A method and system for evaluating the life of a SIS logic controller

Abstract

The present invention discloses a method and system for evaluating the lifespan of an SIS (System-of-Integrated System) logic controller (SIS) logic controller. The method comprises: generating an initial sample space based on various factors affecting the operating conditions of the SIS logic controller; loading samples under various combinations of factors onto a preset finite element model to obtain a first lifespan result; then comparing the first lifespan result with the results of an accelerated lifespan test under the same conditions to obtain a lifespan error; establishing a data-driven model that characterizes the relationship between the input parameters of the combination factors and the lifespan error parameter, and solving the data-driven model to obtain an optimal combination factor data set; obtaining second lifespan data for the optimal combination factor data set under different input current conditions; constructing a lifespan evaluation model using the different input current data and the corresponding second lifespan data; and inputting the current data and utilizing the lifespan evaluation model to obtain dynamic lifespan evaluation results. The present invention establishes a lifespan model applicable to SIS logic controllers, providing SIS logic controller integrity assessment capabilities.

Description

Technical Field

[0001] The present invention relates to the field of high-precision evaluation of SIS logic controller reliability, and in particular to a method and system for evaluating the life of a SIS logic controller. Background Art

[0002] As my country's industrial development continues to expand, the scale of production in the chemical industry is also growing. Against this backdrop, the chemical safety sector faces even more severe challenges. Safety instrumented systems (SIS) offer advantages such as wide coverage, high safety, and self-diagnostic capabilities. SIS controllers primarily consist of hardware components, including the core PU board, DI, DO, AI, and AO boards. Failure of any of these boards can impact the functional integrity of the entire SIS. However, due to the relatively recent introduction of SIS into the chemical industry, research on its reliability is currently primarily focused on the design and development phase, primarily by manufacturers. Considering that petrochemical and refining environments are often located in harsh, outdoor environments, this significantly reduces the operational reliability and service life of SIS controllers. Therefore, conducting hardware reliability model analysis and evaluation of safety instrumented SIS systems in petrochemical settings is of great significance.

[0003] Because SIS logic controllers have only recently been introduced to China, targeted research on their reliability has yet to be fully conducted. In related industries, such as conventional DCS and PLC controllers, the main research methods include finite element numerical simulations based on state physics equations and accelerated aging experiments. Studies have shown that the main cause of failure is the failure of the integrated circuit board, so existing technologies mostly focus on the thermal reliability and thermal stress reliability of the integrated circuit board. In finite element technology, to reduce the amount of calculation, research often uses boundary settings under ideal conditions, and to avoid the non-convergence problems caused by high-dimensional and complex physical state equations, factors such as salt spray, electromagnetic interference, and ambient humidity are often ignored. However, in the harsh petrochemical environment, these factors will lead to a reduction in the life of the SIS system, which is far from the actual experimental data and the life results under real operating conditions. This problem not only plagues SIS systems but also poses a difficult problem facing the current electronics industry.

[0004] On the other hand, some reliability assessments for electronic products often rely on classic aging tests. In an environmental laboratory, accelerated controller aging tests are conducted under loading conditions similar to those in the operating environment. The experimental data is then trained using intelligent algorithms such as Monte Carlo and neural networks to fit the correlation coefficients of the accelerated life model, from which the actual life under normal operating conditions can be inferred. However, the precise fitting coefficients are closely related to the experimental sample size. Obtaining an accurate model using this method requires a large number of experimental samples, which consumes considerable time and resources, significantly increasing economic costs.

[0005] In response to the problems existing in traditional technologies, it is urgent to organically combine finite element numerical simulation and aging experiments, use computer software to optimize the error between numerical simulation and experimental results, obtain more and more accurate experimental samples, and establish a life evaluation model suitable for SIS logic controllers to form a more efficient and accurate SIS logic controller reliability analysis method, thereby scientifically and completely evaluating the life results of SIS logic controllers. Summary of the Invention

[0006] The purpose of this invention is to address the problem that traditional technologies ignore multiple operating conditions, resulting in a large discrepancy between actual experimental data and life results under real operating conditions, and to propose a technical solution for evaluating the life of SIS logic controllers by establishing a life evaluation model.

[0007] To address the above technical problems, an embodiment of the present invention provides a method for evaluating the life of an SIS logic controller, comprising: step 1: generating an initial sample space based on various factors affecting the operating conditions of the SIS logic controller; step 2: loading samples under various combination factors in the initial sample space onto a preset finite element model to obtain a first life result under the corresponding combination factor, and then comparing the first life result with the results of an accelerated life test under the same conditions to obtain a life error; step 3: establishing a data-driven model that characterizes the relationship between the input parameters of the combination factors and the life error parameters, and solving the data-driven model based on the samples of each combination factor and the corresponding life error to obtain an optimal combination factor data set; step 4: obtaining second life data of the optimal combination factor data set under different input current conditions, thereby constructing a life evaluation model suitable for the SIS logic controller based on a basic failure physics model and using different input current data and the corresponding second life data; and step 5: obtaining a dynamic life evaluation result using the life evaluation model based on real-time input current data of the SIS logic controller to be evaluated.

[0008] Preferably, in the method, step one includes: determining various variable factors and corresponding parameter ranges; using the optimal Latin hypercube criterion to fill the sample space according to the parameter ranges of various variable factors to form a three-dimensional sample space distribution; and normalizing the three-dimensional sample space distribution to obtain the initial sample space.

