A Multi-Fault Mode Reliability Simulation Analysis Method for Electromechanical Equipment

Abstract

This invention discloses a multi-fault mode reliability simulation analysis method for electromechanical equipment, comprising: establishing a fault tree for the electromechanical equipment, identifying multiple fault modes as the base events of the fault tree, and establishing a fault physical model of the functional characteristics of the base events and the basic design variables at the bottom level; using reliability input variables as random variables in the fault physical model, and setting and sampling the parameters of the random variables; based on the sampling results of the random variable parameters, establishing explicit models, implicit models, or Kriging response surface surrogate models as fault physical models for multiple fault modes respectively; using the Monte Carlo adaptive importance sampling method to perform reliability simulation analysis, and obtaining the comprehensive reliability of the electromechanical equipment under multiple fault modes; optimizing the parameters of the reliability input variables, and repeating the simulation analysis until the comprehensive reliability meets the requirements. This invention can realize coupled reliability analysis of multiple fault modes of electromechanical equipment, thereby improving the reliability level of electromechanical equipment.

Description

Technical Field

[0001] This invention belongs to the field of reliability simulation analysis technology, specifically relating to a reliability simulation analysis method for multiple failure modes of electromechanical equipment, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] Electromechanical equipment is a complex system integrating multiple physical processes and unit technologies, including mechanical, electrical, and hydraulic systems, onto a mechanical carrier to form a cohesive whole. It operates in harsh environments, is difficult to maintain, requires higher precision, and has a longer lifespan. During its long-term service, the dynamic characteristics of electromechanical equipment continuously change, and faults may propagate and transform, exhibiting multiple failure modes and mechanisms coexisting. Electromechanical equipment often experiences multiple failure modes simultaneously during use, and the product's components and influencing factors are interdependent, leading to the interconnection of these multiple failure modes, making them impossible to consider independently. Therefore, reliability analysis of multi-failure-mode problems requires considering the combined effects of all components and influencing factors. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-fault mode reliability simulation analysis method, computer equipment, computer-readable storage medium, and computer program product for electromechanical equipment, which can realize 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 method for reliability simulation analysis of multiple fault modes of electromechanical equipment, comprising:

[0005] Step S1: Establish a fault tree for the electromechanical equipment. Based on the various stresses and coupling effects of multiple components of the electromechanical equipment, determine multiple fault modes as the bottom events of the fault tree. Establish a fault physical model of the functional characteristics of the bottom events and the basic design variables of the bottom layer. The functional characteristics represent the reliability output variables of the corresponding fault modes of the electromechanical equipment, and the basic design variables of the bottom layer represent the reliability input variables of the corresponding fault modes of the electromechanical equipment.

[0006] Step S2: The reliability input variable is used as the random variable in the fault physical model. The parameters of the random variable are set, and the Latin hypercube sampling method is used to sample the parameters of the random variable.

[0007] Step S3: Based on the sampling results of the random variable parameters, establish explicit models, implicit models, or Kriging response surface surrogate models as physical models for multiple fault modes.

[0008] Step S4: Simultaneously drive the explicit model, implicit model, or Kriging response surface surrogate model, and use the Monte Carlo adaptive importance sampling method to perform reliability simulation analysis to obtain the comprehensive reliability of electromechanical equipment under multiple failure modes.

[0009] Step S5: Determine whether the overall reliability meets the requirements. If it does not meet the requirements, optimize the parameters of the reliability input variables and repeat steps S2 to S4 until the overall reliability meets the requirements.

[0010] Preferably, the functional characteristics include force, deformation, life and performance parameters, and the underlying basic design variables include size, material and load parameters.

[0011] Preferably, in step S2, the parameters of the random variable are set as follows:

[0012] Load the sample data corresponding to the random variable;

[0013] Choose the distribution type and calculate the distribution parameters by fitting the sample data;

[0014] The random variables are evaluated based on the sample data and the fitting method to obtain the final distribution type and distribution parameters;

[0015] Set the upper and lower limits for random variables.

