A method, system, device, and medium for board card life prediction

Abstract

The present application relates to the technical field of relay protection, in particular to a kind of board card life prediction method, system, equipment and medium, method includes the following steps: based on board card device Construction board card life prediction model;Monitoring the operating data that influences board card life, obtains no-fault working time t2 based on board card life prediction model, calculates board card cumulative operating time t1;Whether t1 and t2 satisfy preset condition is judged;If satisfy, judge board card is about to be in fault state, carry out replacement prompt;If not satisfy, output board card is in normal operating state.The present application predicts the life of board card, and the prediction condition is displayed to liquid crystal interface, wireless data transmission is used, and remote visualization is realized;The present application can promote the data sharing of smart grid, by analyzing the overall fault diagnosis of power system network, this part can be used as input characteristic quantity, so that the stability of power system can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of relay protection, in particular to a method, system and device for predicting the service life of a board card and a medium. BACKGROUND

[0002] In the field of relay protection, although the relay protection function has been widely recognized as a key element for evaluating the stability and safety of power systems, the operational stability of the internal board of the relay protection device has long been overlooked. This situation is due to two challenges: one is the technical implementation problem, that is, there is a lack of systematic solution to comprehensively monitor and evaluate the board state; the second is the cognitive misunderstanding, which generally believes that the board replacement cycle is long, and thus ignores the potential replacement needs and hidden dangers caused by sudden failures. In fact, board damage often has suddenness, and its repair and replacement not only consumes time and effort, but also significantly increases the operation and maintenance cost, thereby bringing uncertainty to the stable operation of the power system. More importantly, the continuous operation of power system equipment is crucial to the protection of residential and industrial electricity, and any unplanned downtime may cause industrial accidents and economic losses. With the continuous promotion of the construction of smart grid by the State Grid, the requirements for relay protection equipment have not only been limited to the reliability of functions, but also emphasized the predictability and maintainability of its operational state. Although the existing substation background monitoring system can monitor the overall operational state of the device, it lacks the ability to predict and provide early warning of potential board failures, and it is difficult to meet the higher requirements of smart grid for safety. SUMMARY

[0003] The purpose of the present application is to provide a method, system, device and medium for predicting the service life of a board card to solve at least one of the above technical problems.

[0004] The present application achieves the above-mentioned purpose by the following technical solutions:

[0005] A method for predicting the service life of a board card, characterized in that it comprises the following steps:

[0006] Constructing a board life prediction model based on the influencing devices that affect the service life of the board;

[0007] Monitoring the operational data that affect the service life of the board, obtaining the fault-free working time t2 based on the board life prediction model, and calculating the cumulative running time t1 of the board;

[0008] Determining whether t1 and t2 meet the preset condition; if they do, determining that the board is about to be in a fault state and prompting replacement; if they do not, outputting that the board is in a normal operating state;

[0009] The preset condition is t1>t2*0.95.

[0010] Further, the board card life prediction model is:

[0011]

[0012] Wherein, L op represents the board card capacitance prediction life; ε1, ε2 are respectively the first correction coefficient and the second correction coefficient; L o is the life at the maximum temperature of the capacitance; T max is the maximum working temperature of the capacitance; T h is the actual working temperature of the capacitance; I x is the actual current value flowing through the capacitance when running; Is is the standard allowable current; f x is the actual current frequency flowing through the capacitance, f s is the standard industrial frequency.

[0013] Further, in the board card life prediction model:

[0014] ε1=e η

[0015] ε2=e -2πC

[0016] Wherein, C is the nominal capacity of the capacitance at room temperature; η is the temperature coefficient of static capacity, which is determined by the following formula:

[0017]

[0018] Wherein, is the static capacitance value corresponding to the upper limit temperature of the capacitance material; C 25 is the static capacitance value at 25℃; T c-max is the upper limit temperature of the capacitance material.

[0019] Further, the actual working temperature T h of the capacitance is obtained in the following way: a platinum resistance is designed to be close to the lower end of the capacitance in the circuit, and a measurement circuit is externally connected to obtain the voltage and current value of the platinum resistance, and the temperature of the capacitance when running is determined according to the relationship between the resistance value of the platinum resistance and the temperature.

[0020] The actual current value I x flowing through the capacitance when running is obtained in the following way: the current value on the series side is collected in real time as the current on the current side.

[0021] Further, the process of constructing the board card life prediction model is:

[0022] Obtain the influencing devices affecting the life of the board card;

[0023] Determine the influence factors of each influencing device;

[0024] Determine an influencing device based on the influence factor, which represents the life of the board card.

[0025] Further, the influencing device includes a chip, a capacitor, a resistor, an inductor, and a diode.

[0026] Determine the influence factor of each influencing device, and the specific steps are as follows:

[0027] Obtain the membership degree of each influencing element; the membership degree is a 1x5 vector, which represents the influence degree of each influencing device under each influencing element.

[0028] Calculate the Euclidean distance of each influencing device from the reference centroid point based on the membership degree of each influencing element.

[0029] Calculate the influence factor of each influencing device based on the Euclidean distance of each influencing device from the reference centroid point.

[0030] A system for predicting the life of a board card, comprising:

[0031] A construction module for constructing a board card life prediction model.

[0032] A monitoring module for monitoring the real-time temperature of a capacitor, obtaining a fault-free working time t2 based on the board card life prediction model, and calculating the cumulative running time t1 of the board card.

[0033] A judgment module for judging whether t1 and t2 meet a preset condition; if yes, it is judged that the board card is about to be in a fault state, and a replacement prompt is given; if not, it is output that the board card is in a normal running state.

[0034] The preset condition is t1>t2*0.95.