[0009] Preferably, the method, in the process of generating the first life result, includes: constructing a three-dimensional finite element model of the SIS logic controller, loading the rated current on the current controller, and then sequentially loading the samples under each combination factor in the initial sample space onto the three-dimensional finite element model, combining the linear cumulative damage theory, and taking the controller chip temperature exceeding the preset threshold temperature as the cutoff basis, to obtain the first life result under different sample loading conditions.

[0010] Preferably, the method, in the process of solving the data-driven model, includes: continuously adjusting the factor data in the initial sample space so that the life error parameter output by the model reaches a minimum value, thereby obtaining the optimal combination factor data group.

[0011] Preferably, the method, in step three, includes: a first step, fitting the data-driven model according to each combination factor sample and the corresponding life error to obtain an initial relationship model; a second step, obtaining a new input sample point based on a mixed point criterion, and using the initial relationship model to obtain a response value of the current new sample point; a third step, judging whether the current relationship model meets the application requirements based on the new sample point and the corresponding response value using a preset convergence judgment condition; if so, the numerical range of each factor corresponding to the minimum life value of the current relationship model is used as the optimal combination factor data group.

[0012] Preferably, if the current relationship model does not meet the application requirements, the method adds new sample points based on the point-adding criteria and uses the current initial relationship model to obtain corresponding response values, thereby returning to the first step to update the initial relationship model based on the samples of each combination factor and the corresponding life errors, as well as the newly added sample points and their response values.

[0013] Preferably, the method and the data-driven model are implemented using a Kriging proxy model.

[0014] Preferably, the method, in step four, includes: based on a three-dimensional finite element model of the SIS logic controller, and using the optimal combination factor data group as the controller operating environment condition; applying multiple groups of input current data to the controller in the finite element model to obtain second life data under different input current sample conditions; based on the basic failure physics model, according to different input current samples and the corresponding second life data, obtaining the life evaluation model that characterizes the relationship between current and life.

[0015] Preferably, the method, in the step of obtaining the life evaluation model characterizing the relationship between current and life based on the basic failure physics model and according to different input current samples and corresponding second life data, includes: obtaining a training data group and a verification data group according to the input current samples and the corresponding second life data samples in accordance with a preset ratio of training data volume to verification data volume, and the ratio of training data volume to verification data volume is 4:1; fitting the basic failure physics model according to the training data group to obtain an initial life prediction model containing initial fitting coefficients; verifying the initial life prediction model according to the verification data group to update the initial fitting coefficients to obtain the life evaluation model containing the updated fitting coefficients.

[0016] Preferably, in the method, the basic failure physics model is expressed using the following expression:

[0017]

[0018] Among them, t represents the life parameter, E represents the failure acceleration energy, k represents the Boltzmann constant, i represents the current parameter, A, Represent the coefficients to be fitted.

[0019] On the other hand, the present invention also proposes a computer-readable storage medium comprising a series of instructions for executing the method steps described above.

[0020] On the other hand, the present invention also proposes a system for evaluating the life of an SIS logic controller, comprising: an influencing factor sample space generation module, configured to generate an initial sample space based on factors affecting the operating conditions of the SIS logic controller; a life error sample data generation module, configured to load samples under each combination of factors in the initial sample space onto a preset finite element model to obtain a first life result under the corresponding combination of factors, and then compare the first life result with the results of an accelerated life test under the same conditions to obtain a life error; an optimal combination factor generation module, configured to establish a data-driven model that characterizes the relationship between the input parameters of the combination factors and the life error parameters, and solve the data-driven model based on each combination factor sample and the corresponding life error to obtain an optimal combination factor data set; a life evaluation model generation module, configured to obtain second life data of the optimal combination factor data set under different input current conditions, thereby constructing a life evaluation model suitable for the SIS logic controller based on a basic failure physics model and using the different input current data and the corresponding second life data; and an online evaluation module, configured to obtain a dynamic life evaluation result using the life evaluation model based on real-time input current data of the SIS logic controller to be evaluated.

[0021] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0022] This invention proposes a method and system for evaluating the lifespan of SIS logic controllers. This method addresses the problem of traditional technologies ignoring various environmental factors, resulting in discrepancies between lifespan results and actual results. By considering the characteristics of the primary environmental variables that trigger SIS logic controller failures and comparing lifespan results from finite element modeling with those from accelerated testing, the method applies multiple boundary conditions based on a traditional integrated circuit thermal stress finite element model. Proxy models are used to optimize boundary condition parameters, aligning environmental variables more closely with actual operating conditions. This method then establishes a lifespan evaluation model suitable for SIS logic controllers, providing comprehensive SIS logic controller lifespan assessment capabilities.

[0023] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 Schematic diagram of the steps of a method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0026] Figure 2 This is a flow chart of the step of determining an initial sample space in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0027] Figure 3 This is an example diagram of the spatial distribution of initial sample points in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0028] Figure 4 This is a flow chart of the step of optimizing the boundary conditions of the finite element model in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0029] Figure 5 This is a flow chart of constructing a SIS logic controller failure physical equation in a method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0030] Figure 6 Schematic diagram of the overall structure of the system for evaluating the life of a SIS logic controller according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings and examples, so that the present invention can fully understand how to apply technical means to solve technical problems and achieve technical effects, and thus implement the invention accordingly. It should be noted that, as long as no conflict exists, the various embodiments of the present invention and the various features of the embodiments can be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0032] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than here.

