Diaphragm capsule sensor fatigue reliability evaluation method considering multi-source uncertainty

Abstract

The invention relates to the field of reliability design analysis, in particular to a diaphragm capsule sensor fatigue reliability evaluation method considering multi-source uncertainty. The method mainly comprises the following steps: (1) establishing a multi-source uncertainty representation model of the capsule sensor; (2) establishing a three-dimensional assembly model and a finite element model of the capsule sensor; (3) establishing a fault physical model and a reliability model of the capsule sensor; and (4) evaluating the fatigue reliability of the capsule sensor based on Monte Carlo simulation and a Kriging agent model. The method has the beneficial effects that the influence of multi-source uncertainty including pre-tension uncertainty is fully considered, a large amount of simulation time can be effectively saved, the fatigue reliability of the diaphragm capsule sensor is evaluated more efficiently and accurately, and theoretical method support is provided for reliability design, fault prediction, maintenance and replacement decision making and the like of the diaphragm capsule sensor.

Description

Technical Field

[0001] The invention relates to the field of reliability design analysis, and in particular to a fatigue reliability evaluation method for a membrane box sensor taking multi-source uncertainties into consideration. Background Art

[0002] There are many potential risk factors in the long-term automated operation of large-scale industrial processes and complex equipment systems. Instruments, as the basic units of measurement, control and perception, are key equipment for the automated operation and control of large-scale industrial processes and complex equipment systems, and are also the fundamental guarantee for their safe and reliable operation. Once an instrument fails, it will quickly affect the operation of the entire system, and in severe cases, it will lead to accidents, causing environmental damage, economic losses, and even casualties.

[0003] The rapid development of science and technology has made the types and forms of instruments and meters increasingly rich. At present, instrumentation technology has entered a new stage of electronicization, digitization, and intelligence. Intelligent instruments have many high-end functions such as self-diagnosis, error correction, remote communication, human-computer interaction, and data processing. They can complete more accurate and stable measurement and monitoring tasks under more complex environmental conditions. They are currently widely used in aerospace, railway transportation, water conservancy and electricity, petrochemicals, and nuclear power. However, due to the powerful functions contained in intelligent instruments, they inevitably have problems such as high structural complexity and cumbersome production processes, resulting in relatively low reliability. Therefore, how to ensure the long-term stable and reliable operation of intelligent instruments and provide solid guarantees for large-scale industrial processes and complex equipment systems has become a major problem that needs to be solved urgently.

[0004] Smart pressure transmitter is an intelligent pressure parameter measuring instrument, which can be used to measure the differential pressure, pressure and other parameters of various liquids and gases. It is one of the most representative products of smart instruments. Among the many components of the smart pressure transmitter, the diaphragm sensor plays a vital role. It has the function of converting the medium pressure signal into an electrical signal. Its reliability will directly affect the reliability of the whole machine of the smart pressure transmitter. Under the pressure of the measuring medium, the central measuring diaphragm in the diaphragm sensor will initiate microcracks under the continuous impact of the external pressure. Under the action of the alternating load, the microcracks will continue to initiate, gather, and connect, eventually forming macro cracks and causing the central measuring diaphragm to break, and finally causing damage to the whole machine of the smart pressure transmitter. At the same time, the diaphragm sensor of the smart pressure transmitter will be affected by the uncertainty of its material, structure, applied load, etc. These uncertainties will cause the performance of each component of the diaphragm sensor to fluctuate, and ultimately affect the failure behavior of the diaphragm sensor and the whole machine of the smart pressure transmitter. Among these uncertain parameters, the effect of pre-tension on the deflection of the pressure-sensing diaphragm is very obvious. The magnitude of pre-tension is related to the subsequent mechanical properties of the diaphragm, and the uniformity and consistency of its internal tension also directly affect the accuracy and long-term stability of diaphragm measurement. Therefore, it is necessary to focus on the impact of the uncertainty of pre-tension on the diaphragm box sensor. In this case, in order to design a high-precision, long-life intelligent pressure transmitter, it is necessary to develop a set of high-precision fatigue reliability evaluation methods suitable for diaphragm box sensors under the premise of fully considering multi-source uncertainties, which can be used for reliability analysis and evaluation in the product design stage.