[0035] An electronic device, comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the computer program is executed by the processor to implement any of the methods for predicting the life of a board card.

[0036] A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement any of the methods for predicting the life of a board card.

[0037] A computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the steps of any of the methods for predicting the life of a board card.

[0038] The present application has the beneficial effect that the present application predicts the remaining life of the board card by constructing a prediction model to monitor and calculate the accumulated running time and the predicted failure-free working time of the board card in real time, and prompts replacement when approaching the failure state, effectively improving the timeliness and efficiency of board card maintenance, and prolonging the overall operation life of the system. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 A method flowchart for board card life prediction according to an embodiment of the present application;

[0040] Figure 2 A method flowchart for determining the influencing device representing the life of the board card according to an embodiment of the present application;

[0041] Figure 3 A method flowchart for board card life prediction according to another embodiment of the present application;

[0042] Figure 4 A system structure diagram for board card life prediction according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] The present application will now be discussed with reference to the example embodiments. It should be appreciated that the discussed embodiments are only to enable those of ordinary skill in the art to better understand and thus implement the content of the present application, and are not intended to imply any limitation on the scope of the present application.

[0044] As used herein, the term "comprising" and variations thereof are to be construed as meaning "including, but not limited to". The term "based on" is to be construed as "based at least in part on". The terms "one embodiment" and "an embodiment" are to be construed as "at least one embodiment".

[0045] Embodiment one

[0046] Figure 1 A method flowchart for board card life prediction according to an embodiment of the present application. As shown in Figure 1 According to an embodiment of the present application, a method for board card life prediction, comprising the following steps:

[0047] Step S102: Construct a board card life prediction model based on the influencing device affecting the life of the board card;

[0048] The operating life of the whole relay protection device depends on the life of a specific hardware board. As long as one board cannot work or is malfunctioning, the whole device is considered to be invalid. Therefore, the evaluation of the operating life of the device is affected by the life of the board. Meanwhile, if all factors affecting the life of the board are to be studied, it is difficult to model the evaluation or the modeling function has too many parameters in actual use. Therefore, the present application focuses on the influence of the devices on the board on the life of the board. Compared with other coupling factors, the length of the life of the devices on the board has the most direct influence. Therefore, the characteristic quantity studied by the present application focuses on the devices on the board, and a board card life prediction model is constructed based on the devices on the board.

[0049] Step S104: monitoring operating data affecting the life of the board card, obtaining a failure-free working time t2 based on the board card life prediction model, and calculating a cumulative operating time t1 of the board card;

[0050] The operating data affecting the life of the board card is monitored in real time, the operating data affecting the life of the board card is input into the board card life prediction model, and a failure-free working time t2 is calculated. The cumulative operating time t1 of the board card is recorded and calculated by a timer inside the board card or an external monitoring system.

[0051] Step S106: determining whether t1 and t2 satisfy a preset condition; if yes, determining that the board card is about to be in a failure state and outputting a replacement prompt; if not, outputting that the board card is in a normal operating state;

[0052] The preset condition is t1>t2*0.95.

[0053] When t1 approaches 95% of t2, it is considered that the board card is about to enter a failure state. If t1>t2*0.95, it is determined that the board card is about to be in a failure state, and a replacement prompt is output. This can be realized by an LED indicator light, a buzzer on the board card, or sending an alarm signal to a monitoring system. If t1≤t2*0.95, it is output that the board card is in a normal operating state, and continues to be monitored.

[0054] Preferably, in the present embodiment, the board card life prediction model is:

[0055]

[0056] wherein L op represents the predicted life of the capacitor of the board card; ε1 and ε2 are respectively a first correction coefficient and a second correction coefficient; L o is the life at the maximum temperature of the capacitor; T max is the maximum working temperature of the capacitor; T h is the actual working temperature of the capacitor; I x is the actual current value flowing through the capacitor during operation; Is is a standard permissible current; f xFor the actual current frequency flowing through the capacitor, f s For the standard industrial electric frequency, specifically,

[0057] ε1=e η , ε2=e -2πC

[0058] Wherein, C is the nominal capacity of the capacitor at normal temperature; η is the temperature coefficient of static capacity, which is determined by the following formula:

[0059]

[0060] Wherein, is the static capacity value corresponding to the upper limit temperature of the capacitor material; C 25 is the static capacity value at 25 DEG C; T c-max is the upper limit temperature of the capacitor material.

[0061] In the embodiment, by constructing a board card life prediction model, combining the real-time monitored capacitor temperature data, the fault-free working time t2 of the board card is calculated and predicted, and the cumulative running time t1 of the board card is recorded, by comparing the relationship between t1 and t2, whether the board card is about to enter a fault state is judged, so that a replacement prompt is given in time.

[0062] The application predicts and warns the board card failure in advance by real-time monitoring the board card device running data, constructing a life prediction model based on the device characteristics, and accurately predicting the board card failure, thereby effectively prolonging the overall operation life of the relay protection device.

[0063] According to an embodiment of the application, the actual working temperature T h of the capacitor is obtained by: designing a platinum resistance close to the lower end of the capacitor in the circuit, then externally connecting a measurement circuit to obtain the voltage and current values of the platinum resistance, and determining the temperature of the capacitor during operation according to the relationship between the resistance value of the platinum resistance and the temperature;

[0064] The actual current value I x flowing through the capacitor during operation is obtained by: real-time acquisition of the current value on the series side as the current value on the current side.

[0065] In the embodiment, in order to determine the board card life prediction model, specific parameters need to be determined, including the following steps:

[0066] Step 1: determining the first correction coefficient ε1.