[0033] The terms used herein are intended only to describe specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "a", "an", "an item" used herein are also intended to include the plural. It should also be understood that the terms "comprise" and / or "include" used herein specify the presence of stated features, integers, steps, operations, units and / or components, and do not preclude the presence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0034] As my country's industrial development continues to expand, the scale of production in the chemical industry is also growing. Against this backdrop, the chemical safety sector faces even more severe challenges. Safety instrumented systems (SIS) offer advantages such as wide coverage, high safety, and self-diagnostic capabilities. SIS controllers primarily consist of hardware components, including the core PU board, DI, DO, AI, and AO boards. Failure of any one board can impact the functional integrity of the entire SIS. However, due to the relatively recent introduction of SIS into the chemical industry, research on its reliability is currently primarily focused on the design and development phase, primarily by manufacturers. Considering that petrochemical and refining environments are often located in harsh, outdoor environments, this significantly reduces the operational reliability and service life of SIS controllers. Therefore, conducting hardware reliability model analysis and evaluation of safety instrumented SIS systems in petrochemical settings is of great significance.

[0035] Because SIS logic controllers have only recently been introduced to China, targeted research on their reliability has yet to be fully conducted. In related industries, such as conventional DCS and PLC controllers, the main research methods include finite element numerical simulations based on state physics equations and accelerated aging experiments. Studies have shown that the main cause of failure is the failure of the integrated circuit board, so existing technologies mostly focus on the thermal reliability and thermal stress reliability of the integrated circuit board. In finite element technology, to reduce the amount of calculation, research often uses boundary settings under ideal conditions, and to avoid the non-convergence problems caused by high-dimensional and complex physical state equations, factors such as salt spray, electromagnetic interference, and ambient humidity are often ignored. However, in the harsh petrochemical environment, these factors will lead to a reduction in the life of the SIS system, which is far from the actual experimental data and the life results under real operating conditions. This problem not only plagues SIS systems but also poses a difficult problem facing the current electronics industry.

[0036] On the other hand, some reliability assessments for electronic products often rely on classic aging tests. In an environmental laboratory, accelerated controller aging tests are conducted under loading conditions similar to those in the operating environment. The experimental data is then trained using intelligent algorithms such as Monte Carlo and neural networks to fit the correlation coefficients of the accelerated life model, from which the actual life under normal operating conditions can be inferred. However, the precise fitting coefficients are closely related to the experimental sample size. Obtaining an accurate model using this method requires a large number of experimental samples, which consumes considerable time and resources, significantly increasing economic costs.

[0037] In response to the problems existing in traditional technologies, it is urgent to organically combine finite element numerical simulation and aging experiments, use computer software to optimize the error between numerical simulation and experimental results, obtain more and more accurate experimental samples, and establish a life evaluation model suitable for SIS logic controllers to form a more efficient and accurate SIS logic controller reliability analysis method, thereby scientifically and completely evaluating the life results of SIS logic controllers.

[0038] To address the aforementioned issues, the present invention proposes a solution for evaluating the lifespan of SIS logic controllers. This solution considers the environmental factors that influence the operating conditions of the SIS logic controller, integrates these environmental factors into the physical equations, and generates a sample space based on the variables of these environmental factors to conduct the finite element modeling process. Furthermore, multiple boundary conditions are applied to the traditional integrated circuit thermal stress finite element model. A proxy model is used to optimize the boundary condition parameters, making the model's environment more similar to actual operating conditions. The resulting SIS logic controller lifespan model is then more accurately guided by the actual operating conditions. The lifespan evaluation model is then used to generate dynamic lifespan evaluation results, providing comprehensive SIS logic controller lifespan assessment capabilities.

[0039] Example 1

[0040] Figure 1 This is a schematic diagram of the steps of the method for evaluating the life of a SIS logic controller according to an embodiment of the present application. Figure 1 The method for evaluating the life of SIS logic controllers in this paper is described in detail.

[0041] like Figure 1 As shown, step S110 generates an initial sample space according to various factors affecting the operating conditions of the SIS logic controller.

[0042] In one embodiment, Figure 2 As shown, in step S110 of generating the sample space, the following steps are included: first, determining the various variable factors that affect the operating conditions of the SIS logic controller and the parameter range corresponding to each variable factor; then, based on the parameter range of each variable factor, using the optimal Latin hypercube criterion to fill the sample space to form a three-dimensional sample space distribution; finally, normalizing the three-dimensional sample space distribution to obtain the initial sample space.

[0043] Specifically, first, based on the field working conditions of the petrochemical industry, the characteristics of the external environmental condition variables that affect the SIS logic controller and the numerical range of each characteristic parameter are fully considered. Then, the Latin hypercube sampling method is used to sample the environmental variables within the environmental parameter range to form a three-dimensional sample space distribution. Finally, the three-dimensional sample space is optimized to obtain the initial sample space X, see Figure 3 In the embodiment of the present invention, the main environmental variables that affect the SIS logic controller are factors such as high temperature in summer, humidity in the air, and vibration caused by large moving equipment.

[0044] After obtaining the initial sample space, the process proceeds to step S120. In step S120, samples under different combinations of factors in the initial sample space obtained in step S110 are loaded onto a preset finite element model to obtain first life results under the corresponding combinations of factors. The first life results are then compared with the results of an accelerated life test under the same conditions to obtain a life error.

[0045] In one embodiment, Figure 4 As shown, the steps of generating the first life result include: constructing a three-dimensional finite element model of the SIS logic controller, loading the rated current on the current controller, and then sequentially loading samples under various combination factors in the initial sample space constructed according to the Latin hypercube onto the three-dimensional finite element model. In combination with the linear cumulative damage theory, the first life result under different sample loading conditions is obtained based on the controller chip temperature exceeding a preset threshold temperature.

