Multi-fault-mode reliability simulation analysis method for electromechanical equipment

Abstract

The invention discloses an electromechanical equipment multi-fault mode reliability simulation analysis method, which comprises the following steps: establishing a fault tree of electromechanical equipment, determining a plurality of fault modes as bottom events of the fault tree, and establishing a fault physical model of functional characteristic quantities of the bottom events and bottom layer basic design variables; taking the reliability input variable as a random variable of the fault physical model, and carrying out parameter setting and sampling on the random variable; based on the sampling result of the random variable parameters, establishing an explicit model, an implicit model or a kriging response surface agent model as a fault physical model for a plurality of fault modes; performing reliability simulation analysis by using a Monte Carlo adaptive important sampling method to obtain the comprehensive reliability of the electromechanical equipment in the multi-fault mode; and optimizing the parameters of the reliability input variables, and repeating simulation analysis until the comprehensive reliability meets the requirement. Coupling reliability analysis of multiple fault modes of the electromechanical equipment can be realized, and the reliability level of the electromechanical equipment is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of reliability simulation analysis, and specifically relates to a reliability simulation analysis method for multiple failure modes of electromechanical equipment, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Electromechanical equipment is a complex piece of equipment that integrates multiple physical processes, including mechanical, electrical, and hydraulic processes, and multiple technology units, into a mechanical carrier to form its overall functionality. Its operating environment is harsh, repair and maintenance are difficult, and it requires higher precision and a longer lifespan. During its long service life, the dynamic characteristics of electromechanical equipment continuously change, and faults may propagate and transform, exhibiting the coexistence of multiple failure modes and mechanisms. Electromechanical equipment often has multiple failure modes that coexist during use. The coupling between product components and influencing factors leads to the interconnectedness of multiple failure modes, making them impossible to consider independently. Therefore, reliability analysis of multiple failure modes requires consideration of the combined effects of each component and influencing factor. Summary of the Invention

[0003] The purpose of the present invention is to provide a reliability simulation analysis method for multiple fault modes of electromechanical equipment, a computer device, a computer-readable storage medium and a computer program product, which can realize the coupled reliability analysis of multiple fault modes of electromechanical equipment and improve the rapid design optimization and reliability level of electromechanical equipment.

[0004] One aspect of the present invention provides a multi-failure mode reliability simulation analysis method for electromechanical equipment, comprising: Step S1: Establish a fault tree for the electromechanical equipment. Based on the various stresses of the electromechanical equipment and the coupling effects of multiple components, determine multiple failure modes as bottom events of the fault tree, and establish a fault physical model of functional characteristic quantities of the bottom events and underlying basic design variables, wherein the functional characteristic quantities represent reliability output variables of the corresponding failure modes of the electromechanical equipment, and the underlying basic design variables represent reliability input variables of the corresponding failure modes of the electromechanical equipment. Step S2: Using the reliability input variable as a random variable of the fault physics model, setting the parameters of the random variable, and sampling the random variable parameters using the Latin hypercube sampling method; Step S3, based on the sampling results of the random variable parameters, establishing explicit models, implicit models or kriging response surface proxy models as fault physical models for multiple fault modes; Step S4, simultaneously driving the explicit model, implicit model or kriging response surface proxy model, using the Monte Carlo adaptive importance sampling method to perform reliability simulation analysis to obtain the comprehensive reliability of the electromechanical equipment under multiple fault modes; Step S5, judging whether the comprehensive reliability meets the requirements. If it does not meet the requirements, optimizing the parameters of the reliability input variables, and repeating steps S2 to S4 until the comprehensive reliability meets the requirements.

[0005] Preferably, the functional characteristic quantities include force, deformation, life and performance parameters, and the underlying basic design variables include size, material and load parameters.

[0006] Preferably, in step S2, the parameters of the random variables are set as follows: Load the sample data corresponding to the random variable; Select the distribution type and calculate the distribution parameters by fitting the sample data; Evaluate the random variable based on sample data and fitting method to obtain the final distribution type and distribution parameters; Set the lower and upper bounds for the random variable.

[0007] Preferably, the distribution types include uniform distribution, binomial distribution, normal distribution, lognormal distribution, exponential distribution and Poisson distribution.