[0067] First, calculate the temperature coefficient η of the static capacity of the capacitor material, and determine the static capacity value C max corresponding to the maximum working temperature T of the capacitor according to the chip manual or oven baking; and 25The first correction coefficient ε1 is calculated according to the standard parameters.

[0068] Step 2: determining the second correction coefficient ε2.

[0069] Since the parameter compensation of the capacitor material has been performed, only the basic capacitance value needs to be brought in for calculation, and other parameters in the expression of the second correction coefficient ε2 are fixed parameters, and the value is easy to obtain.

[0070] Step 3: the actual working temperature T of the capacitor h is obtained. In the circuit, a platinum resistance is designed to be close to the lower end of the capacitor, and a measurement circuit is externally connected to obtain the voltage and current values of the platinum resistance. According to the relationship between the resistance value and the temperature of the platinum resistance, the temperature of the capacitor during operation is determined.

[0071] Step 4: the actual current value I flowing through the capacitor during operation x is obtained. Since the actual capacitor is widely distributed in the circuit, the current value on the series side is directly collected as the current flowing through the current side in real time.

[0072] Step 5: frequency f is obtained. In the algorithm of the relay protection, high-frequency harmonic interference is focused. Therefore, the current collected in step 4 is directly subjected to discrete Fourier transform, the highest frequency component is extracted and brought in, and the current frequency f actually flowing through the capacitor is obtained x ; the standard industrial electric frequency f s is directly brought in 50Hz for calculation in China.

[0073] Step 6: the life L0 at the maximum temperature of the capacitor, the maximum working temperature T of the capacitor max and the standard allowable current I s are determined. The running life L0 of the capacitor at the maximum temperature T max is generally given in the manual. Or through constant temperature oven baking at a specified preset high temperature, the temperature value is controlled as much as possible above the actual operating temperature, and the accuracy of the calibrated model will be higher. Is the current value allowed by the capacitor design in the circuit design, which is usually a fixed value.

[0074] Through steps 1-6, the model parameter determination method proposed in the application can determine or provide a real-time acquisition method.

[0075] The application realizes accurate perception of the working state of the capacitor and dynamic determination of the life model parameters by integrating platinum resistance temperature measurement and current monitoring, combining material characteristics and discrete Fourier transform, and improves the accuracy and real-time performance of capacitor life prediction.

[0076] According to an embodiment of the application, the process of constructing a board card life prediction model is as follows:

[0077] Step 1: obtaining an influencing device affecting the life of the board card;

[0078] Step two, determine the impact factor of each influencing device;

[0079] Step three, determine the influencing device that characterizes the board card life based on the impact factor.

[0080] In this embodiment, in the process of constructing the board card life prediction model, first, the main influencing factors of the board card life are determined, and the key devices that affect the board card life are focused on. This step is the basis of building the model, because the life of the board card is often affected by the comprehensive performance degradation of multiple devices inside it. Through in-depth analysis of the structure and working environment of the board card, those devices that have a decisive effect on the overall life of the board card are identified. Preferably, the influencing devices in this embodiment include chips, capacitors, resistors, inductors, and diodes; as shown in Table 1:

[0081]

[0082] Table 1: Factors affecting the operation of influencing devices

[0083] Next, the impact factor of each influencing device is determined. These impact factors may include the working temperature, working voltage, current load, usage time, environmental humidity, vibration, etc. of the device. These factors affect the performance and life of the device through different mechanisms, such as high temperature accelerating material aging, and large current load causing the device to heat up and increasing the risk of damage. Through laboratory testing, field monitoring, and data analysis, etc. means, the specific influence of these impact factors on the life of the device can be quantified. Preferably, the impact factor of each influencing device is determined, and the specific steps are as follows:

[0084] Obtain the membership degree of each influencing factor; the membership degree is a 1x5 vector, indicating the influence degree of each influencing device under each influencing factor;

[0085] Calculate the Euclidean distance of each influencing device from the reference centroid point based on the membership degree of each influencing factor;

[0086] Calculate the impact factor of each influencing device based on the Euclidean distance of each influencing device from the reference centroid point.

[0087] Specifically, the features described above are evaluated to determine the impact factor Q of the device, which is calculated as follows:

[0088] 1. Device impact factor proportion classification

[0089] Due to the influence of multiple devices, it is necessary to abstract the above-mentioned influence degree and make a unified comparison, so it is necessary to calculate the impact factor Q of each device. The following takes the feature classification set = {a, b, c, d, e} = {chip, capacitor, resistor, inductor, diode}.

[0090] 2. Device element membership determination

[0091] The corresponding membership function is evaluated, and since a unified model cannot be used, the impact degree is evaluated here, which is relatively simple, and the device membership table is obtained as shown in Table 2:

[0092]

[0093] Table 2 Device membership table

[0094] Since the impact elements of the device are multiple, the membership values of the impact elements of different devices are respectively:

[0095] The membership of the impact range A is μ A (i) = {1.0, 0.8, 0, 0.1, 0.6};

[0096] The membership of the distribution range B is μ B (i) = {0.5, 0.8, 1.0, 0.2, 0.6};

[0097] The membership of the temperature range C is μ C (i) = {0.6, 0.8, 1.0, 0.9, 0.8};

[0098] The membership of the temperature-affected D is μ D (i) = {0.6, 0.8, 0, 0.3, 1.0};

[0099] Wherein, i = a, b, c, d, e; the membership referred to herein can well represent multiple devices in a 1x5 vector, facilitating subsequent numerical processing.

[0100] The above-obtained membership is to objectify the specific correlation degree as a number for evaluation, and since different factors have different proportions, it is not conducive to weighted average calculation of the impact factors of each device. Decision tree, Bayesian classifier, artificial neural network, support vector machine, k-nearest neighbor and other classification algorithms are not suitable for solving this problem. The present application considers that the classification has a center point deviation, is suitable for optimal Euclidean distance, and has no sample training, facilitating simple classification evaluation.