[0046] According to the characteristics of electronic components, their failure is mostly due to the accumulation of Joule heat under the electro-thermal effect after the chip has been powered on for a long time, leading to chip breakdown. Therefore, when performing factor loading, the variable that needs to be input is current, so the rated current is applied to the board chip. Then, each combination factor of the initial sample space is loaded onto the finite element model in sequence. Because the damage to the finite element model caused by each sample in each combination factor is cumulative, loading each group of factors onto the finite element model will produce different life results. Therefore, when loading different combination factors, the Comsol fatigue module will be used to perform multi-physics field simulation to obtain the SIS logic controller finite element simulation life results (i.e., the first life results). The first life results under different loading conditions are then compared with the accelerated life experiments under the same loading conditions to obtain the SIS logic controller life error y.

[0047] After obtaining the life error, the process proceeds to step S130 . Step S130 establishes a data-driven model that characterizes the relationship between the combination factor input parameters and the life error parameters, and solves the current data-driven model based on each combination factor sample and the corresponding life error to obtain the optimal combination factor data set.

[0048] In one embodiment, Figure 4 As shown, in step S130 of obtaining the optimal combination factor data group, it includes: the first step, fitting the data-driven model according to each combination factor sample and the corresponding life error to obtain the initial relationship model; the second step, based on the mixed point criterion, obtaining a new input sample point, and using the initial relationship model to obtain the response value of the current new sample point; the third step, based on the new sample point and the corresponding response value, using the preset convergence judgment condition, judging whether the current relationship model meets the application requirements, if so, then the numerical range of each factor corresponding to the minimum life value of the current relationship model is used as the optimal combination factor data group.

[0049] In addition, if the current relationship model does not meet the application requirements, new sample points are added based on the point-adding criteria and the corresponding response values ​​are obtained using the current initial relationship model, thereby returning to the first step to update the initial relationship model based on the samples of each combination factor and the corresponding life error, as well as the newly added sample points and their response values.

[0050] In one embodiment, the process of solving the data-driven model includes: continuously adjusting the factor data in the initial sample space to obtain the minimum value of the life error parameter output by the model, thereby obtaining the optimal combination factor data group.

[0051] Additionally, the data-driven model is implemented using the Kriging surrogate model.

[0052] Specifically, based on the combined factor samples and the corresponding life error results obtained in step S120, an initial relationship model of the data-driven model of the relationship between the combined factor samples and the life error is established. Then, based on the mixed point addition criterion, the initial relationship model is used to obtain a new life error result based on the new input sample point. Finally, the new sample points are continuously adjusted to obtain the minimum value of the life error. If the new sample points and the life error result meet the preset convergence judgment conditions, the numerical range of each factor of the sample point is the optimal combined factor data set. If the new input sample point does not meet the convergence judgment conditions, it is necessary to add new sample points based on the point addition criterion, and then obtain a new life error result based on the initial relationship model. Finally, the initial relationship model is updated using the obtained data until the sample points and the corresponding life error results meet the convergence judgment conditions.

[0053] After obtaining the optimal combination factor data set, the process proceeds to step S140. Step S140 obtains the second life data of the optimal combination factor data set under different input current conditions, thereby constructing a life assessment model suitable for the SIS logic controller based on the basic failure physics model using the different input current data and the corresponding second life data.

[0054] In one embodiment, the specific steps of the life assessment model construction process are as follows: first, based on the three-dimensional finite element model of the SIS logic controller constructed in step S120, the optimal combination factor data group is used as the controller operating environment condition; then, multiple sets of input current data are applied to the controller in the finite element model to obtain second life data under different input current sample conditions; finally, based on the basic failure physics model, according to different input current samples and the corresponding second life data, a life assessment model characterizing the relationship between current and life is obtained.

[0055] In one embodiment, the process of constructing a life assessment model includes: first, obtaining a training data set and a verification data set based on the input current sample and the corresponding second life data sample, in accordance with a preset ratio of training data volume to verification data volume; then, fitting the above-mentioned basic failure physics model based on the constructed training data set to obtain an initial life prediction model containing initial fitting coefficients; and finally, verifying the initial life prediction model based on the constructed verification data set to update the initial fitting coefficients and obtain a life assessment model containing updated fitting coefficients. The ratio of the data volume in the training data set to the data volume in the verification data set is 4:1.

[0056] The above basic failure physical model is expressed as follows:

[0057]

[0058] Among them, t represents the life parameter, E represents the failure acceleration energy, k represents the Boltzmann constant, i represents the current parameter, A, Represent the coefficients to be fitted.

[0059] Specifically, the optimal combination factor data set obtained in step S130 is used as the environmental condition for the operation of the controller. Based on this environment, multiple sets of input current data are applied to the controller in the finite element model to obtain corresponding second life data. The obtained input current data and the corresponding second life data are divided into a training data set and a verification data set according to a preset ratio of 4:1. The training data set, which accounts for 4 / 5 of the data, is used to fit the basic failure physics model to obtain an initial life prediction model containing initial fitting coefficients. The verification data set, which accounts for 1 / 5 of the data, is used to verify the initial life prediction model, update the initial fitting coefficients, and obtain a life evaluation model containing the updated fitting coefficients.

[0060] After the life evaluation model is obtained, the process proceeds to step S150 . In step S150 , a dynamic life evaluation result is obtained using the life evaluation model according to the real-time input current data of the SIS logic controller to be evaluated.

[0061] Specifically, through the life evaluation model, dynamic life evaluation results can be obtained by inputting current data, thereby providing guidance for actual work and reliability analysis, and forming the integrity assessment capability of the SIS logic controller.

[0062] Example 2

[0063] Based on the above-mentioned embodiment 1, the embodiment of the present invention provides a specific embodiment of a method for evaluating the life of a SIS logic controller.