[0005] Based on the above background, the present invention provides a fatigue reliability assessment method for a diaphragm sensor considering multi-source uncertainty. The Monte Carlo simulation is combined with the finite element simulation to evaluate the impact of multi-source uncertainty on the fatigue failure of the diaphragm sensor, and the Kriging proxy model is used to reduce the calculation time and computing resources of the finite element simulation to achieve the best balance between calculation accuracy and calculation efficiency. Summary of the invention

[0006] In order to fully consider the influence of diaphragm pre-tension on the fatigue behavior of the central measurement diaphragm and more accurately evaluate the reliability level of the diaphragm sensor under multi-source uncertainty, the present invention provides a method for evaluating the fatigue reliability of the diaphragm sensor considering multi-source uncertainty. This method can effectively save a lot of simulation time and resources, and more efficiently analyze and evaluate the fatigue reliability of the diaphragm sensor, while fully considering the influence of multi-source uncertainty including pre-tension uncertainty. This method can provide theoretical method support in the reliability design, fault prediction, maintenance and replacement decision-making of the diaphragm sensor, and at the same time has potential practical application value in dealing with complex and time-consuming reliability modeling and analysis problems.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: a fatigue reliability evaluation method of a membrane box sensor considering multi-source uncertainty, comprising the following steps:

[0008] Step 1: Sort out the multi-source uncertainties faced by the membrane box sensor during its manufacturing, assembly and service, including material parameter uncertainty, assembly parameter uncertainty and load parameter uncertainty, and establish a probabilistic characterization model of the above parameters through probabilistic statistics methods.

[0009] Specifically, six key variables are selected from material parameters, assembly parameters and load parameters to carry out uncertainty analysis, namely: elastic modulus E1 of center measuring diaphragm material, Poisson's ratio v1 of center measuring diaphragm material, elastic modulus E2 of isolation diaphragm material, Poisson's ratio v2 of isolation diaphragm material, medium pressure P and pre-tension T of center measuring diaphragm.

[0010] For the pre-tension T of the central measuring diaphragm, in order to simulate the application method of the pre-tension of the diaphragm in the actual assembly process, the effect of the pre-tension is simulated by applying radial deformation to the diaphragm. The pre-tension is equivalently converted into radial deformation using formula (1). The uncertainty of the pre-tension is reflected in the uncertainty of the radial expansion of the central measuring diaphragm in the actual assembly process:

[0011]

[0012] Wherein, T is the magnitude of the pre-tension on the diaphragm; E is the elastic modulus of the diaphragm material; D is the diameter of the diaphragm; ΔD is the equivalent radial expansion of the diaphragm.

[0013] Subsequently, the normal distribution is used to characterize the uncertainty of the pretension T, that is, the uncertainty of the radial expansion ΔD of the central measured diaphragm. The probability density function of the normal distribution is shown in formula (2):

[0014]

[0015] Where μ is the mean of the normal distribution and σ is the standard deviation of the normal distribution.

[0016] Finally, the equivalent radial expansion ΔD of the theoretical design value of the pre-tension applied to the central measuring diaphragm during the actual assembly process is CL As the mean of the normal distribution, the fluctuation upper limit ΔD of the radial expansion in the actual assembly process is determined. UCL and fluctuation lower limit ΔD LCL , and the standard deviation of the normal distribution is calculated based on the Six Sigma quality management method A normal distribution probability statistical model is established to characterize the uncertainty of pre-tension, namely

[0017] For the remaining five random variables, their respective nominal values ​​A CL As the mean of the normal distribution, the standard deviation of the normal distribution of the corresponding random variable is determined by using a coefficient of variation of 0.05, 0.05×A CL , a normal distribution probability statistical model is established to characterize the uncertainty of the material and load parameters, namely A~N(A CL ,(0.05A CL ) 2 ).

[0018] Step 2: Establish a three-dimensional assembly model of the diaphragm box sensor, carry out meshing, impose degree of freedom constraints and load boundary conditions, and establish a finite element simulation model of the diaphragm box sensor.