[0101] 3. Device impact factor calculation

[0102] According to the purpose of device classification, the life of the board card is evaluated, so a multi-dimensional vector can be determined, and the optimal device reference vector is variable. Since the impact elements of the device, such as the impact range A, the distribution range B, the temperature-affected D and the easy-to-break degree E, the larger the value, the shorter the running life of the board card, and the larger the temperature range C, the more beneficial the running life of the board, the vector is recorded as the reference center point:

[0103] W={w1, w2, w3, w4, w5}={1, 1, 0, 1, 1} (1)

[0104] wherein, W is the reference centroid point vector, w1, w2, w3, w4, w5 are elements in the reference centroid point vector respectively;

[0105] Calculate the multi-dimensional space Euclidean distance:

[0106]

[0107] wherein, r o-i is the distance between the i-th influencing device in the membership degree vector and the reference centroid point, i=a, b, c, d, e;

[0108] The above Euclidean distance r o-i based on the reference centroid point represents the final calculated value Q i .

[0109]

[0110] wherein, Q i is the influence factor of the i-th influencing device in the membership degree vector.

[0111] This calculation does not need to be weighted by the membership degree, and due to the above-mentioned influence factors, the coupling degree between them is low, so it will not cause spatial coupling.

[0112] The influence factor table of the device type obtained by calculation is shown in Table 3:

[0113] Device type r o-i ]]> Q i ]]> Chip (a) 1.3304 0.7517 Capacitor (b) 1.0050 0.9950 Resistance (c) 2.0000 0.5000 Inductor (d) 1.7320 0.5774 Diode (e) 1.0247 0.9759

[0114] Table 3 Influence factor table of device type

[0115] In this embodiment, according to the data in Table 3, it is determined that the chip has the greatest impact when it fails. It should be noted that the modeling scenario is that the running board fails, and the device failure probability needs to be considered. The chip damage will definitely have a huge impact on the logic timing of the entire board, which will directly lead to the failure of the entire device, but the possibility of chip failure is relatively small, and the most suitable influence factor should be selected.

[0116] The replacement of plug-in in the field of relay protection is difficult to avoid, and the replacement of the board is also directly related to the life of the corresponding board. Generally, high-integration chips consider taking measures to cope with the influence of high-temperature resistance, corrosion resistance and other special environments at the beginning of design. Therefore, the life of the analog component related to the overall influence of the board is more explicit. Among the capacitor, resistor and inductor, the capacitor is more susceptible to external changes, so the research on the life of the capacitor has a higher correlation with the life of the board.

[0117] According to the theory proposed in the present application, the selected capacitor is most suitable for research, and the distribution and comprehensive influence factor of the diode is slightly lower than that of the capacitor device although the diode is easy to be damaged when the voltage across the two ends exceeds the upper limit of the working voltage. Therefore, according to the above classification discussion, it is determined that the capacitor is a characteristic device for evaluating the service life of the board.

[0118] A mathematical model or algorithm for characterizing the service life of the board is constructed based on the influence factor. This step is the core of model construction, which needs to establish a connection between the performance degradation of each influencing device and the overall service life of the board. Advanced technologies such as machine learning and deep learning can be used to analyze and train a large amount of historical data to learn the complex relationship between the performance degradation of each influencing device and the service life of the board, so as to ensure the accuracy and reliability of the model.

[0119] Finally, the prediction effect of the model is verified through actual test. The model is applied to actual board life prediction, and the difference between the model prediction result and the actual observation result is compared to evaluate the accuracy and reliability of the model. According to the test result, the model is adjusted and optimized as necessary to improve its prediction accuracy and practicability.

[0120] The following situations need to be considered when using the service life of the capacitor to characterize the service life of the board:

[0121] Temperature, as the most basic parameter in the experimental environment, should be considered as an influencing factor of the service life of the board. The heating or cooling process of the device will affect the service life of the product, especially the strict upper limit of the use temperature of the device. The influence of such temperature on the running device will not only cause insignificant cracking, swelling, deformation, etc. between materials, but also change the circuit parameters.

[0122] The Arrhenius formula in thermodynamics theory can be used to calculate the service life of the capacitor, which is an empirical formula used to describe the relationship between the reaction rate of chemical substances and temperature, as follows.

[0123]

[0124] The predicted service life of the capacitor L op ; The service life of the capacitor at the maximum temperature L o ; T max is the maximum working temperature of the capacitor; T h is the actual working temperature of the capacitor. However, the theoretical formula described in the capacitor life considers fewer factors, and the accuracy is poor, and the steps of how to obtain the parameters are not specified.

[0125] Therefore, based on the above problems, the present application performs multi-parameter fusion modeling to improve the industrial use precision as much as possible. The voltage across the capacitor directly affects its heating, and according to the impedance X c Calculation formula:

[0126]

[0127] Since the heat power is related to temperature, the operating life change caused by heat is ΔL op . The heat power is P = (I x ) 2 X c , the current change ΔI x .

[0128] Combining equation (4), the following equation can be obtained.

[0129]

[0130] Where, ε' = e -2πC ; I x represents the actual current value flowing through the capacitor during operation; I s is the standard allowable current, obtained by calibration; in order to facilitate calculation, the current change ΔI x before and after is the ratio of I x and I s .

[0131] If the frequency f in the operating circuit is distorted, high-frequency pulses are generated, which will affect the heating of the capacitor more obviously, and the capacitor itself has a direct characteristic of impedance, so the interference of high-frequency harmonic components must be considered.