[0064] S1 determines the initial sample space X based on the field working conditions of the petrochemical industry, taking into full consideration the characteristics of the external condition variables that affect the SIS logic controller and the numerical range of each characteristic parameter.

[0065] Furthermore, in S1, according to the fault tree, the main external influencing factors affecting the SIS logic controller in n petrochemical conditions are listed, and the space X is filled based on the optimal Latin hypercube criterion. The initial sample space X = [x 1 ,x 2 ,x 3 ,…,x n ] T .

[0066] S2 sequentially loads the finite element model based on the initial boundary condition sample constructed using the Latin hypercube. Rated current is applied to the board chip, and multi-physics simulation is performed using the Comsol fatigue module to obtain finite element simulation life results for the SIS logic controller. These results are then compared with accelerated life tests under the same conditions to determine the error y in the SIS logic controller's lifespan. Due to the characteristics of electronic components, failures are often caused by chip breakdown due to Joule heat accumulation caused by the electro-thermal effect after prolonged operation.

[0067] Further, in S2, S21: Construct a 3D finite element model of the SIS logic controller in the Comsol software, load the rated operating current to the chip of the board integrated circuit, and complete the material property setting, meshing and other processes. According to the boundary condition characteristic parameter sample constructed by the Latin hypercube, load the boundary conditions in sequence, and in the fatigue module, combine the linear cumulative damage theory to obtain the SIS logic finite element simulation life result y FEM .

[0068] Further, in S2, S22: through the accelerated aging test platform, the accelerated aging test is carried out under the same boundary conditions and current conditions to obtain the experimental life y EXP , the experimental life y EXP Compare the life results obtained by finite element analysis to obtain the life error under each corresponding condition, that is, y = |y EXP -y FEM |.

[0069] S3 establishes a data-driven model between multi-boundary input parameters and the life error, takes the minimum error value as the optimization goal, uses the Kriging proxy model to construct the correspondence between the input characteristic variables x and y, and optimizes the error y to obtain the optimal boundary conditions. The results are updated back to the numerical simulation to obtain the optimal finite element model and the optimal boundary conditions.

[0070] Further, in S3, S31: construct variable X = [x 1 ,x 2 ,x 3 ,…,x n ] T The corresponding result Y={y (1) ,y (2) ,…,y (n)} T The corresponding relationship expression of , the Kriging model expression is selected as:

[0071] y(x)=f T (x)β+z(x)

[0072] Where β is the regression coefficient; f(x) is the regression function about x; the random term z(x) is a random function with a mean of zero and a covariance of:

[0073]

[0074] Among them, R(θ,x i ,x j ) represents any two sample points x i and x j The spatial correlation degree of θ is the model parameter to be trained, σ is the Z Represents the variance of the random function z(x). The correlation between two points in the Kriging surrogate model is related to the distance between the two points. The correlation function obtained using the exponential form is expressed as:

[0075]

[0076] Where n is the number of variables, x i k and x j k is the sample point x i and x j The kth component of k Indicates the model parameters to be trained under the specified component sample points.

[0077] Furthermore, in S3, S32: a genetic algorithm is used to optimize the agent model. The optimization model is shown in the following formula to achieve the optimization result with the minimum y value:

[0078]

[0079] Among them, x 1-min 、x 1-max Represent the minimum and maximum values ​​of the variable x1, x 2-min 、x 2-max Represent the minimum and maximum values ​​of the variable x2, x n-min 、x n-max Represents the variable x n In step S32, by searching for the minimum y value, the best optimization result value of each variable is obtained, thereby forming the optimal combination factor data set.

[0080] Further, in S3, S33: after obtaining the optimal x boundary value, the value is updated back to the finite element model, so as to further calculate the life of the SIS logic controller based on the optimal finite element model.

[0081] Based on a finite element numerical model with optimal boundary conditions, S4 applies different input currents within the safe current range to obtain a large amount of lifespan data for SIS logic controllers, i.e., obtains secondary lifespan data corresponding to different input current samples. In combination with typical Arrhenius failure physics methods for electronic devices, a failure physics model suitable for SIS logic controllers is constructed.

[0082] Further, in S4, S41: according to the safe current range in the manual, apply a current range (i±25%i) to the board chip to obtain the life results t of group q.

[0083] Furthermore, in S4, S42: According to the failure physics equation, the characteristic life t of the reliability life of electronic products is often expressed by the highest chip junction temperature on the board. Since the junction temperature is positively correlated with the current value, the life t of this patent is expressed by the rated current i:

[0084]

[0085] Where E is the failure acceleration energy and k is the Boltzmann constant. Substitute t and i obtained in step S41 into the above formula and fit it into the established life evaluation model. Through the established life evaluation model and life data, the coefficients A and -E of the failure physical equation can be further obtained. a / k.

[0086] Example 3

[0087] Based on the above-mentioned first and second embodiments, the present invention further provides another specific embodiment of a method for evaluating the life of a SIS logic controller.

[0088] In step T1, based on the field working conditions of the petrochemical industry, the characteristics of the external condition variables affecting the SIS logic controller and the numerical range of each characteristic parameter are fully considered. The Latin hypercube sampling method is used to design preliminary experimental variables and determine the initial sample space X.

