CN119150534A - Mechanism and data fusion type diesel engine overall reliability prediction method - Google Patents
Mechanism and data fusion type diesel engine overall reliability prediction method Download PDFInfo
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Abstract
The method for predicting the reliability of the whole diesel engine by combining mechanism and data in the technical field of internal combustion engines comprises the following steps of basic modeling parameter input, engine performance model construction, dynamic model construction, abrasion and fatigue calculation of multiple samples of key components, reliability analysis of a mechanism model and reliability analysis of the whole diesel engine after combining a statistical model. The invention fuses the mechanism model and the statistical data to predict the reliability of the whole diesel engine, which improves the confidence of the reliability calculation of the diesel engine obviously.
Description
Technical Field
The invention relates to the field of reliability of diesel engines, in particular to a mechanism capable of improving reliability calculation confidence of a diesel engine and a method for predicting the reliability of the whole diesel engine by data fusion.
Background
The traditional diesel engine reliability prediction method is a method based on mathematical statistics or a method based on fault physics. Based on the method of mathematical statistics, a large amount of test and operation data are required, and the realization of the high-power marine engine with a small sample number is difficult. Based on the fault physics method, a mathematical model is adopted to establish a causal relationship to reflect objective physical laws, but a plurality of fault influencing factors are difficult to establish an accurate mechanism model, and the application of the method is limited. Only by combining reliability statistical theory with failure physical model research can complexity and regularity of the uncertain world be better known.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention discloses a method for predicting the reliability of the whole diesel engine by fusing mechanism models and statistical data, which is used for predicting the reliability of the whole diesel engine by fusing the mechanism models and the statistical data, so that the confidence of the reliability calculation of the diesel engine is remarkably improved, and the method not only can obtain the reliability of parts and the whole diesel engine of the mechanism models of the diesel engine, but also can obtain the reliability of the whole diesel engine fused with the statistical data.
The invention is realized by the following technical scheme, which comprises the following steps:
step one, inputting basic modeling parameters, including material attribute parameters, engine structure and performance parameters;
Constructing an engine performance model, and solving a differential equation set consisting of a simultaneous energy conservation equation, a mass conservation equation and an ideal gas state equation by adopting a prediction correction method in order to solve the change of state parameters in each subsystem along with the crank angle;
Step three, constructing a dynamic model, namely taking parameters such as cylinder pressure, temperature and the like obtained by an engine performance model as boundary conditions, and calculating the load of each part based on the kinematics and dynamics of a crank-connecting rod mechanism;
Step four, calculating abrasion and fatigue of multiple samples of the key component, wherein the multiple samples are generated based on a Monte Carlo algorithm, the abrasion loss of a friction pair is calculated based on an Arcard model, and the fatigue of the component is calculated based on a fatigue accumulated damage theory according to an S-N curve of the material;
Step five, analyzing the reliability of the mechanism model, and carrying out statistical analysis on the abrasion and fatigue of the obtained multiple samples of the components to obtain the reliability of each component;
and step six, analyzing the reliability of the whole machine after the statistical model is fused, calculating the reliability of the failure rate data of the counted parts, and fusing the reliability obtained by the calculation of the mechanism model to obtain the reliability of the whole machine after the fusion.
Further, in the first step of the invention, the material property parameters comprise poisson ratio of key component materials, elastic modulus of the materials, hardness of the materials, S-N curve of crank shaft materials, S-N curve of connecting rod materials, tensile strength of the crank shaft materials, tensile strength of the connecting rod materials, engine structure and performance parameters comprise heat release rate, valve timing, stroke, cylinder diameter, compression ratio, cylinder number, volume number, valve number, firing sequence, rotating speed, power, volume size, connecting rod ratio, piston mass, connecting rod mass, crank imbalance mass, connecting rod length, distance from connecting rod centroid to connecting rod big end center, crank rotation angle speed, crank radius, piston diameter, distance from a main journal middle section to a section in a nearest side crank arm, crank arm width, crank arm thickness, crank pin section diameter, main journal section diameter, rod body section area, key friction pair contact length, key friction pair component wear coefficient, key friction pair assembly clearance and key friction pair replacement limit clearance.