[0132]

[0133] Where, ΔL op ' is the predicted life change value of the capacitor considering high-frequency harmonic components, f x is the current frequency flowing through the capacitor, and f s is the standard industrial frequency.

[0134] The materials of industrial production capacitors include metal film, polypropylene, thin porcelain, etc. The loss, leakage, and high temperature resistance of different capacitors are different, so different capacitor materials will definitely affect the life of the capacitor. The temperature of the device also directly leads to the change of the operating life.

[0135] The electrostatic capacity of capacitors made of different materials is affected differently at different temperatures, so it is best to consider the influence of capacitor materials. The corresponding electrostatic capacity temperature coefficient of the capacitor material is η.

[0136]

[0137] The upper limit temperature of the capacitor material is T c-max ; the electrostatic capacity value at 25℃ is C 25The upper limit temperature of the capacitor material corresponds to the electrostatic capacitance value

[0138] According to the temperature coefficient of the electrostatic capacity, the offset of the stable running capacitor charge at different temperatures can be known, and the temperature T x The corresponding capacitance C x , and further obtain the correction value of the capacitor.

[0139] C x = η (T x -T 25 ) + C 25 (9)

[0140] The above formula is abstracted as follows for easy calculation:

[0141] C x = ηC (10)

[0142] Wherein C is the theoretical value at a relatively stable temperature, and for a 1uF capacitor, the corresponding capacitance value at 25 degrees Celsius is 1uF; C is the nominal capacity at normal temperature. Then the correction parameter caused by the material is:

[0143] ε = e -2πηC = e η × e -2πC = ε1ε2 (11)

[0144] Wherein, ε1 = e η , ε2 = e -2πC . These two coefficients are empirical parameters of the life running estimation model, which must be calibrated through experiments in actual use.

[0145] Combined with formulas (4), (6), (7) and (11), the estimated life model of the capacitor running can be obtained after arrangement.

[0146]

[0147] According to the model formula, it can be concluded that the life of the capacitor running is related to the current value flowing through the capacitor, the frequency of the current, and the temperature value at which the capacitor actually runs. Obviously, the model conclusion conforms to the theoretical analysis.

[0148] The application provides a board card life prediction model based on influence factor analysis, identifies key influencing devices, quantifies the influence factors, and applies a mathematical model to predict the board card life, thereby improving the prediction accuracy and practicality, and providing a scientific basis for board card maintenance and replacement.

[0149] Example two

[0150] The operation life of the whole relay protection device depends on the life of a specific hardware board, and as long as one board cannot work or is out of order, the whole device will be considered to be invalid. Therefore, the evaluation of the operation life of the device is affected by the life of the board. Meanwhile, if all factors affecting the life of the board are to be studied, it is difficult to model the evaluation or the modeling function has too many parameters in actual use. Therefore, the present application focuses on the influence of the devices on the board on the life of the board. The devices on the board have the most direct influence on the life of the devices compared with other coupling factors. Therefore, the characteristic quantity studied by the present application focuses on the devices on the board.

[0151] Figure 2 The method flow chart for determining the influencing devices of the board card life according to an embodiment of the present application is shown in the figure. Figure 3 The method flow chart for predicting the board card life according to another embodiment of the present application is shown in the figure. Figures 2-3 As shown in the figure, according to an embodiment of the present application, for the devices on the board, the influencing factors can be determined by experience accumulation, including chips, capacitors, resistors, inductors and diodes. Other unconventional devices are not embodied in the evaluation. According to the influence of each device, a table of elements affecting the operation of the devices is listed as shown in Table 1.

[0152]

[0153]

[0154] Table 1: Table of elements affecting the operation of the devices

[0155] According to the above data, it is determined that the chip has the greatest influence when it has a problem. However, it needs to be noted that the modeling scenario is that the board has a failure, and the probability of device failure needs to be considered. The chip damage will definitely have a great influence on the logic timing of the whole board, and will directly lead to the failure of the whole device. However, the possibility of chip failure is relatively small, and the most suitable influencing factor should be selected.

[0156] The method for calculating the influence factor Q of the devices is determined by evaluating the above-described characteristics.

[0157] 1. Classification of the proportion of device influencing elements

[0158] Due to the influence of multiple devices, the above-described influence degree needs to be abstracted and compared uniformly. Therefore, the influence factor Q of each device needs to be calculated. The following takes the characteristic classification set = {a, b, c, d, e} = {chip, capacitor, resistor, inductor, diode}.

[0159] 2. Determination of the degree of device elements

[0160] The corresponding membership function is evaluated, and since a unified model cannot be used, the influence degree is evaluated here, which is relatively simple, and the membership table of the device is shown in Table 2:

[0161]

[0162] Table 2 Membership table of the device

[0163] Since the influencing factors of the device are multiple, the membership values of the influencing factors of different devices are respectively:

[0164] The membership of the influence range A is μ A (i) = {1.0, 0.8, 0, 0.1, 0.6};

[0165] The membership of the distribution range B is μ B (i) = {0.5, 0.8, 1.0, 0.2, 0.6};

[0166] The membership of the temperature range C is μ C (i) = {0.6, 0.8, 1.0, 0.9, 0.8};

[0167] The membership of the temperature-affected D is μ D (i) = {0.6, 0.8, 0, 0.3, 1.0};

[0168] Wherein, i = a, b, c, d, e; the membership referred to herein can well represent multiple devices in a 1x5 vector, facilitating subsequent numerical processing.