[0089] In this embodiment, Figure 2 This is a flow chart of the step of determining an initial sample space in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0090] Based on the characteristic variables of the operating conditions of SIS logic controllers in the petrochemical industry, a library of external input variable conditions X and the numerical ranges of each condition that affect the reliability of SIS logic control are established. The ambient temperature T, board spacing d, and vibration amplitude A are used as external input variables. The ranges of the input variables are shown in Table 1:

[0091] Table 1 Input feature variable range

[0092]

[0093] Filling the space X=[x based on the optimal Latin hypercube criterion T ,x A ,x d ] T , construct 20 initial samples of 3 variables through normalization. Figure 3 This is an example diagram of the spatial distribution of initial sample points in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application. The three-dimensional spatial distribution constructed based on the above 3 variables and 20 initial samples is as follows: Figure 3 shown.

[0094] Combined with the characteristic parameter range of the boundary conditions in Table 1, the initial sample space is denormalized to obtain the partial experimental table shown in Table 2.

[0095] Table 2 Input features and output result samples

[0096]

[0097] Next, step T2 begins, sequentially loading the finite element model based on the initial boundary condition sample constructed using the Latin hypercube. Rated current is applied to the board chip, and multi-physics simulation is performed using the Comsol fatigue module. Failure is determined by exceeding the chip's fatigue limit, and the finite element simulation lifespan of the SIS logic controller is obtained. Based on the characteristics of electronic components, failure is often caused by Joule heat accumulation due to electro-thermal effects after prolonged operation, leading to chip breakdown.

[0098] On the other hand, building an accelerated aging test platform can provide external test variables included in X to meet the test range. The simulation results are compared with the accelerated life test under the same conditions to obtain the service life error Y of the SIS logic controller.

[0099] In this embodiment, Figure 4 This is a flow chart of the step of optimizing the boundary conditions of the finite element model in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application.

[0100] like Figure 4 As shown in the figure, after determining the initial sample space, the actual lifespan of the SIS logic controller obtained experimentally is compared with the lifespan calculated using finite element numerical simulation to obtain the SIS logic controller lifespan error, Y. The experimental lifespan results are obtained by first establishing an accelerated aging test platform and providing the external test variables, including X, to meet the test range. Then, an accelerated aging experiment is conducted using the same boundary conditions as the finite element numerical simulation. Finally, the actual lifespan of the SIS logic controller under the accelerated aging experiment is obtained.

[0101] The steps for calculating the service life through finite element numerical simulation are as follows: First, boundary conditions are set based on the X range to create a 3D geometric model, set material properties, and mesh the finite element model. Then, a multi-physics simulation is performed based on the thermal stress failure physics model. Finally, the service life of the SIS logic controller is calculated.

[0102] In this embodiment of the present invention, a 3D finite element model of the SIS logic controller was constructed using Comsol software. The rated operating current of 5A was applied to the chip of the board integrated circuit, and the processes of material property setting, meshing, and heat dissipation coefficient setting were completed. The boundary condition samples shown in Table 2 were applied accordingly. In the fatigue module, the linear cumulative damage theory was combined, and the experimental cutoff was set at 75% of the rated temperature. The finite element simulation life results y of the SIS logic controller were obtained. FEM Through the accelerated aging test platform, the accelerated aging test is carried out under the same boundary conditions and current conditions to obtain the experimental life y EXP , calculate the difference between the two results y=|y EXP -y FEM As shown in Table 2.

[0103] After obtaining the life error of the SIS logic controller, step T3 proceeds to establish a data-driven model between the multi-boundary input parameters and the life error. Minimizing this error is the optimization objective. Using a Kriging surrogate model, the correspondence between the input characteristic variables X and Y is constructed, and the error Y is optimized to obtain the optimal boundary conditions. During the solution, a hybrid point addition criterion is employed to ensure that the surrogate model meets convergence requirements or that the number of points added reaches an upper limit. The optimized result for X is output when Y is minimized. The results are then updated back into the numerical simulation to obtain the optimal finite element model.

[0104] like Figure 4 As shown in the figure, after obtaining the service life error of the SIS logic controller, an error compensation proxy model is constructed to optimize the boundary conditions of the finite element model. The steps for constructing the error compensation proxy model are: first prepare the initial data and the corresponding response value, then select the modeling method, construct the Kriging proxy model to establish the mapping relationship between the error Y and the input condition X, obtain the update point based on the mixed point addition criterion, and calculate the response value of the update point. According to the new sample points and the corresponding response values, it is judged whether the current relationship model meets the convergence requirements or whether the number of added points reaches the upper limit. If the conditions are met, the result of X corresponding to the current minimum Y value is output, and the output X value is used to update the boundary conditions set in the finite element numerical simulation. If the conditions are not met, the sample points are added based on the point addition criterion and returned to the previous step, the sample points and the response values ​​of the newly added sample points are added to the sample set, and the initial proxy model is updated.

[0105] In this embodiment, a library of external input variable conditions that affect the reliability of SIS logic control is constructed. T ,x A ,x d ] T The corresponding result Y={y1,y2,…,y 20 The corresponding expression of}, the minimum y value optimization expression based on the Kriging model is:

[0106]

[0107] Genetic algorithm is used to optimize the agent model, and the optimization result of achieving the minimum y value is [x T =38,x A =29.3,x d =66.7], y = 30.56. The above optimal X boundary condition characteristic parameters are updated back to the finite element model.

[0108] Next, in step T4, based on the finite element numerical model with optimal boundary conditions, current i is applied within the safe current range to obtain multiple sets of lifespan data. Incorporating typical Arrhenius failure physics methods for electronic devices, the failure physics equation for the SIS logic controller is constructed. Four-fifths of the lifespan data sets are selected for fitting the coefficients of the failure physics equation. The remaining one-fifth of the data is used for model verification, and the coefficients are revised.