Further, in the second step of the present invention, the energy conservation equation is:
Wherein: The unit of the crank angle is deg, the unit of m z is the mass of working medium, the unit of u z is the specific internal energy of working medium in a cylinder, the unit of J/kg, the unit of m in is the mass of air intake, the unit of h i is the specific enthalpy of air intake, the unit of J/kg, the unit of Q f is the heat brought by fuel injected into a cylinder, the unit of J, the unit of Q w is the heat transfer and dissipation energy, the unit of J, the unit of m o is the mass of exhaust, the unit of h o is the specific enthalpy of exhaust, the unit of J/kg, the unit of p z is the pressure in a subsystem, the unit of Pa, the unit of V z is the internal volume of the subsystem, and the unit of m 3;
The mass conservation equation is:
Wherein: The unit of the crank angle is deg, the unit of m z is kg of working medium, the unit of m in is kg of air intake, the unit of m o is kg of exhaust, the unit of g f is kg of fuel oil, and the unit of x is the combustion percentage of fuel oil in an air cylinder;
the ideal gas state equation is expressed as:
pzVz＝mzRzTz
Wherein p z is the pressure in the subsystem, the unit is Pa, V z is the internal volume of the subsystem, the unit is m 3;mz and represents the mass of working medium, the unit is kg, T z is the temperature in the cylinder, the unit is K, R z is the gas constant of the working medium, and the unit is J/(kg.K).
Still further, in the third step of the present invention, the load of each of the components may be expressed as:
Wherein F ∑ is the acting force on the small end of the connecting rod along the center line of the cylinder, which can be decomposed into a piston lateral force F NG and a connecting rod thrust F C, the units are N, the connecting rod thrust F C can be further decomposed into a normal force F R along the crank direction and a tangential force F T perpendicular to the crank direction, the units are N, F P is the acting force at the crank pin, the units are N, F K is the acting force at the main journal, the units are N, m p,mc and m K are respectively expressed as piston mass, connecting rod mass and crank unbalanced mass, the units are kg, the connecting rod mass is always equivalent to two parts, respectively expressed as a reciprocating part m CA and a rotary part m CB, the units are kg, F g is expressed as the gas pressure of the piston which is periodically changed in the cylinder, the units are N, L cb is the distance from the center of the connecting rod centroid to the center of the large end of the connecting rod, the unit is m, the unit is the m is the angle of the center of the large end of the connecting rod, the ω is the angular velocity, the unit is the rad, the unit is the R is the crank radius, the unit is the swing angle, The unit is crank angle, the unit is deg, lambda is the ratio of crank radius to connecting rod length, D P is piston diameter, the unit is m, m r represents total mass which is concentrated at the crank pin and rotates along with the crank, the unit is kg, and the total mass can be expressed as:
mr＝mK+mCB
Wherein m K and m CB are respectively the unbalanced mass of the crank and the mass of the rotary motion part of the connecting rod, and the units are kg.
Furthermore, in the fourth step of the present invention, the sample is generated by adopting the monte carlo algorithm, which is to determine the distribution function of the uncertainty parameters by considering the influence of uncertainty factors such as material performance, manufacturing assembly, external load, etc. suffered by the damage of the diesel engine, generate a series of random numbers, transform the random numbers to obtain the sample of the corresponding probability distribution function, substitute the sample into the corresponding uncertainty parameters, and generate a large number of random samples of the corresponding distribution function for each uncertainty parameter.
Still further, in the fourth step of the present invention, the wear depth of the key friction pair component in the Archard wear model may be expressed as:
Wherein H is the abrasion depth, the unit is m, l is the sliding distance of a friction pair, the unit is m, K is the dimensionless abrasion coefficient, H is the Brinell hardness of the material, the unit is HBW, p is the contact stress, and the unit is Pa, and the abrasion coefficient can be expressed as:
Wherein F is normal contact pressure, the unit is N, S is contact area, and the unit is m 2.
Wear damage is expressed as:
wherein D w represents abrasion damage, 1;t represents time, h and h c are respectively abrasion depth and critical abrasion depth, and the units are mm.