[0016] Preferably, the distribution type includes uniform distribution, binomial distribution, normal distribution, log-normal distribution, exponential distribution, and Poisson distribution.

[0017] Preferably, the electromechanical equipment is an electric pump, the various 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.

[0018] Preferably, the support thickness, front cover depth, vibration spectrum amplification factor, and SN curve amplification factor are selected as reliability input variables, and lifespan is selected as reliability output variable.

[0019] Preferably, a Kriging response surface proxy model is established for fatigue failure of the housing base, an implicit model is established for failure of the front cover, and an explicit model is established for failure of the circuit board.

[0020] Another aspect of the present invention provides a computer device including 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 described above.

[0021] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0022] Another aspect of the present invention provides a computer program product including a computer program that, when executed by a processor, implements the steps of the method described above.

[0023] According to the above-described aspects of the present invention, the multi-fault mode reliability simulation analysis method, computer equipment, computer-readable storage medium, and computer program product for electromechanical equipment can realize coupled reliability analysis of multiple fault modes of electromechanical equipment, thereby improving the rapid design optimization and reliability level of electromechanical equipment. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort:

[0025] Figure 1 This is a flowchart of the reliability simulation analysis method for multiple fault modes of electromechanical equipment according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of a fault tree for multi-fault mode reliability simulation analysis according to an embodiment of the present invention;

[0027] Figure 3 This is a structural diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0029] Embodiments of the present invention provide a method for reliability simulation analysis of multiple fault modes of electromechanical equipment. Figure 1 This is a flowchart of the multi-fault mode reliability simulation analysis method for electromechanical equipment according to an embodiment of the present invention, such as... Figure 1 As shown, the multi-fault mode reliability simulation analysis method for electromechanical equipment in this embodiment of the invention includes steps S1 to S5.

[0030] In step S1, a fault tree for the electromechanical equipment is established. Based on the various stresses acting on the electromechanical equipment and the coupling effects of multiple components, multiple fault modes are determined as the base events of the fault tree. A fault physical model of the functional characteristic quantities of the base events and the basic design variables of the bottom layer is established.

[0031] Fault tree analysis can identify multiple failure modes of electromechanical equipment (products) and directly establish logical relationships between bottom-level and top-level events, linking multiple failure modes and meeting the requirements of coupled analysis of multiple failure modes. In actual reliability analysis, in addition to establishing logical relationships between top-level and bottom-level events, it is also possible to establish functional relationships between the state of the top event (product failure) in the fault tree and the underlying design parameters.

[0032] Building upon traditional fault tree analysis, extending the fault tree downwards allows for the construction of a fault physics model for the underlying events, generating 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 surrogate models, such as Kriging response surface surrogate models. Through limit state function gates, a fault physics model is established for the underlying event functional characteristics (parameters used to characterize the underlying event state, such as force, deformation, life, and performance parameters) and the underlying basic design variables (dimensional parameters, material parameters, and loads).

[0033] Among them, the functional characteristic quantities represent the reliability failure criteria or output variables of the product corresponding to the failure mode, and the underlying basic design variables represent the reliability impact parameters or input variables of the product corresponding to the failure mode.

[0034] In step S2, the reliability input variable is used as a random variable in the fault physical model, the parameters of the random variable are set, and the Latin hypercube sampling method is used to sample the parameters of the random variable.

[0035] Specifically, based on the physical model of the fault and the corresponding reliability input 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 in the physical model of the fault, and the parameters of the random variables are set.

[0036] 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. The distribution types generally include uniform distribution, binomial distribution, normal distribution, log-normal distribution, exponential distribution, Poisson distribution, etc.

[0037] Random variable parameters can be set either automatically by using data fitting tools to fit the distribution type and parameters of the random variable, or they can be defined directly based on known random characteristics. Random variable parameter settings include:

[0038] (1) Loading variable samples

[0039] When choosing to fit parameters using known random data, you need to first load the random data corresponding to the random variables;

[0040] (2) Define the fitting method

[0041] You can choose a specific distribution type and calculate the distribution parameters by fitting the sample data, or you can use software tools to automatically select the optimal distribution type and corresponding distribution parameters.

