Method for determining overhaul life of mechanical product based on preventive maintenance

Abstract

The invention discloses a mechanical product overhaul life determination method based on preventive maintenance, and relates to the technical field of mechanical product reliability and maintenance engineering, and the method comprises the following steps: S1, constructing a mechanical product disciplinary cross equation; s3, constructing a mechanical product performance degradation model based on preventive maintenance, and estimating unknown parameters by adopting a maximum likelihood estimation method so as to determine a relationship between a performance degradation amount and time; and S4, estimating parameters of the performance degradation model and finally determining the overhaul life of the mechanical product. According to the method, the influence of preventive maintenance on the mechanical product performance is comprehensively considered, the performance fluctuation and degradation process caused by the maintenance activity is described by constructing the segmented performance degradation function, and the relationship between the performance degradation amount and the time is determined by using the maximum likelihood estimation method; and the finally determined overhaul life of the mechanical product is more accurately applied to an actual engineering scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical product reliability and maintenance engineering, in particular to a method for determining overhaul life of mechanical products based on preventive maintenance. BACKGROUND

[0002] Overhaul is a deep maintenance for core components such as engines, gearboxes and the like, which involves comprehensive disassembly, inspection and replacement, aiming to restore performance and prolong service life, usually involving high cost and complex process. With the development of equipment towards high performance and high complexity, the performance degradation law and maintenance strategy of mechanical products have become key problems in engineering practice. At present, the research on life prediction of mechanical products mainly focuses on the construction of performance degradation model, which infers the remaining life of mechanical products through monitoring and analysis of key parameters. According to the theory of confidence reliability, the performance of mechanical products must degrade during operation, and quantifying this degradation process can clarify the performance degradation law. At present, the method for evaluating the overhaul life of mechanical products, such as diesel power system, generally infers the remaining life of mechanical products by monitoring and analyzing key parameters such as main oil passage pressure, supercharger efficiency, intake and exhaust pressure.

[0003] However, on the one hand, the existing method often ignores the influence of preventive maintenance on the performance of mechanical products, and cannot accurately reflect the performance fluctuation caused by maintenance activities in the actual service process. Preventive maintenance, as an important means to ensure the reliability of mechanical products, can temporarily improve the performance of mechanical products by regular or conditional inspection, maintenance and repair, etc., so that the mechanical products can return to a better operating state. There are three strategies: time-based maintenance, condition-based maintenance and proactive maintenance. Time-based maintenance is carried out according to the specified interval or cumulative working time; condition-based maintenance is carried out for mechanical products with failure symptoms through regular or continuous monitoring; proactive maintenance identifies root cause factors and controls in advance. However, with the increase of maintenance times, its effect will gradually weaken, making the performance degradation process of mechanical products show complex swing characteristics.

[0004] On the other hand, there are a large number of uncertain factors in the actual operation of mechanical products, and the existing model has shortcomings in dealing with uncertain factors in the performance degradation process, and it is difficult to quantify the influence of uncertainty on the life prediction result. In addition, in the aspect of parameter estimation, the adaptability of traditional methods to nonlinear degradation model is poor, resulting in low accuracy of model parameter estimation and affecting the accuracy of overhaul life determination. SUMMARY

[0005] In order to solve the above-mentioned problems of the prior art, the present application aims to provide a preventive maintenance-based mechanical product overhaul life determination method, so as to solve the problem that the prior art has less research on the preventive maintenance-based, uncertain factor and nonlinear degradation characteristic mechanical product overhaul life determination method. Therefore, it is necessary to develop a preventive maintenance-based, uncertain factor and nonlinear degradation characteristic mechanical product overhaul life determination method, so as to improve the reliability of life prediction and the scientificity of maintenance decision.