When the wear damage reaches 1, the component fails.
The method is particularly used for feeding back medium leakage in the cylinder caused by the reduction of the tightness of the cylinder due to the abrasion of a friction pair of a cylinder sleeve and a piston ring to the calculation and analysis of a performance model.
The number of cycles of the fatigue damage under the symmetrical cyclic stress amplitude is expressed as:
lgN=a+blgσ-1
Wherein N is the corresponding failure cycle times of the stress amplitude, the unit is 1, sigma -1 represents the stress amplitude of the material under the symmetrical loading with the stress ratio of-1, the unit is MPa, a and b are material constants, and the stress amplitude is obtained according to the S-N curve of the material. Since the forces to which the component is subjected are asymmetric loading forces, when calculating fatigue life using an S-N curve, the effect of the average stress is usually corrected by using the Goodman formula, which is expressed as:
Wherein, sigma b is the tensile limit of the material, sigma m and sigma a are the stress mean value and the stress amplitude under the actual working condition, sigma -1 is the stress amplitude when the stress ratio is-1, and the units are all MPa.
Fatigue damage is damage at all stress levels added up, expressed as:
Wherein D j represents fatigue damage, 1;t represents time, h, n i represents that the component has completed n i cycles under the stress amplitude sigma i, 1;N i represents the total number of cycles the component can bear under the stress amplitude, 1;m represents the number of different stress amplitudes, 1, and the component fails when the fatigue damage reaches 1.
Further, in the fifth step of the present invention, the reliability of each component is obtained by the statistical analysis, and the calculation principle is expressed as follows:
Wherein t represents time in h, n represents the total number of samples, D i represents the damage of the ith sample, Expressed as the reliability of the jth component at time t, the units 1;I (x) are all indicative functions, expressed as:
The reliability of the mechanism model of the whole machine can be expressed as follows:
Wherein t represents time in h, P { d p (t) <1} is the reliability of the mechanism model of the whole machine at the time t, j represents the j-th component, Expressed as the reliability of the jth component at time t, in 1.
Still further, in the step six of the present invention, the reliability of the component failure rate data is calculated, and for the components with reliability test and general purpose for which the failure rate is determined by the data statistics, such as some seals and general purpose components, the failure data of these components can be considered to be obtained under the condition that the critical components are operating normally, and assuming that the failures of these components are independent of each other, the reliability of these components is expressed as:
Where t represents time in h, d p (t) and d d (t) are some norms of the injury vectors DP (t) and DD (t), respectively, and can be expressed as:
dp(t)=||DP(t)||
dd(t)=||DD(t)||
wherein DP (t) represents damage of one part calculated by the mechanism model and is a random vector changing with time, DD (t) represents damage of the other part obtained by data statistics and is a random vector changing with time, and P { DD k (t) <1} is reliability of the kth part of the data statistics, wherein the unit is 1, and the reliability can be expressed as:
wherein k is the kth component counted by data, the unit is 1;t, eta is eta, zeta is h, lambda k (eta) is the instantaneous failure rate of the kth component, eta, and the unit is h -1.
The reliability of the integrated whole machine is calculated according to the following principle:
R(t)=P{dp(t)＜1,dd(t)＜1}＝P{dp(t)＜1}P{dd(t)＜1|dp(t)<1}
Wherein R (t) is the reliability of the whole machine after mechanism and data fusion, the unit is 1;t for t time, the unit is h, and d p (t) and d d (t) are respectively a certain norm of the damage vectors DP (t) and DD (t), and can be expressed as follows:
dp(t)=||DP(t)||
dd(t)=||DD(t)||
Wherein DP (t) represents damage of one part calculated by the mechanism model and is a random vector changing with time, and DD (t) represents damage of the other part obtained by data statistics and is also a random vector changing with time.