[0019] Specifically, a three-dimensional assembly model of the isolation diaphragm, metal shell, central measuring diaphragm (solid structure) and silicone oil transmission medium (liquid structure) is established at the same time, and mesh division is carried out. By applying load boundary conditions, the effect of external pressure is transferred to the central measuring diaphragm through silicone oil, thereby simulating the fatigue behavior of the central measuring diaphragm of the membrane box sensor under the action of external pressure.

[0020] Step 3: Define the fault physical model used to characterize the fatigue behavior of the diaphragm in the center of the diaphragm sensor, and thereby establish a reliability model of the diaphragm sensor.

[0021] Specifically, as shown in formula (3), the Basquin-Coffin-Manson equation is a fault physics model widely used in fatigue life prediction, which can describe the fatigue life of materials under different strain amplitudes. This equation considers both low-cycle fatigue and high-cycle fatigue. Therefore, this failure mechanism model is selected to characterize the fatigue behavior of the central measuring diaphragm of the diaphragm sensor.

[0022]

[0023] in, is the total strain amplitude; is the elastic strain amplitude; is the plastic strain amplitude; σ′ f is the fatigue strengthening coefficient; E is the elastic modulus of the material; b is the Basquin fatigue strength index; ε′ f is the fatigue ductility coefficient; c is the Coffin-Manson fatigue ductility index; N f is the fatigue life.

[0024] Furthermore, the reliability model of the membrane box sensor can be represented in the form of a time-varying reliability function, as shown in formula (4):

[0025]

[0026] Where R(t) is the reliability function of the membrane box sensor, which represents the reliability at time t; N f is a random variable, which represents the fatigue life of the membrane box sensor under the influence of various uncertainty parameters; f(t) is the probability density function of the random variable.

[0027] Step 4: Monte Carlo simulation method and Kriging proxy model are used to evaluate the fatigue life distribution and fatigue reliability of the membrane box sensor, which specifically includes the following steps:

[0028] Step 4.1: Based on the probability representation model of each uncertainty parameter established in step 1, Monte Carlo random sampling is performed to generate an initial sample pool containing N1 groups of samples.

[0029] Specifically, each group of samples in the initial sample pool consists of six parameters: the elastic modulus E1 of the central measuring diaphragm material, the Poisson's ratio v1 of the central measuring diaphragm material, the elastic modulus E2 of the isolation diaphragm material, the Poisson's ratio v2 of the isolation diaphragm material, the medium pressure P, and the equivalent radial expansion ΔD of the pre-tension of the central measuring diaphragm. The initial sample pool can be expressed as:

[0030] Ω0={(E1 i ,v1 i ,E2 i ,v2 i ,P i ,ΔD i )|i∈N * ,i≤N1} (5)

[0031] Among them, Ω0 is the initial sample pool; (E1 i ,v1 i ,E2 i ,v2 i ,P i ,ΔD i ) is the i-th group of samples; N1 is the number of samples not less than 50.

[0032] Step 4.2: Substitute the N1 groups of random samples of each uncertain parameter generated in step 4.1 into the finite element model of the diaphragm sensor established in step 2, carry out finite element simulation analysis of the diaphragm sensor, and extract the maximum strain value of the central measurement diaphragm.

[0033] Specifically, the maximum strain value of the central measurement diaphragm in the finite element simulation results of the N1 group of samples in the initial sample pool can be expressed as:

[0034] Ω1={Δε t i |i∈N * ,i≤N1} (6)

[0035] Among them, Ω1 is the set of maximum strain values ​​of the initial sample pool; Δε t i is the maximum strain value of the i-th group of samples.

[0036] Step 4.3: Based on the fatigue model of the central measuring diaphragm established in step 3, the fatigue life of the N1 group of diaphragm sensor samples is calculated using the maximum strain value of the central measuring diaphragm obtained in step 4.2;

[0037] Specifically, the fatigue life of the N1 group of samples in the initial sample pool can be calculated using formula (3), expressed as:

[0038] Ω2={N f i |i∈N * ,i≤N1} (7)

[0039] Where Ω2 is the fatigue life of the initial sample pool; N f i is the fatigue life of the i-th group of samples.