[0109] In an embodiment of the present invention, Figure 5 This is a flow chart of constructing a SIS logic controller failure physical equation in the method for evaluating the life of a SIS logic controller according to an embodiment of the present application. Figure 5 As shown in Figure 1, the steps for constructing the SIS logic controller failure physics equation are as follows: First, under optimal boundary conditions, apply a current i within the rated range to the chip to obtain q sets of lifetime sample results. Next, the SIS logic controller failure physics equation is constructed. The data results from 80% of the sample are used to calculate the unknown coefficients in the equation. The data results from the remaining 20% ​​of the sample are then used to verify and revise the equation coefficients.

[0110] Specifically, in step T41, under the optimal boundary conditions, the current range i = [3.5A, 6.25A] is applied to the board chip, and other parameter settings remain unchanged. 100 sets of life results y are obtained. FEM To distinguish the results from Table 2, the life results under the optimal boundary conditions are represented by t, and the result is t = [240.5, 240, ..., 243].

[0111] In step T42, according to the failure physics equation, the life t and the rated current i are expressed as:

[0112]

[0113] The coefficients A and -E in the characteristic life model are calculated using the first 80 sets of data obtained in step S41. a / k is fitted to obtain the initial coefficients A=8002 and -E a / k=0.12. Substituting into the above formula, the fitting result was verified by using the last 20 sets of life data and further corrected to obtain the final results A=8001.55 and -E a / k=0.122.

[0114] Example 4

[0115] Based on the above-described first to third embodiments, the present invention provides a computer-readable storage medium storing a computer program. Executing the computer program executes a series of instructions for executing steps in a method for evaluating the life of a SIS logic controller. The computer program is capable of executing computer instructions, which include computer program code. The computer program code may be in source code form, object code form, an executable file, or some intermediate form.

[0116] Computer-readable storage media may include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0117] It should be noted that the content contained in computer-readable storage media can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, the content contained in computer-readable storage media does not include electric carrier signals and telecommunication signals.

[0118] Example 5

[0119] Based on the above-mentioned embodiments 1 to 3, an embodiment of the present invention further provides a system for evaluating a SIS logic controller, which is used to implement the above-mentioned method for evaluating the life of a SIS logic controller. Figure 6 Schematic diagram of the overall structure of the system for evaluating the life of SIS logic controller according to the embodiment of the present application. Figure 6As shown, the system includes: an influencing factor sample space generation module 61, a life error sample data generation module 62, an optimal combination factor generation module 63, a life evaluation model generation module 64 and an online evaluation module 65.

[0120] Specifically, the influencing factor sample space generation module configuration 61 is implemented according to the method described in the above step S110, and is configured to generate an initial sample space according to the factors affecting the working condition of the SIS logic controller; the life error sample data generation module 62 is implemented according to the method described in the above step S120, and is configured to load the samples under each combination factor in the initial sample space on the preset finite element model, obtain the first life result under the corresponding combination factor, and then compare the first life result with the result of the accelerated life test under the same conditions to obtain the life error; the optimal combination factor generation module 63 is implemented according to the method described in the above step S130, and is configured to establish a relationship between the input parameter characterizing the combination factor and the life error parameter. The data-driven model of the relationship between the two is constructed, and the data-driven model is solved according to the samples of each combination factor and the corresponding life error to obtain the optimal combination factor data group; the life evaluation model generation module 64 is implemented according to the method described in the above step S140, and is configured to obtain the second life data of the optimal combination factor data group under different input current conditions, so as to construct a life evaluation model suitable for the SIS logic controller based on the basic failure physical model and using different input current data and the corresponding second life data; the online evaluation module 65 is implemented according to the method described in the above step S150, and is configured to obtain a dynamic life evaluation result by using the life evaluation model according to the real-time input current data of the SIS logic controller to be evaluated.

[0121] The present invention discloses a method and system for evaluating the lifespan of SIS logic controllers. This method and system consider operating conditions within a petrochemical environment, such as board spacing, ambient humidity, and vibration. These boundary condition features are incorporated into the finite element model during model establishment to more accurately reflect the lifespan of the SIS logic controller under realistic operating conditions. Furthermore, when setting boundary condition parameters, an optimal Latin hypercube is used to construct the sample space to improve the spatial fillness of the sample. Furthermore, the present invention organically combines finite element numerical simulation with accelerated aging experiments, calculating the error between the finite element model and the experiment under corresponding conditions. The relationship between the input boundary condition characteristic parameters and the error is then established, and the Kriging surrogate model is used to optimize the error. The boundary conditions of the finite element model are updated to obtain an optimal finite element model that closely matches the experiment. Using the optimal finite element model significantly reduces the number of experimental samples, saving manpower and financial resources, while ensuring a sufficient sample size and improving the accuracy of subsequent lifespan model establishment. Furthermore, the present invention compares multiple accelerated lifespan models, comparing indicators such as the correlation coefficient and mean error (RMSE) of the lifespan models to establish an accelerated lifespan model suitable for SIS logic controllers, providing guidance for practical applications.

[0122] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by anyone skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0123] In the description of the present invention, unless otherwise specified, "plurality" means two or more; terms such as "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," and "tail" indicate positions or relationships based on those shown in the accompanying drawings. These terms are intended solely to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, terms such as "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0124] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integral connection; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0125] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should extend to equivalent substitutions of these features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0126] References in this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "one embodiment" or "an embodiment" in various places throughout this specification do not necessarily refer to the same embodiment.