[0008] Preferably, the electromechanical equipment is an electric pump, the multiple stresses include load, pressure, and vibration, the multiple components include a housing base, a front cover, and a circuit board, and the multiple failure modes include housing base fatigue failure, front cover failure, and circuit board failure.

[0009] Preferably, the thickness of the bracket, the depth of the front end cover, the vibration spectrum amplification factor, and the SN curve amplification factor are selected as reliability input variables, and the lifespan is selected as the reliability output variable.

[0010] Preferably, a kriging response surface proxy model is established for the fatigue failure of the housing base, an implicit model is established for the failure of the front cover, and an explicit model is established for the failure of the circuit board.

[0011] Another aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0012] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0013] Yet another aspect of the present invention provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0014] The reliability simulation analysis method for multiple fault modes of electromechanical equipment, computer equipment, computer-readable storage medium, and computer program product according to the above aspects of the present invention can realize coupled reliability analysis of multiple fault modes of electromechanical equipment, thereby improving the rapid design optimization and reliability level of electromechanical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts. Figure 1 is a flow chart of a method for simulating and analyzing reliability of multiple failure modes of electromechanical equipment according to an embodiment of the present invention; Figure 2 Schematic diagram of a fault tree for reliability simulation analysis of multiple fault modes according to an embodiment of the present invention; Figure 3 4 is a structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0017] The embodiment of the present invention provides a reliability simulation analysis method for multiple failure modes of electromechanical equipment. Figure 1 FIG. 1 is a flow chart of a method for simulating and analyzing reliability of electromechanical equipment using multiple fault modes according to an embodiment of the present invention. Figure 1 As shown, the multi-failure mode reliability simulation analysis method of electromechanical equipment according to the embodiment of the present invention includes steps S1 to S5.

[0018] In step S1, a fault tree of the electromechanical equipment is established. According to the various stress effects and coupling effects of multiple components of the electromechanical equipment, multiple failure modes are determined as the bottom events of the fault tree, and a fault physical model of the functional characteristic quantities of the bottom events and the underlying basic design variables is established.

[0019] Fault tree analysis can reveal multiple failure modes for electromechanical equipment (products). It also allows for the direct establishment of logical relationships between bottom and top events, linking multiple failure modes and meeting the requirements for coupled analysis of multiple failure modes. In actual reliability analysis, in addition to establishing logical relationships between top and bottom events, functional relationships can also be established between the top event (product failure) state of the fault tree and the underlying design parameters.

[0020] By expanding the fault tree downward based on traditional fault tree analysis, we can construct a fault physics model for the bottom event and generate limit state equations. These limit state equations can be explicit models, such as functions or multivariate formulas, implicit models, such as finite element simulation models, or proxy models, such as kriging response surface proxy models. Through limit state function gates, we establish a fault physics model for the bottom event functional characteristics (parameters used to characterize the bottom event state, such as force, deformation, life, and performance parameters) and the underlying basic design variables (dimensional parameters, material parameters, and loads).

[0021] The functional characteristic quantity represents the reliability failure criterion or output variable of the product corresponding to the failure mode, and the underlying basic design variable represents the reliability influencing parameter or input variable of the product corresponding to the failure mode.

[0022] In step S2, the reliability input variable is used as the random variable of the fault physical model, the parameters of the random variable are set, and the Latin hypercube sampling method is used to sample the random variable parameters.

[0023] Specifically, based on the fault physical model and the corresponding reliability input variables and output variables, the impact of the randomness of the input variables on the reliability of the product is considered, the input variables are used as random variables of the fault physical model, and the parameters of the random variables are set.

[0024] The distribution characteristics of random variables are the basis of reliability analysis. The distribution type of random variables can be estimated based on the parameters of experimental / measured data. Distribution types generally include uniform distribution, binomial distribution, normal distribution, lognormal distribution, exponential distribution, Poisson distribution, etc.