[0040] Step 4.4: Use the uncertainty parameters of the N1 group samples obtained in step 4.3 and the corresponding fatigue life to construct the Kriging proxy model.

[0041] Specifically, a Kriging proxy model is constructed with Ω0 as the sample feature and Ω2 as the sample response.

[0042] Step 4.5: Based on the probability characterization model of each uncertainty parameter established in step 1, Monte Carlo random sampling is performed to generate a candidate sample pool containing group N2 samples, and the Kriging model constructed in step 4.4 is used to predict the fatigue life of group N2 samples in the candidate sample pool.

[0043] Specifically, each group of samples in the candidate sample pool is composed of six parameters, namely, the elastic modulus E1 of the central measurement diaphragm material, the Poisson's ratio v1 of the central measurement diaphragm material, the elastic modulus E2 of the isolation diaphragm material, the Poisson's ratio v2 of the isolation diaphragm material, the medium pressure P, and the equivalent radial expansion ΔD of the pre-tension of the central measurement diaphragm. The candidate sample pool can be expressed as:

[0044] Ω3={(E1 i ,v1 i ,E2 i ,v2 i ,P i ,ΔD i )|i∈N * ,i≤N2} (8)

[0045] Among them, Ω3 is the candidate sample pool; (E1i ,v1 i ,E2 i ,v2 i ,P i ,ΔD i ) is the i-th group of samples; N2 is the number of samples not less than 5000.

[0046] Furthermore, the fatigue life of the N2 group of samples in the candidate sample pool predicted by the Kriging model can be expressed as:

[0047] Ω4={N f i |i∈N * ,i≤N2} (9)

[0048] Where Ω4 is the set of predicted values ​​of fatigue life of candidate sample pool; N f i is the predicted value of fatigue life of the i-th group of samples.

[0049] Step 4.6: Use the fatigue life of the N2 group of samples in the candidate sample pool obtained in step 4.5 to draw a frequency-fatigue life histogram, and use the reliability model established in step 3 to evaluate the fatigue life distribution and fatigue reliability of the membrane box sensor.

[0050] Specifically, as shown in formula (10), the kernel density function is used to evaluate the probability density function of the fatigue life of the membrane box sensor:

[0051]

[0052] in, is the probability density function estimate of the fatigue life of the membrane box sensor; N2 is the number of samples in the candidate sample pool; N fi is the fatigue life of the i-th group of samples in the candidate sample pool; h is the bandwidth; K(·) is the kernel function, and the normal kernel function is generally used, and its expression is as follows:

[0053]

[0054] Subsequently, the fatigue reliability of the membrane box sensor is evaluated using the reliability model established in step 3. The evaluation results are as follows:

[0055]

[0056] The present invention has the following beneficial effects:

[0057] 1. The fatigue reliability evaluation method of the diaphragm sensor considering multi-source uncertainty provided by the present invention fully considers the influence of the diaphragm pre-tension on the fatigue behavior of the central measurement diaphragm, and can more accurately evaluate the reliability level of the diaphragm sensor under multi-source uncertainty, and can provide theoretical method support in the reliability design, fault prediction, maintenance and replacement decision-making of the diaphragm sensor;

[0058] 2. The present invention provides a method for evaluating the fatigue reliability of a diaphragm box sensor taking into account multi-source uncertainties. Under the premise of fully considering the influence of multi-source uncertainties including pre-tension uncertainty, it can effectively save a lot of simulation time and resources, and more efficiently analyze and evaluate the fatigue reliability of the diaphragm box sensor. It has potential practical application value in dealing with highly complex and time-consuming reliability modeling and analysis problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of a fatigue reliability evaluation method of a membrane box sensor considering multi-source uncertainty proposed by the present invention;

[0060] Figure 2 It is a three-dimensional geometric assembly model of the diaphragm sensor of the intelligent pressure transmitter in the implementation case of the present invention;