In the second step of the invention, an energy conservation equation, a mass conservation equation and an ideal gas state equation are established for the volumes of all subsystems, the change of state parameters under each crank angle is considered, and a forecast correction method is adopted for solving. In step three, a dynamic model is built based on crank-link mechanism kinematics and dynamics and the loads of the components are solved. In the fourth step, the component load obtained based on dynamics is used as a boundary condition, the abrasion loss of the friction pair is calculated based on an Arcard model, the fatigue of the component is calculated based on an S-N curve of the material and a fatigue accumulated damage theory, and samples with corresponding distribution are generated for uncertainty parameters in the abrasion model and the fatigue model by a Monte Carlo algorithm. In the fifth step of the invention, the reliability of the component and the mechanism model of the whole machine is obtained by statistically analyzing the abrasion and the fatigue damage of the multiple samples. In the step six of the invention, the reliability of the component statistical model is obtained from the statistical priori data, and the reliability of the whole machine is obtained after the component statistical model is fused with the mechanism model.
Compared with the prior art, the method has the beneficial effects that compared with the traditional method for predicting the reliability of the diesel engine, the method has two remarkable characteristics. Firstly, the method aims at modeling the real-time reliability of the diesel engine, and can comprehensively consider the coupling relation between the real-time abrasion loss and the performance of the piston ring. Secondly, the method not only focuses on the mechanism model of the diesel engine part, but also can fuse the statistical model of the prior statistical data. The invention fuses the mechanism model and the statistical data to predict the reliability of the whole diesel engine, and the confidence coefficient of the reliability calculation of the diesel engine is obviously improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of in-cylinder real-time pressure in accordance with an embodiment of the present invention;
FIG. 3 is a real-time temperature map in a cylinder according to an embodiment of the present invention;
FIG. 4 is a graph showing the damage profile of a piston ring after 8000 hours of operation of an embodiment of the present invention;
FIG. 5 is a graph showing the damage profile of a cylinder liner after 8000 hours of operation of an embodiment of the present invention;
FIG. 6 is a reliability graph of a complete machine mechanism model according to an embodiment of the present invention;
FIG. 7 is a diagram of overall reliability of a diesel engine with mechanism and data fusion in accordance with an embodiment of the present invention.
Detailed Description
In order to make the description of the present invention easier to understand, the following further explains the technical solution of the present invention in conjunction with the specific embodiment. The examples are only for illustration of the invention, but the invention is not limited to this. The procedures used in the examples below without specifying the particular conditions are generally carried out under conventional conditions or in accordance with the product specifications.
Examples
As shown in fig. 1 to 6, the present invention is realized by the following technical solutions:
step one, obtaining the real input parameters of a certain model. The required material property parameters and engine performance parameters are shown in tables 1 and 2.
TABLE 1 Engine structural parameters
TABLE 2 Material Property parameters
And step two, constructing a performance model. The state parameters vary with crank angle assuming that the pressure, temperature, etc. parameters inside each subsystem are uniformly distributed in spatial position. The differential equation set formed by the simultaneous three basic equations adopts a prediction correction method to solve the change of the state parameters in each subsystem along with the crank angle.
The energy conservation equation is expressed as:
the mass conservation equation is expressed as:
The ideal gas state equation is expressed as:
pzVz＝mzRTz
Wherein: The unit of the crank angle is deg, the unit of m z is kg, the unit of u z is the specific internal energy of the working medium in the cylinder, the unit of J/kg, the unit of m in is the air intake mass, the unit of h i is the specific enthalpy of air intake, the unit of J/kg, the unit of Q f is the heat brought by fuel injected into the cylinder, the unit of J is Q w is the heat transfer energy dissipated by heat transfer, the unit of J, the unit of m o is the exhaust mass, the unit of h o is the specific enthalpy of exhaust, the unit of J/kg, the units of p z and V z are the pressure and volume in the subsystem, the unit of Pa and m 3;gf are the fuel mass, the unit of kg, the unit of x is the combustion percentage in the cylinder, the unit of T z is the temperature in the cylinder, and the unit of R z is the gas constant of the working medium, and the unit of J/(kg.K); can be expressed as
Wherein m p and m d are respectively the quality coefficients of premixed combustion and diffusion combustion, and Q d is the fuel fraction of diffusion combustion; And Combustion duration angles of premixed combustion and diffusion combustion respectively,The combustion start angle is τ, which is the premixed combustion leading angle, and the units are deg.