[0042] (3) Distribution attribute fitting

[0043] Based on sample data and fitting methods, random variables are evaluated to obtain the final distribution type and distribution parameters. The software can automatically calculate its statistical parameters for preliminary evaluation of the distribution parameters.

[0044] (4) Distribution type and parameter configuration

[0045] By selecting the distribution type corresponding to the random variable, for most common distribution types, we can obtain its corresponding distribution function, as well as PDF and CDF curves;

[0046] (5) Define the truncated state

[0047] You can set upper and lower limits for random variables, or you can set only the upper or lower limit.

[0048] Based on the random variable parameter settings, the random variable parameter sampling adopts the Latin hypercube sampling method. Latin hypercube sampling has the characteristic of uniform stratification, and at the same time, it can obtain the tail sample values ​​with fewer samples, thereby reducing the number of simulation runs required. Therefore, the parameter sampling of this invention adopts Latin hypercube sampling.

[0049] In step S3, based on the sampling results of the random variable parameters, explicit models, implicit models, or Kriging response surface surrogate models are established as fault physical models for multiple fault modes.

[0050] 1. Explicit Model-Driven Simulation

[0051] 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, the explicit mapping relationship between input variables and output variables is constructed, and 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.

[0052] An explicit model, as a function, requires defining input and output variables separately, and then establishing the relationship between the inputs and outputs through formulaic expressions. The explicit modeling process is as follows:

[0053] (1) Input variable analysis

[0054] Based on the relationships between nodes defined in the fault tree, all child nodes and their corresponding variables of the explicit model nodes are automatically edited. A node can correspond to multiple variables, and all input variables are obtained.

[0055] (2) Output variable settings

[0056] The system automatically determines whether the explicit model supports multiple output variables based on the type of the parent node. If it supports a single variable, it automatically creates an input variable. If it supports multiple output variables, it creates a separate variable name.

[0057] (3) Define explicit formulas

[0058] 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.

[0059] 2. Implicit Model-Driven Simulation

[0060] For implicit fault physical models, such as finite element simulation models, modeling is mainly achieved by integrating third-party software. When the object being analyzed lacks explicit formulas, it is necessary to utilize other mature commercial analysis software such as finite element analysis software and dynamic analysis software to establish implicit function parameters. The software platform is then linked to the third-party computational software to drive the simulation calculation of the implicit model. The implicit model modeling process is as follows:

[0061] (1) Definition of input and output variables

[0062] Same as explicit modeling functionality;

[0063] (2) Input file mapping

[0064] Establish the relationship between input variables and input files. During the iteration process, the software modifies the third-party model according to the relationship to achieve the purpose of updating the model. Unrelated input variables will be directly ignored. It supports establishing mapping relationships with multiple input files.

[0065] (3) Implicit model driven

[0066] For different third-party programs, different driver commands are selected or created, which are then used to call the target program in the background to execute the modified input file and obtain the output file.

[0067] (4) Output file mapping

[0068] Establish the relationship between output variables and output files. During the iteration process, the software reads the calculation results of each iteration based on this relationship. It is necessary to map all output variables and also support establishing mapping relationships with multiple output files.

[0069] 3. Proxy Model-Driven Simulation

[0070] The surrogate model is a simulation model built upon the implicit model, further developed into a Kriging response surface model. This surrogate model replaces the complex implicit model in reliability simulation calculations, driving the simulation based on random sampling results of the input variables. Compared to other traditional interpolation techniques, the Kriging response surface has two advantages. First, it is based on the dynamic construction of known information, using only some information near the estimated point, rather than all information, to simulate unknown information. Second, it possesses both local and global statistical properties, allowing the Kriging response surface to analyze the trends and dynamics of known information.