[0025] Random variable parameter settings can be automatically fitted with the distribution type and parameters of random variables through data fitting tools, or they can be directly defined based on known random characteristics. Random variable parameter settings include: (1) Load variable samples When choosing to perform parameter fitting using known random data, you need to first load the random data corresponding to the random variable; (2) Define the fitting method You can select a specific distribution type and calculate the distribution parameters by fitting the sample data, or you can use the software tool to automatically select the optimal distribution type and corresponding distribution parameters; (3) Distribution attribute fitting Evaluate random variables based on sample data and fitting methods to obtain the final distribution type and distribution parameters. The software can automatically calculate its statistical parameters for preliminary evaluation of the distribution parameters. (4) Distribution type and parameter configuration Select the distribution type corresponding to the random variable. For most common distribution types, you can get the corresponding distribution function, PDF and CDF curves; (5) Define the truncated state Set the upper and lower limits of the random variable. You can set only the upper limit or only the lower limit.

[0026] On the basis of random variable parameter setting, random variable parameter sampling adopts Latin hypercube sampling method. Latin hypercube sampling has the characteristic of uniform stratification. At the same time, it can obtain the sample value of the tail with less sampling, thereby reducing the number of runs required for simulation. Therefore, the parameter sampling of the present invention adopts Latin hypercube sampling.

[0027] In step S3, based on the sampling results of the random variable parameters, explicit models, implicit models or kriging response surface proxy models are established for multiple fault modes as fault physical models.

[0028] 1. Explicit Model-Driven Simulation Based on random variable parameter sampling, for explicit fault physical models, the mathematical model of the analysis object is mainly established through explicit expressions or codes, and the explicit mapping relationship between input variables and output variables is constructed. The simulation is driven according to the random sampling results of the input variables of the mathematical model, so as to obtain the distribution form of the output variables of the mathematical model.

[0029] As a function, the explicit model needs to define the input and output variables separately, and then establish the relationship between the input and output through formula expressions. The explicit model modeling process is: (1) Input variable analysis According to the relationship between the nodes defined in the fault tree, all child nodes of the explicit model node and their corresponding variables are automatically edited. One node can correspond to multiple variables, and all input variables can be obtained. (2) Output variable setting Automatically determine whether the explicit model supports multiple output variables based on the type of the parent node. If it supports a single variable, automatically create the input variable. If it supports multiple output variables, create additional variable names. (3) Define explicit formula After obtaining the input and output variables, the relationship between the input and output variables is described by formulas, following the Python syntax standard. At the same time, formulas in the formula library can be directly referenced, including typical mechanical part models, logic gates, and standard mathematical functions.

[0030] 2. Implicit Model Driven Simulation For implicit fault physical models, such as finite element simulation models, modeling is mainly done by integrating third-party software. When the object of analysis does not have an explicit formula, it is necessary to use other mature commercial analysis software such as finite element analysis software and dynamic analysis software to establish implicit functional functions. The third-party calculation software is linked to the software platform to drive the simulation calculation of the implicit model. The implicit model modeling process is as follows: (1) Input and output variable definitions Same modeling function as explicit model; (2) Input file mapping Establish a relationship between input variables and input files. During the iteration process, the software modifies the third-party model based on this relationship to achieve the purpose of updating the model. Unassociated input variables will be directly ignored. It supports establishing mapping relationships with multiple input files. (3) Implicit model driven For different third-party programs, select or create different driver commands, which are used to call the target program in the background to execute the modified input file and obtain the output file; (4) Output file mapping Establish a relationship between output variables and output files. During the iteration process, the software reads the calculation results of each iteration based on this relationship. All output variables need to be mapped. It also supports establishing mapping relationships with multiple output files.

[0031] 3. Proxy Model Driven Simulation A surrogate model is a simulation model based on the Kriging response surface model, which further develops the implicit model. It replaces the complex implicit model in reliability simulation calculations and drives the simulation based on random sampling of the surrogate model's input variables. Compared to other traditional interpolation techniques, the Kriging response surface model offers two advantages. First, it is based on the dynamic construction of known information, simulating unknown information using only certain information near the estimated point, rather than all information. Second, it possesses both local and global statistical properties, which enable the Kriging response surface to analyze trends and dynamics in known information.

[0032] The surrogate model can be trained directly using the generated training samples or by importing training samples from an external finite element model or other methods. Machine learning methods can also be used to generate surrogate models. Support vector regression (SVR) is a type of machine learning method that maps data into a high-dimensional space using a kernel function and constructs an optimal separating hyperplane within that space. While machine learning generally requires a large amount of training data, SVR methods are less data-intensive and are therefore suitable for situations where training samples are obtained through finite element calculations.