And thirdly, constructing a dynamic model. Taking the cylinder pressure and the temperature in the cylinder (shown in fig. 2 and 3) calculated by the performance model as boundary conditions, according to crank link mechanism dynamics analysis, the stress of each part can be expressed as follows:
Wherein F ∑ is the acting force on the small end of the connecting rod along the center line of the cylinder, which can be decomposed into a piston lateral force F NG and a connecting rod thrust F C, the units are N, the connecting rod thrust F C can be further decomposed into a normal force F R along the crank direction and a tangential force F T perpendicular to the crank direction, the units are N, F P is the acting force at the crank pin, the units are N, F K is the acting force at the main journal, the units are N, m p,mc and m K are respectively expressed as piston mass, connecting rod mass and crank unbalanced mass, the units are kg, the connecting rod mass is always equivalent to two parts, respectively expressed as a reciprocating part m CA and a rotary part m CB, the units are kg, F g is expressed as the gas pressure of the piston which is periodically changed in the cylinder, the units are N, L cb is the distance from the center of the connecting rod centroid to the center of the large end of the connecting rod, the unit is m, the unit is the m is the angle of the center of the large end of the connecting rod, the ω is the angular velocity, the unit is the rad, the unit is the R is the crank radius, the unit is the swing angle, The unit is crank angle, the unit is deg, lambda is the ratio of crank radius to connecting rod length, D P is piston diameter, the unit is m, m r represents total mass which is concentrated at the crank pin and rotates along with the crank, the unit is kg, and the total mass can be expressed as:
mr＝mK+mCB
Wherein m K and m CB are respectively the unbalanced mass of the crank and the mass of the rotary motion part of the connecting rod, and the units are kg.
And fifthly, calculating the abrasion and fatigue of multiple samples of the key parts. According to the Archard wear model, the critical friction pair component wear depth can be expressed as:
Wherein H is the abrasion depth, the unit is m, l is the sliding distance of a friction pair, the unit is m, K is the dimensionless abrasion coefficient, H is the Brinell hardness of the material, the unit is HBW, p is the contact stress, and the unit is Pa, and the abrasion coefficient can be expressed as:
Wherein F is normal contact pressure, the unit is N, S is contact area, and the unit is m 2.
Wear damage is expressed as:
Wherein D w represents wear damage (as shown in FIGS. 4 and 5), 1;t represents time, h and h c are the wear depth and critical wear depth, respectively, in mm.
When the wear damage reaches 1, the component fails.
The method is particularly used for feeding back medium leakage in the cylinder caused by the reduction of the tightness of the cylinder due to the abrasion of a friction pair of a cylinder sleeve and a piston ring to the calculation and analysis of a performance model.
Component fatigue is calculated from the S-N curve of the material, and the number of cycles under the symmetrical cyclic stress amplitude is expressed as:
lgN=a+blgσ-1
Wherein N is the corresponding failure cycle times of the stress amplitude, the unit is 1, sigma -1 represents the stress amplitude of the material under the symmetrical loading with the stress ratio of-1, the unit is MPa, a and b are material constants, and the stress amplitude is obtained according to the S-N curve of the material. Since the forces to which the component is subjected are asymmetric loading forces, when calculating fatigue life using an S-N curve, the effect of the average stress is usually corrected by using the Goodman formula, which is expressed as:
Wherein, sigma b is the tensile limit of the material, sigma m and sigma a are the stress mean value and the stress amplitude under the actual working condition, sigma -1 is the stress amplitude when the stress ratio is-1, and the units are all MPa.
Fatigue damage is damage at all stress levels added up, expressed as:
Wherein D j represents fatigue damage, 1;t represents time, h, n i represents that the component has completed n i cycles under the stress amplitude sigma i, 1;N i represents the total number of cycles the component can bear under the stress amplitude, 1;m represents the number of different stress amplitudes, 1, and the component fails when the fatigue damage reaches 1.