[0071] The surrogate model can be trained directly using generated training samples, or it can be trained by directly importing training sample sets obtained from external finite element models or other methods. Machine learning methods can be introduced to generate surrogate models. Support Vector Regression (SVR) is a machine learning method that maps data to a high-dimensional space through a kernel function, constructing the optimal separating hyperplane in that high-dimensional space. Machine learning generally requires a large amount of training data, while the SVR method does not have particularly high requirements for the amount of data, making it suitable for situations where the training sample set is obtained through finite element calculations.

[0072] In step S4, the Monte Carlo adaptive importance sampling method is used to perform reliability simulation analysis under multiple failure modes to obtain the reliability of electromechanical equipment under multiple failure modes.

[0073] The reliability simulation analysis calculation of this invention is based on the probability theory method. It establishes the relationship between the basic variables of the input variables and the output response through the fault tree, and uses statistical methods to collect sample data of the basic variables to obtain the statistical regularity of the basic variables. Then, it adopts the model-driven method to transfer the statistical regularity of the basic variables to the output response, and finally performs reliability calculation for multiple fault modes through the statistical regularity of the output response.

[0074] Based on the input and output parameters of each failure mode, the Monte Carlo adaptive importance sampling method is used to perform reliability simulation analysis under multiple failure modes, thereby obtaining the reliability of electromechanical equipment under multiple failure modes.

[0075] In step S5, it is determined whether the overall reliability meets the requirements. If the requirements are not met, the parameters of the reliability input variables are optimized, and steps S2 to S4 are repeated until the overall reliability meets the requirements.

[0076] The following specific implementation example illustrates the reliability simulation analysis method for multiple fault modes of electromechanical equipment according to an embodiment of the present invention.

[0077] In this embodiment, the electromechanical equipment is an electric pump, the various 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.

[0078] 1) Establish a fault tree for a certain electric pump on the software platform, such as... Figure 2 As shown, considering the combined effects of various stresses such as load, pressure, and vibration, as well as the coupling effects of multiple electronic components, the three key failure modes of a certain electric pump, namely fatigue failure of the housing base, failure of the front cover, and failure of the circuit board, are taken as the bottom events.

[0079] 2) Considering the characteristics of a certain electric pump, reliability simulation analysis was conducted on the initial design scheme and the improved scheme for the weak links of the electric pump (i.e., the thickness of the bracket and the depth of the front cover). Based on the above three failure modes, the dimensions (with the thickness of the bracket and the depth of the front cover as input variables), load (with the vibration spectrum amplification factor as input variable), and material (with the SN curve amplification factor as input variable) were selected, and the product life was used as the output variable. The reliability input variables for the three failure modes were sampled simultaneously. The distribution type and parameters of the reliability input variables are detailed in Table 1. The failure criterion for the three failure modes is that the life result is not less than 2000h.

[0080] 3) For fatigue failure of the housing base, a Kriging response surface proxy model was established; for failure of the front cover, an implicit model was established by calling third-party simulation software; and for failure of the circuit board, a data statistical explicit model was established based on the reliability calculation results under temperature and vibration conditions using PWA software.

[0081] 4) The mathematical model that simultaneously drives three failure modes is used to calculate the comprehensive reliability of an electric pump under multiple failure modes through reliability simulation analysis. The simulation results show that the reliability level under the initial design conditions is low and does not meet the product requirements.

[0082] 5) After optimizing and improving the weak links of the electric pump (i.e., the thickness of the bracket and the depth of the front cover), the reliability simulation analysis results of the initial design and the improved design are compared in Table 2. The calculation results show that, considering the coupling effect of multiple stresses such as pressure, load and vibration, and the interactive influence of multiple failure modes such as fatigue failure of the housing base, failure of the front cover and failure of the circuit board, the overall reliability of the electric pump has been significantly improved.