[0127] Although the embodiments disclosed above are for facilitating understanding of the present invention, the contents described are merely embodiments adopted for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art of the present invention may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for evaluating the life of a SIS logic controller, characterized in that: include: Step 1: Generate an initial sample space based on the factors that affect the operating conditions of the SIS logic controller; Step 2: Load the samples under each combination of factors in the initial sample space onto a preset finite element model to obtain a first life result under the corresponding combination of factors, and then compare the first life result with the result of an accelerated life test under the same conditions to obtain a life error; Step 3: Establish a data-driven model that characterizes the relationship between the combination factor input parameters and the life error parameters, and solve the data-driven model based on each combination factor sample and the corresponding life error to obtain the optimal combination factor data set; Step 4: Obtain the second life data of the optimal combination factor data group under different input current conditions, thereby constructing a life evaluation model suitable for the SIS logic controller based on the basic failure physics model using different input current data and the corresponding second life data; Step 5: Based on the real-time input current data of the SIS logic controller to be evaluated, the life evaluation model is used to obtain a dynamic life evaluation result, wherein the step 4 includes: Based on a three-dimensional finite element model of the SIS logic controller, the optimal combination factor data set is used as the controller operating environment condition; Apply multiple sets of input current data to the controller in the finite element model to obtain second life data under different input current sample conditions; Based on the basic failure physics model, according to different input current samples and the corresponding second life data, the life evaluation model that characterizes the relationship between current and life is obtained.

2. The method according to claim 1, characterized in that The step one comprises: Determine various variable factors and corresponding parameter ranges; According to the parameter range of each variable factor, the optimal Latin hypercube criterion is used to fill the sample space to form a three-dimensional sample space distribution; Normalizing the three-dimensional sample space distribution to obtain the initial sample space.

3. The method according to claim 1 or 2, characterized in that The process of generating the first lifespan result includes: A three-dimensional finite element model of the SIS logic controller is constructed, and the rated current is loaded on the current controller. Then, samples under various combination factors in the initial sample space are sequentially loaded onto the three-dimensional finite element model. Combined with the linear cumulative damage theory, the first life results under different sample loading conditions are obtained when the controller chip temperature exceeds the preset threshold temperature.

4. The method according to claim 1 or 2, characterized in that The process of solving the data-driven model includes: continuously adjusting the factor data in the initial sample space so that the life error parameter output by the model reaches the minimum value, thereby obtaining the optimal combination factor data group.

5. The method according to claim 4, characterized in that The step three includes: The first step is to fit the data-driven model according to the samples of each combination factor and the corresponding life error to obtain the initial relationship model; The second step is to obtain a new input sample point based on the mixed point addition criterion, and obtain the response value of the current new sample point using the initial relationship model; The third step is to determine whether the current relationship model meets the application requirements based on the new sample points and the corresponding response values, using the preset convergence judgment conditions. If so, the numerical range of each factor corresponding to the minimum life value of the current relationship model is used as the optimal combination factor data group.

6. The method according to claim 5, characterized in that If the current relationship model does not meet the application requirements, new sample points are added based on the point addition criteria and the corresponding response values ​​are obtained using the current initial relationship model, thereby returning to the first step to update the initial relationship model based on the combined factor samples and the corresponding life errors, as well as the newly added sample points and their response values.

7. The method according to claim 4, characterized in that The data driven model is implemented using the Kriging proxy model.

8. The method according to claim 1, characterized in that The step of obtaining the life evaluation model characterizing the relationship between current and life based on the basic failure physics model and according to different input current samples and corresponding second life data includes: According to the input current sample and the corresponding second life data sample, a training data group and a verification data group are obtained according to a preset ratio of the training data amount to the verification data amount, wherein the ratio of the training data amount to the verification data amount is 4:1; Fitting the basic failure physics model according to the training data set to obtain an initial life prediction model containing initial fitting coefficients; The initial life prediction model is verified based on the verification data set to update the initial fitting coefficients to obtain the life evaluation model containing the updated fitting coefficients.

9. The method according to claim 1, characterized in that The basic failure physics model is expressed using the following expression: in, t Represents the life parameter, E represents the failure acceleration energy, k represents the Boltzmann constant, i Indicates current parameter, A, Represent the coefficients to be fitted.

10. A computer-readable storage medium, characterized in that It contains a series of instructions for executing the method steps according to any one of claims 1 to 9.

11. A system for evaluating the life of a SIS logic controller, characterized in that: include: an influencing factor sample space generation module configured to generate an initial sample space according to various factors influencing the operating conditions of the SIS logic controller; a life error sample data generation module configured to load samples under various combination factors in the initial sample space onto a preset finite element model, obtain a first life result under the corresponding combination factors, and then compare the first life result with a result of an accelerated life test under the same conditions to obtain a life error; an optimal combination factor generation module configured to establish a data-driven model characterizing the relationship between the combination factor input parameters and the life error parameters, and solve the data-driven model based on each combination factor sample and the corresponding life error to obtain an optimal combination factor data set; a life evaluation model generation module configured to obtain second life data of the optimal combination factor data group under different input current conditions, thereby constructing a life evaluation model suitable for the SIS logic controller based on the basic failure physics model using the different input current data and the corresponding second life data; The online evaluation module is configured to obtain a dynamic life evaluation result using the life evaluation model based on the real-time input current data of the SIS logic controller to be evaluated, wherein the life evaluation model generation module is further configured to: Based on a three-dimensional finite element model of the SIS logic controller, the optimal combination factor data set is used as the controller operating environment condition; Apply multiple sets of input current data to the controller in the finite element model to obtain second life data under different input current sample conditions; Based on the basic failure physics model, according to different input current samples and the corresponding second life data, the life evaluation model that characterizes the relationship between current and life is obtained.

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