And step six, calculating the reliability of the mechanism model and fusing the reliability with the statistical model. The damage of each component is statistically analyzed to obtain the reliability of each component, and the calculation principle is expressed as follows:
Wherein t represents time in h, n represents the total number of samples, D i represents the damage of the ith sample, Expressed as the reliability of the jth component at time t, the units 1;I (x) are all indicative functions, expressed as:
Further, the reliability of the complete machine mechanism model (as shown in fig. 6) can be expressed as:
Wherein t represents time in h, P { d p (t) <1} is the reliability of the mechanism model of the whole machine at the time t, j represents the j-th component, Expressed as the reliability of the jth component at time t, in 1.
Step seven, calculating the reliability of the failure rate data of the counted components, and for the common components with the failure rate determined by the reliability test and the data statistics, such as some sealing elements and common components, considering that the failure data of the components are obtained under the condition that the key components normally operate, and assuming that the failures of the components are mutually independent, obtaining the reliability of the whole machine after being fused with a mechanism model (as shown in fig. 7), wherein the calculation principle is as follows:
R(t)=P{dp(t)＜1,dd(t)＜1}＝P{dp(t)＜1}P{dd(t)＜1|dp(t)<1}
Wherein R (t) is the reliability of the whole machine after mechanism and data fusion, the unit is 1;t, the unit is h, and the unit is P { d d(t)＜1|dp (t) <1} can be expressed as:
Where P { DD k (t) <1} is the reliability of the kth component of the data statistics, and is expressed as 1:
where k is the kth component counted by data, the unit is 1;t represents time t, eta represents time eta, zeta represents time zeta, the units are h, lambda k (eta) is the instantaneous failure rate of the kth component at time eta, and the units are h -1.dp (t) and d d (t) are some norms of damage vectors DP (t) and DD (t) respectively, and can be expressed as follows:
dp(t)=||DP(t)||
dd(t)=||DD(t)||
Wherein DP (t) represents damage of one part calculated by the mechanism model and is a random vector changing with time, and DD (t) represents damage of the other part obtained by data statistics and is also a random vector changing with time.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (8)
1. A method for predicting the overall reliability of a diesel engine by mechanism and data fusion is characterized by comprising the following steps:
Step one, inputting basic modeling parameters, including material attribute parameters and engine structure and performance parameters;
Step two, constructing an engine performance model, namely solving a differential equation set formed by a simultaneous energy conservation equation, a mass conservation equation and an ideal gas state equation by adopting a prediction correction method in order to solve the change of state parameters in each subsystem along with the crank angle;
The third step of dynamic model construction, which is to take parameters such as cylinder pressure, temperature and the like obtained by an engine performance model as boundary conditions, and calculate the load of each part based on the kinematics and dynamics of a crank-connecting rod mechanism;
Step four, calculating the abrasion and fatigue of multiple samples of the key component, wherein the multiple samples are generated based on a Monte Carlo algorithm, the abrasion loss of a friction pair is calculated based on an Arcard model, and the fatigue of the component is calculated based on a fatigue accumulated damage theory according to an S-N curve of the material;
Step five, reliability analysis of the mechanism model, namely carrying out statistical analysis on abrasion and fatigue of multiple samples of the obtained parts to obtain the reliability of each part;
and step six, analyzing the reliability of the whole machine after the statistical model is fused, namely calculating the reliability of the failure rate data of the counted parts, and fusing the reliability obtained by the calculation of the mechanism model to obtain the reliability of the whole machine after the fusion.
2. The method according to claim 1, wherein the material property parameters include Poisson' S ratio of key component material, elastic modulus of material, hardness of material, S-N curve of crankshaft material, S-N curve of connecting rod material, tensile strength of crankshaft material, tensile strength of connecting rod material, engine structure and performance parameters include heat release rate, valve timing, stroke, cylinder diameter, compression ratio, number of cylinders, number of valves, firing order, rotation speed, power, volume size, connecting rod ratio, piston mass, connecting rod mass, crank imbalance mass, connecting rod length, distance from connecting rod centroid to connecting rod large end center, crank rotation angle speed, crank radius, piston diameter, distance from main journal middle section to nearest crank middle section, crank arm width, crank arm thickness, crank pin section diameter, main journal section diameter, rod body section area, key friction pair contact length, key friction pair component wear coefficient, key friction pair fitting clearance, key friction pair replacement limit clearance.