[0169] The above membership is obtained to objectify the specific correlation degree as a number for evaluation, and since different factors have different proportions, it is not conducive to weighted average calculation of the influencing factors of each device. The decision tree, Bayesian classifier, artificial neural network, support vector machine, k-nearest neighbor and other classification algorithms are not suitable for solving this problem. The present application considers that the classification has a center point deviation, is suitable for optimal Euclidean distance, and has no sample training, and is convenient for simple classification evaluation.

[0170] 3. Device influencing factor calculation

[0171] According to the purpose of device classification, the life of the board card is evaluated, so a multi-dimensional vector can be determined, and the optimal device reference vector is variable. Since the influencing factors of the device, such as the influence range A, the distribution range B, the temperature-affected D and the easy-to-break degree E, the larger the value, the shorter the running life of the board card, and the larger the temperature range C, the more beneficial the running life of the board, the vector is recorded as the reference center point:

[0172] W = {w1, w2, w3, w4, w5} = {1, 1, 0, 1, 1} (1)

[0173] Wherein, W is the reference centroid point vector, w1, w2, w3, w4, w5 are elements in the reference centroid point vector respectively;

[0174] Calculate the multi-dimensional space Euclidean distance:

[0175]

[0176] Wherein, r o-i is the distance between the i-th influencing device in the membership degree vector and the reference centroid point, i=a, b, c, d, e;

[0177] The above Euclidean distance r o-i based on the reference centroid point represents the final calculated value Q i .

[0178]

[0179] Wherein, Q i is the influence factor of the i-th influencing device in the membership degree vector.

[0180] This calculation does not need to be weighted by the membership degree, and due to the above-mentioned influence factors, the coupling degree between them is low, so it will not cause spatial coupling.

[0181] The calculated device type influence factor table is shown in Table 3:

[0182]

[0183]

[0184] Table 3 Device type influence factor table

[0185] Relay protection field plug-in replacement is difficult to avoid, and the influence of board replacement is directly related to the corresponding life of the board. Generally, high-integration chips consider taking measures to cope with the influence of high-temperature resistance, corrosion resistance and other special environments at the beginning of design. Therefore, the life of the analog component related to the overall influence of the board is more explicit. Among capacitors, resistors and inductors, capacitors are more susceptible to external changes, so the life of the capacitor is more relevant to the life of the board.

[0186] According to the theory proposed in the present application, the capacitor is the most suitable for selection for research. Although the diode is easy to break down when the voltage across the two ends exceeds the upper limit of the working voltage, its distribution and comprehensive influence factor is slightly lower than that of the capacitor. Therefore, according to the above classification discussion, it is determined that the capacitor is the characteristic device for evaluating the life of the board, and the life of the capacitor will be used to represent the life of the board in the following.

[0187] According to one embodiment of the present application, a method for predicting the life of a board based on the reliability of a capacitor comprises:

[0188] Temperature, as the most basic parameter in the experimental environment, should be considered as a factor affecting the life of the board. The heating or cooling process of the device will affect the life of the product, especially the device will have a strict upper limit of the use temperature. The influence of such temperature on the running device will not only cause insignificant cracking, expansion, deformation, etc. between materials, but also change the circuit parameters.

[0189] The Arrhenius formula in thermodynamics can be used to calculate the life of the capacitor, which is an empirical formula used to describe the relationship between the reaction rate of chemical substances and temperature, as follows.

[0190]

[0191] Wherein, L op is the predicted life of the capacitor; L o is the life of the capacitor at the maximum temperature; T max is the maximum operating temperature of the capacitor; and T h is the actual operating temperature of the capacitor. However, the capacitor life described by the theoretical formula has fewer consideration factors and poor accuracy, and the steps of how to obtain the parameters are not indicated.

[0192] Therefore, based on the above problems, the present application performs multi-parameter fusion modeling to improve the industrial use precision as much as possible. The voltage across the capacitor directly affects its heat generation. According to the impedance X c of the capacitor, the calculation formula is as follows.

[0193]

[0194] Since the heat generation power is related to the temperature, the operating life change caused by the heat generation is ΔL op . The heat generation power is P = (I x ) 2 X c , and the change of the two currents before and after is ΔI x .

[0195] Combining formula (4), the following formula can be obtained.

[0196]

[0197] Wherein, ΔL op is the change value of the predicted life of the capacitor; I x represents the actual current value flowing through the capacitor during operation; I s is the standard permissible current, which is obtained by calibration; ΔI x is the change of the two currents before and after, which is obtained by I xThe ratio of the value of the first coefficient ε' and Is is determined; ε' is the first coefficient, and ε' = e -2πC .

[0198] If the frequency f in the running circuit is distorted, high-frequency pulses are generated, which will affect the heating of the capacitor and the inherent resistance of the capacitor itself. Therefore, the interference of high-frequency harmonic components must be considered.

[0199]

[0200] Where ΔL op is the change value of the expected life of the capacitor considering high-frequency harmonic components, f x is the actual current frequency flowing through the capacitor, f s is the standard industrial power frequency.

[0201] The materials used in industrial production of capacitors include metal film, polypropylene, thin porcelain, etc. Different capacitors have different losses, leakage, and high-temperature resistance, so different capacitor materials will definitely affect the life of the capacitor. The temperature of the device also directly affects the running life.

[0202] The static capacitance of capacitors made of different materials is affected differently at different temperatures, so it is best to consider the influence of capacitor materials. The static capacitance temperature coefficient corresponding to the capacitor material is η.

[0203]

[0204] Where η is the static capacitance temperature coefficient, the upper limit temperature T c-max of the capacitor material; the static capacitance value C 25 at 25℃ can be obtained from the chip manual; the static capacitance value corresponding to the upper limit temperature of the capacitor material

[0205] According to the static capacitance temperature coefficient, the offset of the stable running capacitor charge at different temperatures can be known, and the capacitance C x corresponding to the temperature T x is obtained, and the correction value of the capacitor is further obtained.