[0061] Figure 3 It is the grid division result of the diaphragm sensor of the intelligent pressure transmitter in the implementation case of the present invention;

[0062] Figure 4 It is a schematic diagram of boundary conditions in a finite element model of a diaphragm sensor of an intelligent pressure transmitter in an implementation case of the present invention;

[0063] Figure 5 is a schematic diagram of the pre-tensioning force applied to the central measuring diaphragm in an embodiment of the present invention;

[0064] Figure 6 is the fatigue life of 50 groups of samples in the initial sample pool in the implementation case of the present invention;

[0065] Figure 7 It is the fatigue life probability density function and reliability function of the diaphragm sensor of the intelligent pressure transmitter in the implementation case of the present invention. DETAILED DESCRIPTION

[0066] The present invention is further described below with reference to the accompanying drawings and specific implementation examples.

[0067] This implementation case takes the diaphragm sensor of a certain type of intelligent pressure transmitter as the analysis object. Figure 1The method flow shown in the figure applies a fatigue reliability assessment method of a membrane box sensor considering multi-source uncertainty proposed by the present invention. This implementation case specifically includes the following contents:

[0068] 1. Modeling of multi-source uncertainty probabilistic representation of membrane box sensor

[0069] The multi-source uncertainties faced by the diaphragm sensor during its manufacturing, assembly and service were sorted out, including material parameter uncertainty, assembly parameter uncertainty and load parameter uncertainty. Specifically, six key variables were selected from the material parameters, assembly parameters and load parameters for uncertainty analysis, namely: the elastic modulus E1 of the central measuring diaphragm material, the Poisson's ratio v1 of the central measuring diaphragm material, the elastic modulus E2 of the isolation diaphragm material, the Poisson's ratio v2 of the isolation diaphragm material, the medium pressure P, and the pre-tension T of the central measuring diaphragm.

[0070] For the pre-tension T of the central measuring diaphragm, in order to simulate the application method of the pre-tension of the diaphragm in the actual assembly process, the effect of the pre-tension is simulated by applying radial deformation to the diaphragm. The pre-tension is equivalently converted into radial deformation using formula (13). The uncertainty of the pre-tension is reflected in the uncertainty of the radial expansion of the central measuring diaphragm in the actual assembly process:

[0071]

[0072] Wherein, T is the magnitude of the pre-tension on the diaphragm; E is the elastic modulus of the diaphragm material; D is the diameter of the diaphragm; ΔD is the equivalent radial expansion of the diaphragm.

[0073] Subsequently, the normal distribution is used to characterize the uncertainty of the pretension T, that is, the uncertainty of the radial expansion ΔD of the central measured diaphragm. The probability density function of the normal distribution is shown in formula (14):

[0074]

[0075] Where μ is the mean of the normal distribution and σ is the standard deviation of the normal distribution.

[0076] Finally, the theoretical design value T of the pre-tension applied to the central measuring diaphragm during the actual assembly process is CL =Equivalent radial expansion ΔD of 44MPa CL =0.0066mm as the mean of the normal distribution, and determine the upper limit of the fluctuation of the radial expansion in the actual assembly process ΔD based on engineering experience UCL =0.0076mm and fluctuation lower limit ΔD LCL =0.0056mm, and the standard deviation of the normal distribution is calculated based on the Six Sigma quality management method A normal distribution probability statistical model is established to characterize the uncertainty of pretension, namely, ΔD~N(0.0066,(3.3×10 -4 ) 2 ).

[0077] For the remaining five random variables, their respective nominal values ​​A CL As the mean of the normal distribution, the standard deviation of the normal distribution of the corresponding random variable is determined by using a coefficient of variation of 0.05, 0.05×A CL , a normal distribution probability statistical model is established to characterize the uncertainty of the material and load parameters, namely A~N(A CL ,(0.05A CL ) 2 ). The probability representation models of the above six random variables are shown in Table 1.