3. The method for predicting the overall reliability of a diesel engine by mechanism and data fusion according to claim 1, wherein in the second step, the energy conservation equation is:
Wherein: The unit of the crank angle is deg, the unit of m z is the mass of working medium, the unit of u z is the specific internal energy of working medium in a cylinder, the unit of J/kg, the unit of m in is the mass of air intake, the unit of h i is the specific enthalpy of air intake, the unit of J/kg, the unit of Q f is the heat brought by fuel injected into a cylinder, the unit of J, the unit of Q w is the heat transfer and dissipation energy, the unit of J, the unit of m o is the mass of exhaust, the unit of h o is the specific enthalpy of exhaust, the unit of J/kg, the unit of p z is the pressure in a subsystem, the unit of Pa, the unit of V z is the internal volume of the subsystem, and the unit of m 3;
The mass conservation equation is:
Wherein: The unit of the crank angle is deg, the unit of m z is kg of working medium, the unit of m in is kg of air intake, the unit of m o is kg of exhaust, the unit of g f is kg of fuel oil, and the unit of x is the combustion percentage of fuel oil in an air cylinder;
the ideal gas state equation is expressed as:
pzVz＝mzRzTz
Wherein p z is the pressure in the subsystem, the unit is Pa, V z is the internal volume of the subsystem, the unit is m 3;mz and represents the mass of working medium, the unit is kg, T z is the temperature in the cylinder, the unit is K, R z is the gas constant of the working medium, and the unit is J/(kg.K).
4. The method for predicting overall reliability of a diesel engine by mechanism and data fusion according to claim 1, wherein in the third step, the load of each component is expressed as:
wherein F Σ is the acting force on the small end of the connecting rod along the center line of the cylinder, which can be decomposed into a piston lateral force F NG and a connecting rod thrust F C, the units are N, the connecting rod thrust F C can be further decomposed into a normal force F R along the crank direction and a tangential force F T perpendicular to the crank direction, the units are N, F P is the acting force at the crank pin, the units are N, F K is the acting force at the main journal, the units are N, m p,mc and m K are respectively expressed as piston mass, connecting rod mass and crank unbalanced mass, the units are kg, the connecting rod mass is always equivalent to two parts, respectively expressed as a reciprocating part m CA and a rotary part m CB, the units are kg, F g is expressed as the gas pressure of the piston which is periodically changed in the cylinder, the units are N, L cb is the distance from the center of the connecting rod centroid to the center of the large end of the connecting rod, the unit is m, the unit is the m is the angle of the center of the large end of the connecting rod, the ω is the angular velocity, the unit is the rad, the unit is the R is the crank radius, the unit is the swing angle, The unit is crank angle, the unit is deg, lambda is the ratio of crank radius to connecting rod length, D P is piston diameter, the unit is m, m r represents total mass which is concentrated at the crank pin and rotates along with the crank, the unit is kg, and the total mass can be expressed as:
mr＝mK+mCB
Wherein m K and m CB are respectively the unbalanced mass of the crank and the mass of the rotary motion part of the connecting rod, and the units are kg.
5. The method for predicting the reliability of the whole diesel engine by combining mechanism and data according to claim 1, wherein in the fourth step, the sample is generated by adopting a monte carlo algorithm, the distribution functions of the uncertainty parameters are determined by considering the influence of uncertainty factors such as material performance, manufacturing assembly, external load and the like, the distribution functions of the uncertainty parameters are determined, a series of random numbers are generated, the random numbers are transformed to obtain samples of the corresponding probability distribution functions, the samples are substituted into the corresponding uncertainty parameters, and a large number of random samples of the corresponding distribution functions are generated for each uncertainty parameter.