[0206] C x = η (T x -T 25 ) + C 25 (9)

[0207] The above formula is abstracted as follows for easy calculation:

[0208] C x = ηC (10)

[0209] Where C represents the nominal capacity at room temperature, which is the theoretical value at a relatively stable temperature; then the correction parameter caused by the material is:

[0210] ε=e -2πηC =e η ×e -2πC =ε1ε2 (11)

[0211] Where ε is the correction parameter caused by the material; ε1 and ε2 are the first correction coefficient and the second correction coefficient, respectively, and ε1 = e η ε2=e -2πC These two coefficients are empirical parameters of the lifespan prediction model and must be obtained through experimental calibration in actual use.

[0212] Combining formulas (4)(6)(7)(11), the estimated lifespan model of the capacitor can be obtained after simplification.

[0213]

[0214] Among them, L op This represents the predicted lifespan of the circuit board capacitors; ε1 and ε2 are the first and second correction factors, respectively; L o T represents the capacitor's lifespan at its maximum temperature. max T represents the maximum operating temperature of the capacitor. h This refers to the actual operating temperature of the capacitor; I x The actual current flowing through the capacitor during operation; Is is the standard allowable current; f x f is the frequency of the actual current flowing through the capacitor. s It is the standard industrial electrical frequency.

[0215] According to the model formula, the lifespan of a capacitor is related to the current flowing through it, the frequency of the current, and the actual operating temperature of the capacitor. Obviously, the model conclusions are consistent with the theoretical analysis.

[0216] To determine the capacitor lifetime model, specific parameters need to be determined.

[0217] Step 1: Determine the first correction coefficient ε1.

[0218] First, calculate the capacitance temperature coefficient η of the capacitor material, and then determine the upper temperature limit T based on the chip datasheet or oven baking. max (Capacitor's maximum operating temperature) The electrostatic capacitance value C at 25℃ 25 The standard parameter is given, and thus the determined ε1 is calculated.

[0219] Step 2: Determine the second correction coefficient ε2.

[0220] Since the parameters of the capacitor material have already been compensated, we only need to input the basic capacitance value for calculation. The other parameters in the ε2 expression are fixed parameters, and this value is relatively easy to obtain.

[0221] Step 3: Obtain the actual operating temperature T h .

[0222] The circuit is designed with a platinum resistance thermometer placed close to the bottom of the capacitor, and then an external measuring circuit is connected to obtain the voltage and current values ​​of the platinum resistance thermometer. Based on the relationship between the resistance value of the platinum resistance thermometer and the temperature, the operating temperature of the capacitor is determined.

[0223] Step 4: Obtain the actual current value I flowing through the capacitor during operation. x Since the actual capacitors are widely distributed in the circuit, the current value on the series side is directly collected in real time as the current flowing through the current side.

[0224] Step 5: Obtain the frequency f in the operating circuit. In relay protection algorithms, high-frequency harmonic interference is crucial. Therefore, a Discrete Fourier Transform is directly performed on the current acquired in Step 4, and the highest frequency component is extracted and substituted into the algorithm to obtain the actual frequency f of the current flowing through the capacitor. s The 50Hz frequency was directly input for calculation in the country.

[0225] Step 6: Determine the capacitor's lifespan L0 at its maximum temperature and its maximum operating temperature T. max and the standard allowable current I s .

[0226] Maximum allowable temperature T of the capacitor max The operating life L0 is usually given in the manual. Alternatively, it can be achieved by baking in a constant temperature oven at a preset high temperature, ideally above the actual operating temperature, to ensure higher accuracy of the calibrated model. s In circuit design, the allowable current value for capacitor design is usually a fixed value.

[0227] After steps (1)-(6), the model parameter determination method proposed in this invention can determine or provide a real-time acquisition method.

[0228] Depending on the specific application scenario, the specific actual model can be determined as described above. This invention not only provides a method for model determination but also establishes a corresponding board life prediction algorithm based on the model. Figure 2 The following is a schematic diagram of the algorithm's operation.

[0229] STEP 1: Construct a model of board failure with respect to the operating temperature of capacitor components, providing a theoretical basis for calculating board life using capacitor components;

[0230] STPE 2: Real-time working temperature of the measured capacitance is brought into a calculation model for analysis to obtain a fault-free working time t2;

[0231] STEP 3: The algorithm calculates the cumulative running time t1, if t1>t2*0.95, the liquid crystal directly outputs that the board is about to be in a fault state, please replace in time, otherwise the board is in a normal running state is displayed on the liquid crystal.

[0232] In order to solve the uncertainty of the board replacement time and help the intelligent power grid fault prediction and diagnosis, the evaluation object of the board is first evaluated for the influence factor to determine the appropriate characteristic quantity; the running time characteristic quantity is then evaluated for being susceptible to external factors, and it is determined that the temperature and the load voltage determine the state of the characteristic quantity; secondly, the board prediction life is modeled by fitting the characteristic quantity and the typical factors influencing the characteristic quantity, so as to determine the board prediction life model; finally, the prediction result is output by combining the device display, and the remote manager is provided through the wireless module for non-stationary monitoring.

[0233] Embodiment three

[0234] Figure 4 The system structure diagram of the board life prediction of the embodiment of the present application is shown in FIG. 1. Figure 4 As shown in FIG. 1, according to an embodiment of the present application, a board life prediction system comprises:

[0235] A construction module 10 is configured to construct a board life prediction model based on the influence factors influencing the board life;

[0236] A monitoring module 20 is configured to monitor the running data influencing the board life, obtain a fault-free working time t2 based on the board life prediction model, and calculate a board cumulative running time t1;

[0237] A judgment module 30 is configured to judge whether t1 and t2 satisfy a preset condition; if yes, it is judged that the board is about to be in a fault state, and a replacement prompt is given; if not, it is output that the board is in a normal running state.