[0078] Table 1 Probabilistic characterization model of multi-source uncertainty of diaphragm sensor of intelligent pressure transmitter

[0079] 2. Establishment of 3D assembly model and finite element model of membrane box sensor

[0080] (1) Establishment of three-dimensional geometric assembly model: The diaphragm sensor consists of two identical corrugated isolation diaphragms on both sides, a metal shell and a central measuring diaphragm, and is filled with silicone oil as the transmission medium. The three-dimensional geometric assembly model of the diaphragm sensor is established based on SolidWorks software, such as Figure 2 The specific dimensions of the isolation diaphragm and the measuring diaphragm are shown in Table 2.

[0081] Table 2 Specific dimensions of the isolation diaphragm and the measuring diaphragm

[0082] (2) Meshing: ANSYS Workbench 19.1 is used to mesh the diaphragm sensor. Since the isolation diaphragm and the measuring diaphragm are the key components of the diaphragm sensor's pressure transmission, a finer mesh is used to mesh the isolation diaphragm and the measuring diaphragm to improve the calculation accuracy. The meshing results of the diaphragm sensor components are shown in Figure 2. Figure 3 shown.

[0083] (3) Freedom constraints and load boundary conditions: Figure 4As shown, first, in order to simulate the installation of the diaphragm sensor inside the intelligent pressure transmitter, a fixed constraint is applied to the circumferential surface of the metal shell; then a radial displacement is applied to the central measuring diaphragm to simulate the pre-tension of the diaphragm sensor during the actual assembly process; next, a pressure of 0.2MPa is applied to one end of the isolation diaphragm to simulate the effect of the medium pressure load; finally, binding contact is applied between the components of the diaphragm sensor to simulate the actual assembly relationship.

[0084] 3. Establishment of the physical model and reliability model of the membrane box sensor failure

[0085] The fault physics model used to characterize the fatigue behavior of the center measurement diaphragm of the diaphragm sensor is clearly defined. Specifically, as shown in formula (15), the Basquin-Coffin-Manson equation is a fault physics model widely used in fatigue life prediction, which can describe the fatigue life of materials under different strain amplitudes. This equation considers both low-cycle fatigue and high-cycle fatigue. Therefore, this fault mechanism model is selected to characterize the fatigue behavior of the center measurement diaphragm of the diaphragm sensor.

[0086]

[0087] in, is the total strain amplitude; is the elastic strain amplitude; is the plastic strain amplitude; σ′ f is the fatigue strengthening coefficient; E is the elastic modulus of the material; b is the Basquin fatigue strength index; ε′ f is the fatigue ductility coefficient; c is the Coffin-Manson fatigue ductility index; N f is the fatigue life.

[0088] Furthermore, a reliability model of the membrane box sensor is established, which can be represented in the form of a time-varying reliability function, as shown in formula (16):

[0089]

[0090] Where R(t) is the reliability function of the membrane box sensor, which represents the reliability at time t; N f is a random variable, which represents the fatigue life of the membrane box sensor under the influence of various uncertainty parameters; f(t) is the probability density function of the random variable.

[0091] 4. Fatigue reliability evaluation of membrane sensor based on Monte Carlo simulation and Kriging proxy model

[0092] (1) Generation of initial sample pool: Based on the established probability representation model of each uncertainty parameter, Monte Carlo random sampling is performed to generate an initial sample pool containing 50 groups of samples. The initial sample pool can be expressed as:

[0093] Ω0={(E1 i ,v1 i ,E2 i ,v2 i ,P i ,ΔD i )|i∈N * ,i≤50} (17)

[0094] Among them, Ω0 is the initial sample pool; (E1 i ,v1 i ,E2 i ,v2 i ,P i ,ΔD i ) is the i-th group of samples.

[0095] (2) Finite element simulation: Based on the established finite element model, finite element simulations were carried out for 50 groups of samples in Ω0, and the maximum strain value of the central measurement diaphragm in the simulation results was extracted, which can be expressed as:

[0096] Ω1={Δε t i |i∈N * ,i≤50} (18)

[0097] Among them, Ω1 is the set of maximum strain values ​​of the initial sample pool; Δε t i is the maximum strain value of the i-th group of samples.