6. The method for predicting overall reliability of a diesel engine by mechanism and data fusion according to claim 1, wherein in the Archard wear model in step four,
The depth of wear of the critical friction pair components can be expressed as:
Wherein H is the abrasion depth, the unit is m, l is the sliding distance of a friction pair, the unit is m, K is the dimensionless abrasion coefficient, H is the Brinell hardness of the material, the unit is HBW, p is the contact stress, and the unit is Pa, and the abrasion coefficient can be expressed as:
Wherein F is normal contact pressure, the unit is N, S is contact area, and the unit is m 2;
wear damage can be expressed as:
Wherein D w represents abrasion damage, the unit is 1;t, the unit is h, h and h c are respectively the abrasion depth and the critical abrasion depth, the unit is mm, and when the abrasion damage reaches 1, the component fails;
The number of cycles at which fatigue is damaged with a symmetric cyclic stress amplitude can be expressed as:
lgN=a+blgσ-1
Wherein N is the corresponding failure cycle times of the stress amplitude, the unit is 1, sigma -1 represents the stress amplitude of the material under the symmetrical loading with the stress ratio of-1, the unit is MPa, a and b are material constants, and the stress amplitude is obtained according to the S-N curve of the material. Since the forces to which the component is subjected are asymmetric loading forces, when calculating fatigue life using an S-N curve, the effect of the average stress is usually corrected by using the Goodman formula, which can be expressed as:
Wherein, sigma b is the tensile limit of the material, sigma m and sigma a are the stress mean value and the stress amplitude under the actual working condition, sigma -1 is the stress amplitude when the stress ratio is-1, and the units are all MPa;
Fatigue damage is damage at all stress levels added up and can be expressed as:
Wherein D j represents fatigue damage, 1;t represents time, h, n i represents that the component has completed n i cycles under the stress amplitude sigma i, 1;N i represents the total number of cycles the component can bear under the stress amplitude, 1;m represents the number of different stress amplitudes, 1, and the component fails when the fatigue damage reaches 1.
7. The method for predicting the reliability of a complete diesel engine by mechanism and data fusion according to claim 1, wherein in the fifth step, the reliability of each component is obtained by statistical analysis, and the calculation principle is expressed as follows:
Wherein t represents time in h, n represents the total number of samples, D i represents the damage of the ith sample, Expressed as the reliability of the jth component at time t, the units 1;I (x) are all indicative functions, expressed as:
The reliability of the mechanism model of the whole machine can be expressed as follows:
Wherein t represents time in h, P { d p (t) <1} is the reliability of the mechanism model of the whole machine at the time t, j represents the j-th component, Expressed as the reliability of the jth component at time t, in 1.
8. The method for predicting the reliability of a complete diesel engine machine by mechanism and data fusion according to claim 1, wherein in the step six, the reliability of the component failure rate data calculated and counted is calculated, and for the common components for which the failure rate is determined by the reliability test and the data statistics, the failure data of the components can be considered to be obtained under the condition that the critical components are normally operated, and assuming that the failures of the components are independent of each other, the reliability of the components is expressed as:
Where t represents time in h, d p (t) and d d (t) are some norms of the injury vectors DP (t) and DD (t), respectively, and can be expressed as:
dp(t)=||DP(t)||
dd(t)=||DD(t)||
wherein DP (t) represents damage of one part calculated by the mechanism model and is a random vector changing with time, DD (t) represents damage of the other part obtained by data statistics and is a random vector changing with time, and P { DD k (t) <1} is reliability of the kth part of the data statistics, wherein the unit is 1, and the reliability can be expressed as:
Wherein k is the kth component counted by data, the unit is 1;t, eta is eta, zeta is h, lambda k (eta) is the instantaneous failure rate of the kth component at eta, and the unit is h -1;
the reliability of the integrated whole machine is calculated according to the following principle:
R(t)=P{dp(t)＜1,dd(t)＜1}＝P{dp(t)＜1}P{dd(t)＜1|dp(t)<1}
Wherein R (t) is the reliability of the whole machine after mechanism and data fusion, the unit is 1;t for t time, the unit is h, and d p (t) and d d (t) are respectively a certain norm of the damage vectors DP (t) and DD (t), and can be expressed as follows:
dp(t)=||DP(t)||
dd(t)=||DD(t)||
Wherein DP (t) represents damage of one part calculated by the mechanism model and is a random vector changing with time, and DD (t) represents damage of the other part obtained by data statistics and is also a random vector changing with time.
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