[0083] Table 1. Distribution types and parameters of reliability input variables

[0084]

[0085] Table 2 Reliability simulation analysis results of the initial design scheme and the improved scheme of the electric pump

[0086]

[0087] In summary, the method of this invention addresses the problems of complex reliability simulation models and numerous influencing parameters in multi-fault mode (MFMM) systems. By treating multiple fault modes as a composite event, a system reliability simulation model is constructed based on a fault tree. This model simultaneously considers the influencing parameters and outcome characteristic parameters of multiple fault modes, fulfilling the modeling requirement of considering multiple model parameters and multiple stress coupling conditions. Furthermore, based on the known data distribution types, each influencing parameter is synchronously randomized to conduct comprehensive reliability simulation of multiple fault modes. This yields the overall reliability of the multiple fault modes and the overall reliability level of the system, achieving the goal of multi-fault mode reliability simulation analysis for electromechanical equipment.

[0088] According to the method of embodiments of the present invention, the coupled reliability simulation analysis of multiple failure modes is linked by a fault tree-based approach, which can simultaneously satisfy the joint driving forces of implicit models, surrogate models, and explicit models, and can simultaneously consider the coupled effects of functional characteristic variables and basic design variables. The method of embodiments of the present invention has the following beneficial effects:

[0089] 1) It can meet the requirements of coupled reliability analysis of multiple fault modes in electromechanical equipment;

[0090] 2) A comprehensive reliability simulation analysis method is provided, which avoids the difficulty of analyzing the effects 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;

[0091] 3) It provides an efficient comprehensive reliability simulation analysis method for electromechanical equipment, which helps to quickly design and optimize the equipment and improve its reliability level.

[0092] Embodiments of the present invention also provide a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores operating parameter data for various components. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements the steps of the method according to embodiments of the present invention.

[0093] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0094] Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of the embodiments of the present invention.

[0095] Embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the method of the embodiments of the present invention.

[0096] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for reliability simulation analysis of multiple fault modes of electromechanical equipment, characterized in that, include: Step S1: Establish a fault tree for the electromechanical equipment. Based on the various stresses and coupling effects of multiple components of the electromechanical equipment, determine multiple fault modes as the bottom events of the fault tree. Establish a fault physical model of the functional characteristics of the bottom events and the basic design variables of the bottom layer. The functional characteristics represent the reliability output variables of the corresponding fault modes of the electromechanical equipment, and the basic design variables of the bottom layer represent the reliability input variables of the corresponding fault modes of the electromechanical equipment. Step S2: The reliability input variable is used as the random variable in the fault physical model. The parameters of the random variable are set, and the Latin hypercube sampling method is used to sample the parameters of the random variable. Step S3: Based on the sampling results of the random variable parameters, establish explicit models, implicit models, or Kriging response surface surrogate models as physical models for multiple fault modes. Step S4: Simultaneously drive the explicit model, implicit model, or Kriging response surface surrogate model, and use the Monte Carlo adaptive importance sampling method to perform reliability simulation analysis to obtain the comprehensive reliability of electromechanical equipment under multiple failure modes. Step S5: Determine if the overall reliability meets the requirements. If not, optimize the parameters of the reliability input variables and repeat steps S2 to S4 until the overall reliability meets the requirements. In step S2, the parameters of the random variables are set as follows: Load the sample data corresponding to the random variable; Choose the distribution type and calculate the distribution parameters by fitting the sample data; The random variables are evaluated based on the sample data and the fitting method to obtain the final distribution type and distribution parameters; Set upper and lower limits for random variables. The electromechanical equipment is an electric pump, the various 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. The thickness of the electric pump bracket, the depth of the front cover, the vibration spectrum amplification factor, and the SN curve amplification factor were selected as reliability input variables, and the lifespan was selected as the reliability output variable. A Kriging response surface proxy model is established for fatigue failure of the housing base, an implicit model is established for failure of the front cover, and an explicit model is established for failure of the circuit board.

2. The method as described in claim 1, characterized in that, Functional characteristics include force, deformation, life, and performance parameters, while underlying basic design variables include dimensions, material, and load parameters.

3. The method as described in claim 1 or 2, characterized in that, The distribution types include uniform distribution, binomial distribution, normal distribution, log-normal distribution, exponential distribution, and Poisson distribution.

4. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-3.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-3.

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