[0098] (3) Fatigue life calculation: Based on the fault physics model of formula (15), assuming that the pressure on the diaphragm sensor acts once per minute, the fatigue life of 50 groups of samples in Ω0 is calculated respectively, as follows: Figure 6 As shown, it can be expressed as:

[0099] Ω2={N f i |i∈N * ,i≤50} (19)

[0100] Where Ω2 is the fatigue life of the initial sample pool; N f i is the fatigue life of the i-th group of samples.

[0101] (4) Kriging model construction: The Kriging proxy model is constructed using Ω0 as the sample feature and Ω2 as the sample response.

[0102] (5) Generation of candidate sample pool and fatigue life prediction: Based on the established probability characterization model of each uncertainty parameter, Monte Carlo random sampling is performed to generate a candidate sample pool containing 10,000 groups of samples. The fatigue life of the 10,000 groups of samples in the candidate sample pool is predicted using the constructed Kriging model.

[0103] (6) Fatigue life distribution and fatigue reliability evaluation: Using the fatigue life of 10,000 groups of samples in the candidate sample pool, a frequency-fatigue life histogram is drawn, and the kernel density function is used to evaluate the probability density function of the fatigue life of the membrane box sensor:

[0104]

[0105] in, is the probability density function estimate of the fatigue life of the membrane sensor; N2 is the number of samples in the candidate sample pool, N2 = 10000; N fi is the fatigue life of the i-th group of samples in the candidate sample pool; h is the bandwidth, h = 125.77; K(·) is the kernel function, and the normal kernel function is generally used, and its expression is as follows:

[0106]

[0107] Then, if Figure 7 As shown, the fatigue reliability of the membrane box sensor is evaluated using the reliability model of formula (16), and the evaluation results are as follows:

[0108]

[0109] It can be seen from the calculation results that the fatigue reliability evaluation method of the membrane box sensor considering multi-source uncertainty proposed in the present invention can quickly evaluate the reliability level of the membrane box sensor.

[0110] Parts of the present invention that are not disclosed in detail belong to the common knowledge in the art.

[0111] Although the above describes the illustrative specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations using the concept of the present invention are protected.

Claims

1. A fatigue reliability evaluation method for a capsule sensor considering multi-source uncertainty, characterized in that: The following steps are involved: Step 1: Establish a probabilistic characterization model of the uncertainty of the membrane box sensor material parameters, load parameters and assembly parameters through probabilistic statistical methods, which specifically includes the following steps: Step 1.1: Sort out the uncertainty of material parameters and load parameters in the manufacturing and service process of the membrane box sensor, and establish the probability characterization model of the above parameters through probability statistics method; Step 1.2: sort out the uncertainty of assembly parameters in the membrane box sensor assembly process, and establish a probability representation model of the above parameters through probability statistics method; Step 2: Establish a three-dimensional assembly model of the diaphragm sensor, carry out meshing, impose degree of freedom constraints and load boundary conditions, and establish a finite element simulation model of the diaphragm sensor; Step 3: Establish a central measurement diaphragm fatigue model of the form of formula (1) and a diaphragm box sensor reliability model of the form of formula (2); in: is the total strain amplitude; is the elastic strain amplitude; is the plastic strain amplitude; σ′ f is the fatigue strengthening factor; E is the elastic modulus of the material; b is the Basquin fatigue strength index; ε′ f is the fatigue ductility coefficient; c is the Coffin-Manson fatigue extension index; N f is the fatigue life; in: R(t) is the reliability function of the membrane box sensor, which represents the reliability at time t; N f is a random variable, which represents the fatigue life of the central measurement diaphragm under the influence of various uncertainty parameters; f(t) is the probability density function of the random variable; Step 4: Monte Carlo simulation method and Kriging proxy model are used to evaluate the fatigue life distribution and fatigue reliability of the membrane box sensor, which specifically includes the following steps: Step 4.1: Based on the probability representation model of each uncertainty parameter established in step 1, Monte Carlo random sampling is performed to generate an initial sample pool containing N1 groups of samples; Step 4.2: Substitute the N1 groups of random samples of each uncertain parameter generated in step 4.1 into the finite element model of the diaphragm sensor established in step 2, carry out finite element simulation analysis of the diaphragm sensor, and extract the maximum strain value of the central measurement diaphragm; Step 4.3: Based on the fatigue model of the central measuring diaphragm established in step 3, the fatigue life of the N1 group of diaphragm sensor samples is calculated using the maximum strain value of the central measuring diaphragm obtained in step 4.2; Step 4.4: Use the uncertainty parameters of the N1 group samples obtained in step 4.3 and the corresponding fatigue life to construct the Kriging proxy model; Step 4.5: Based on the probability representation model of each uncertainty parameter established in step 1, Monte Carlo random sampling is performed to generate a candidate sample pool containing group N2 samples, and the Kriging model constructed in step 4.4 is used to predict the fatigue life of group N2 samples in the candidate sample pool; Step 4.6: Use the fatigue life of the N2 group of samples in the candidate sample pool obtained in step 4.5 to draw a frequency-fatigue life histogram, and use the reliability model established in step 3 to evaluate the fatigue life distribution and fatigue reliability of the membrane box sensor.