[0033] In step S4, a Monte Carlo adaptive importance sampling method is used to perform reliability simulation analysis under multiple fault modes to obtain the reliability of the electromechanical equipment under the multiple fault modes.

[0034] The reliability simulation analysis calculation of the embodiment of the present invention is based on the probability theory method. The relationship between the input variables and the basic variables of the output response quantity is established through the fault tree, and the sample data of the basic variables are collected using statistical methods to obtain the statistical laws of the basic variables. Then, a model-driven method is adopted to transfer the statistical laws of the basic variables to the output response quantity, and finally the reliability calculation of multiple fault modes is performed based on the statistical laws of the output response quantity.

[0035] According to the input and output parameters of each fault mode, the Monte Carlo adaptive importance sampling method is used to perform reliability simulation analysis under multiple fault modes, so as to obtain the reliability of electromechanical equipment under multiple fault modes.

[0036] In step S5, it is determined whether the comprehensive reliability meets the requirements. If it does not meet the requirements, the parameters of the reliability input variables are optimized, and steps S2 to S4 are repeated until the comprehensive reliability meets the requirements.

[0037] The following describes a specific implementation example to illustrate the multi-failure mode reliability simulation analysis method for electromechanical equipment according to an embodiment of the present invention.

[0038] In this embodiment, the electromechanical device is an electric pump, the multiple stresses include load, pressure, and vibration, the multiple components include a housing base, a front cover, and a circuit board, and the multiple failure modes include fatigue failure of the housing base, failure of the front cover, and failure of the circuit board.

[0039] 1) Establish a fault tree for an electric pump on the software platform, such as Figure 2 As shown in the figure, considering the multiple stress effects of load, pressure, vibration and other factors on a certain electric pump and the coupling effects of multiple electronic components, the three key failure modes of the housing base fatigue failure, front cover failure and circuit board failure are taken as the bottom events; 2) Considering the characteristics of a specific electric pump, reliability simulation analysis was conducted on the initial design and improved solutions targeting the weak links (i.e., bracket thickness and front cover depth) of the electric pump. Based on the three failure modes mentioned above, the dimensions (with bracket thickness and front cover depth as input variables), load (with vibration spectrum amplification factor as input variable), and material (with SN curve amplification factor as input variable) were selected, with product life as the output variable. Reliability input variables for the bottom events of the three failure modes were sampled simultaneously. The distribution types and parameters of the reliability input variables are detailed in Table 1. The failure criterion for all three failure modes was a lifespan of at least 2000 hours. 3) A Kriging response surface proxy model was established for the fatigue failure of the housing base. An implicit model was established for the failure of the front cover by calling third-party simulation software. A statistical explicit model for the failure of the circuit board was established based on the reliability calculation results of the PWA software under temperature and vibration conditions. 4) Simultaneously driving the mathematical model of three failure modes, the comprehensive reliability of a certain electric pump under multiple failure modes was ultimately calculated through reliability simulation analysis. The simulation results showed that the reliability level under the initial design scheme was low and did not meet the product requirements; 5) After optimizing and improving the weak links of the electric pump (i.e., bracket thickness and front cover depth), the reliability simulation analysis results of the initial and improved designs are compared in Table 2. The calculation results show that the overall reliability of the electric pump has been significantly improved by comprehensively considering the coupling of multiple stresses such as pressure, load, and vibration, and the interactive effects of multiple failure modes such as housing base fatigue failure, front cover failure, and circuit board failure.

[0040] Table 1 Distribution types and parameters of reliability input variables

[0041] Table 2 Reliability simulation analysis results of the initial design scheme and improved scheme of the electric pump

[0042] In summary, the method of the present invention addresses the complex reliability simulation models and numerous influencing parameters for multiple fault modes. By treating multiple fault modes as a composite event, a complete machine reliability simulation model is constructed based on a fault tree. The complete machine reliability simulation model simultaneously considers the influencing parameters and resulting characteristic parameters of multiple fault modes, meeting the modeling requirements of simultaneously considering multiple model parameters and multiple stress coupling conditions. Furthermore, the influencing parameters are synchronously randomized according to known data distribution types, and a comprehensive reliability simulation of multiple fault modes is conducted. This results in the comprehensive reliability of the multiple fault modes and the reliability level of the complete machine, achieving the purpose of reliability simulation analysis of multiple fault modes in electromechanical equipment.