[0238] The preset condition is t1>t2*0.95.

[0239] According to an embodiment of the present application, an electronic device comprises a processor, a memory, and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to implement any board life prediction method.

[0240] According to an embodiment of the present application, a computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement any board life prediction method.

[0241] According to an embodiment of the present application, a computer program product comprises a computer program which, when executed by a processor, implements the steps of any of the methods for board life prediction.

[0242] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the system, device, medium and computer program product described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0243] The above description is merely preferred embodiments of the present application and a description of the technical principles used. Those skilled in the art should understand that the scope of the application disclosed in the present application is not limited to the technical solutions formed by the specific combinations of the technical features described above, and should also cover other technical solutions formed by any combinations of the technical features described above or their equivalent features without departing from the inventive concept. For example, the technical solutions formed by mutually replacing the above-described features and the technical features disclosed in the present application (but not limited to) having similar functions.

[0244] It should be understood that the sequence of the steps in the summary of the application and the embodiments does not absolutely mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Claims

1. A method for board card life prediction, the method comprising: The method comprises the following steps: A board card life prediction model is constructed based on the influencing devices affecting the life of the board card. ； wherein, represents the board capacitor life prediction; , are the first and second correction coefficients, respectively; is the capacitor life at maximum temperature; is the capacitor maximum operating temperature; is the capacitor actual operating temperature; is the actual current flowing through the capacitor at runtime; is the standard permissible current; is the actual current frequency flowing through the capacitor, is the standard industrial electrical frequency; The process of constructing the board card life prediction model is: The influencing devices affecting the life of the board card are obtained. The influencing devices include chips, capacitors, resistors, inductors, and diodes. The influence factors of each influencing device are determined. The influencing devices representing the life of the board card are determined based on the influence factors. The influence factors of each influencing device are determined, and the specific steps are: The membership degrees of each influencing element are obtained. The membership degrees are 1x5 vectors, representing the influence degrees of each influencing device under each influencing element. The Euclidean distances of each influencing device from the reference centroid point are calculated based on the membership degrees of each influencing element. The influence factors of each influencing device are calculated based on the Euclidean distances of each influencing device from the reference centroid point. The running data affecting the life of the board card are monitored, and the cumulative running time t1 of the board card is calculated based on the running data and the board card life prediction model.

2. The method of board card life prediction of claim 1, wherein, It is determined whether t1 and t2 satisfy the preset condition. ； ； Wherein, C is the nominal capacity of the capacitance at normal temperature; The electrostatic capacity temperature coefficient is determined by the following formula: ； wherein, the electrostatic capacitance value corresponding to the upper limit temperature of the capacitor material; the electrostatic capacitance value at 25°C; the upper limit temperature of the capacitor material. If the preset condition is satisfied, it is determined that the board card is about to be in a failure state, and a replacement prompt is given. Capacitor actual working temperature The acquisition method is that a platinum resistor is designed to be close to the lower end of the capacitor in the circuit, and a measurement circuit is externally connected to obtain the voltage and current values of the platinum resistor, and the temperature of the capacitor during operation is determined according to the relationship between the resistance value of the platinum resistor and the temperature. Actual current value flowing through the capacitor at runtime The acquisition method is to collect the series side current value in real time as the current side current flowing through.

4. A system for board card life prediction, characterized by, If the preset condition is not satisfied, it is output that the board card is in a normal running state. The preset condition is t1>t2*0.

95. In the board card life prediction model: ， wherein, represents the board capacitor predicted life span; , are the first and second correction coefficients, respectively; is the capacitor life span at maximum temperature; is the capacitor maximum operating temperature; is the capacitor actual operating temperature; is the actual current value flowing through the capacitor at runtime; is the standard permissible current; is the actual current frequency flowing through the capacitor, is the standard industrial electrical frequency; 3. The method for predicting the life of a board card according to claim 2, characterized in that: It comprises: A construction module for constructing a board card life prediction model based on the influencing devices affecting the life of the board card. The board card life prediction model is: The process of constructing the board card life prediction model is: The influencing devices affecting the life of the board card are obtained. The influencing devices include chips, capacitors, resistors, inductors, and diodes. The influence factors of each influencing device are determined. The influencing devices representing the life of the board card are determined based on the influence factors. The influence factors of each influencing device are determined, and the specific steps are: The membership degrees of each influencing element are obtained.

5. An electronic device, comprising: The membership degrees are 1x5 vectors, representing the influence degrees of each influencing device under each influencing element.

6. A computer-readable storage medium, characterized in that, The Euclidean distances of each influencing device from the reference centroid point are calculated based on the membership degrees of each influencing element. The influence factors of each influencing device are calculated based on the Euclidean distances of each influencing device from the reference centroid point. A monitoring module for monitoring the running data affecting the life of the board card, and calculating the cumulative running time t1 of the board card based on the running data and the board card life prediction model. A judging module for judging whether t1 and t2 satisfy the preset condition. If the preset condition is satisfied, it is determined that the board card is about to be in a failure state, and a replacement prompt is given. If the preset condition is not satisfied, it is output that the board card is in a normal running state. The preset condition is t1>t2*0.

95. The computer program is stored on the computer readable storage medium and is executed by the processor to implement the method for predicting the life of a board card according to any one of claims 1 to 3. The computer program is stored on the computer readable storage medium and is executed by the processor to implement the method for predicting the life of a board card according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method of board card life prediction of any one of claims 1 to 3.

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