2. A method for evaluating fatigue reliability of a capsule sensor considering multi-source uncertainty according to claim 1, characterized in that: The material parameter uncertainty and load parameter uncertainty existing in the manufacturing and service process of the combing membrane box sensor described in step 1.1 are used to establish a probability characterization model of the above parameters through a probability statistical method, which specifically includes the following steps: Step 1.1.1: Take the nominal values ​​A of the materials and load parameters of the capsule sensor in actual manufacturing and service process. CL as the mean of a normal distribution; Step 1.1.2: Take the coefficient of variation as 0.05 and calculate the standard deviation of the normal distribution as 0.05×A CL ; Step 1.1.3: Establish a normal distribution probability statistical model to characterize the uncertainty of the material and load parameters, namely: A~N(A CL ,(0.05A CL ) 2 ).

3. The fatigue reliability evaluation method of a capsule sensor considering multi-source uncertainty according to claim 1 is characterized in that: The uncertainty of assembly parameters in the process of assembling the combing membrane box sensor described in step 1.2 is eliminated, and a probability characterization model of the above parameters is established by a probability statistics method, which specifically includes the following steps: Step 1.2.1: The effect of pre-tension is simulated by applying radial deformation to the diaphragm, and the pre-tension is equivalently converted into radial deformation using equation (3). The uncertainty of pre-tension is reflected in the uncertainty of the radial expansion of the diaphragm measured at the center during the actual assembly process: in: T is the magnitude of the pre-tension on the diaphragm; E is the elastic modulus of the measured diaphragm material; D is the diameter of the measuring diaphragm; ΔD is the equivalent radial expansion of the measured diaphragm; Step 1.2.2: Use normal distribution to characterize the uncertainty of pre-tension, i.e. the uncertainty of radial expansion of the central measurement diaphragm; Step 1.2.3: The equivalent radial expansion ΔD of the theoretical design value of the pre-tension applied to the center measuring diaphragm during the actual assembly process CL as the mean of a normal distribution; Step 1.2.4: Determine the upper limit of radial expansion fluctuation ΔD during the actual assembly process UCL and fluctuation lower limit ΔD LCL , and the standard deviation of the normal distribution is calculated based on the Six Sigma quality management method Step 1.2.5: Establish a normal distribution probability statistical model to characterize the uncertainty of pre-tension, namely:

4. The fatigue reliability evaluation method of a capsule sensor considering multi-source uncertainty according to claim 1 is characterized in that: The fatigue life distribution and fatigue reliability of the membrane box sensor described in step 4.6 are evaluated as follows: As shown in formula (4), the kernel density function is used to evaluate the probability density function of the fatigue life of the membrane box sensor: in: is the estimated value of the probability density function of the fatigue life of the membrane box sensor; N2 is the number of samples in the candidate sample pool; N fi is the fatigue life of the i-th group of samples in the candidate sample pool; h is the bandwidth; K(·) is the kernel function; Subsequently, the fatigue reliability of the membrane box sensor is evaluated using the reliability model established in step 3. The evaluation results are as follows:

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