with the Schwarz information criterion; S4322, determine the degradation change point and the estimated interval based on hypothesis testing and calculated Schwarz information criterion.

8. A method for evaluating the reliability of a control circuit of an intelligent circuit breaker according to claim 1, characterized in that: S5, obtaining the reliability function and average life under normal use conditions according to the Arrhenius model and the control circuit performance degradation model, specifically includes: S51, calculate the estimated values ​​of the model parameters of the two-stage Wiener process degradation model under several temperature stresses, and calculate the parameters of the degradation model under normal use conditions based on the Arrhenius acceleration model and the least squares method S52, substituting the model parameters under normal use conditions into the two-stage Wiener process degradation model, establishing a reliability function, and solving the average life under normal use conditions.

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