[0043] The method according to an embodiment of the present invention associates coupled reliability simulation analysis of multiple fault modes through a fault tree-based approach, which can simultaneously satisfy the common drive of implicit models, proxy models, and explicit models, and can simultaneously consider the coupled effects of functional characteristic variables and basic design variables. The method according to an embodiment of the present invention has the following beneficial effects: 1) It can meet the needs of coupled reliability analysis of multiple failure modes of electromechanical equipment; 2) It provides a comprehensive reliability simulation analysis method, which avoids the difficulty of analyzing the impact of multiple failure modes and the coupling of various model parameters, and can fully consider multiple failure modes and various model parameters for comprehensive reliability analysis; 3) It provides an efficient comprehensive reliability simulation analysis method for electromechanical equipment, which helps to quickly optimize the design and improve the reliability level of electromechanical equipment.

[0044] The embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store operating parameter data of each framework. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the method of the embodiment of the present invention are implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0046] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method of the embodiment of the present invention are implemented.

[0047] An embodiment of the present invention further provides a computer program product, including a computer program, which implements the steps of the method of the embodiment of the present invention when executed by a processor.

[0048] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A reliability simulation analysis method for multiple failure modes of electromechanical equipment, characterized in that: include: Step S1: Establish a fault tree for the electromechanical equipment. Based on the various stresses of the electromechanical equipment and the coupling effects of multiple components, determine multiple failure modes as bottom events of the fault tree, and establish a fault physical model of functional characteristic quantities of the bottom events and underlying basic design variables, wherein the functional characteristic quantities represent reliability output variables of the corresponding failure modes of the electromechanical equipment, and the underlying basic design variables represent reliability input variables of the corresponding failure modes of the electromechanical equipment. Step S2: Using the reliability input variable as a random variable of the fault physics model, setting the parameters of the random variable, and sampling the random variable parameters using the Latin hypercube sampling method; Step S3, based on the sampling results of the random variable parameters, establishing explicit models, implicit models or kriging response surface proxy models as fault physical models for multiple fault modes; Step S4, simultaneously driving the explicit model, implicit model or kriging response surface proxy model, using the Monte Carlo adaptive importance sampling method to perform reliability simulation analysis to obtain the comprehensive reliability of the electromechanical equipment under multiple fault modes; Step S5, judging whether the comprehensive reliability meets the requirements. If it does not meet the requirements, optimizing the parameters of the reliability input variables, and repeating steps S2 to S4 until the comprehensive reliability meets the requirements.

2. The method according to claim 1, wherein Functional characteristic quantities include force, deformation, life and performance parameters, and the underlying basic design variables include size, material and load parameters.

3. The method according to claim 1 or 2, wherein: In step S2, the parameters of the random variables are set as follows: Load the sample data corresponding to the random variable; Select the distribution type and calculate the distribution parameters by fitting the sample data; Evaluate the random variable based on sample data and fitting method to obtain the final distribution type and distribution parameters; Set the lower and upper bounds for the random variable.

4. The method according to claim 3, wherein The distribution types include uniform distribution, binomial distribution, normal distribution, lognormal distribution, exponential distribution and Poisson distribution.

5. The method according to claim 1 or 2, wherein: The electromechanical equipment is an electric pump, the multiple stresses include load, pressure, and vibration, the multiple components include a housing base, a front cover, and a circuit board, and the multiple failure modes include fatigue failure of the housing base, failure of the front cover, and failure of the circuit board.

6. The method according to claim 5, wherein The thickness of the bracket, the depth of the front cover, the vibration spectrum amplification factor, and the SN curve amplification factor are selected as reliability input variables, and the life is selected as the reliability output variable.

7. The method according to claim 6, wherein A kriging response surface proxy model is established for the fatigue failure of the shell base, an implicit model is established for the failure of the front cover, and an explicit model is established for the failure of the circuit board.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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