[0006] Specifically, the present application provides a preventive maintenance-based mechanical product overhaul life determination method, which comprises the following steps: S1, constructing a mechanical product interdisciplinary equation: Modeling and coupling the mechanical product components, constructing an average value model to form an interdisciplinary equation; S2, constructing a mechanical product performance degradation model: Based on the confirmed reliability theory, modeling the degradation law of the key parameters affecting the performance of the mechanical product during the operation of the mechanical product, and integrating to obtain a mechanical product performance degradation model; S3, constructing a preventive maintenance-based mechanical product performance degradation model, which divides the degradation process into multiple discontinuous stages , wherein the left limit and the right limit respectively represent the left and right limits of the discontinuous stage , and the segmented performance degradation function of the performance degradation function of the mechanical product in the stage is represented as: In the formula: is a maintenance coefficient for describing the weakening of the maintenance result, is a maintenance item function for describing the maintenance item, is the degradation performance at the end of the stage , , is a performance degradation equation, is a diffusion coefficient and , represents a normal distribution function with an expected value of 0 and a standard deviation of 1, and the normal distribution function is represented as ~N(0,1), represents a normal distribution function with an expected value of and a standard deviation of ; Based on the preventive maintenance multi-stage division, the maintenance coefficient complies with the parameter estimation model of the multi-stage performance degradation attenuation: ​​ ; The unknown repair coefficient is evaluated by using maximum likelihood estimation method and diffusion coefficient , the relationship between preventive maintenance performance degradation and time is determined; S4, parameter estimation of performance degradation model and final determination of overhaul life of mechanical product: In the performance degradation model based on preventive maintenance, combined with the given performance threshold and reliability threshold , the reliability of mechanical product at time is calculated: ; When the performance is lower than the threshold , overhaul is carried out, and the maximum value of time that meets is determined as the overhaul life of mechanical product: ; In the formula: is the overhaul life of mechanical product; is the reliability of mechanical product at time ; is the given reliability threshold; represents the supremum.

[0007] Further, step S2 includes the following sub-steps: S21, determine the performance degradation law of maximum output power by quantifying the trend of degradation of key parameters of mechanical product with time; S22, simulate the performance degradation trend of mechanical product in real service environment by introducing the dynamic degradation mechanism of key parameters in step S21 into the numerical model, and build a prediction model that can reflect the performance degradation of mechanical product; S23, use experimental data driven method, based on historical operation data or bench test data, quantify the degradation trajectory of each parameter by piecewise regression, nonlinear fitting or machine learning algorithm, and model the degradation law of key parameters; S24, obtain the performance degradation data of mechanical product, select the optimal fitting model, and use least square method for parameter estimation; S25, analyze the degradation amount data of key parameters of the mechanical product under specified environment and working state until failure obtained in step S24; S26, perform degradation modeling on the degradation amount data in step S25; S27, integrate the dynamic changing parameter set into the established mechanical product numerical model, derive the performance of the mechanical product, such as the maximum power, maximum speed, maximum flow performance parameter, and the performance degradation function of the mechanical product with the degradation attenuation of the dynamic changing parameter; S28, identify the most significant degradation path affecting the maximum power by performing parameter sensitivity analysis on the mechanical product performance degradation function in step S27.

[0008] Further, in step S25, the method for analyzing the degradation amount data of the mechanical product working until failure under the specified environment and working state is: The specified parameter is estimated by the least square principle, and the observation data is the time series , wherein The degradation model is: ; In the formula: is the initial performance value, is the degradation rate, and the logarithmic transformation and least square estimation method are used for fitting and solving.

[0009] Further, in step S26, the method for degradation modeling is: The specified parameter is least square fitted, the model is taken as a natural logarithm, the residual sum of squares is minimized, the parameter is restored after solving, and the model is taken as a natural logarithm: ; Let , then ; Solve the transformed formula to minimize the residual sum of squares and restore the parameter; .

[0010] Further, step S3 includes the following sub-steps: S31, incorporate the effect of preventive maintenance into the mechanical product performance degradation model; S32, use to represent the set of all possible preventive maintenance items, and preventive maintenance will bring temporary improvement of the performance of the mechanical product and the effect will weaken with the increase of the number of maintenance; S33, consider the uncertainty factors existing in the degradation process of the mechanical product performance; S34, establish the performance degradation function of the mechanical product, and derive the performance expression of the degradation function stage; S35. Use the maximum likelihood estimation method to estimate the parameters of the performance degradation distribution and determine the relationship between the amount of performance degradation and time.

[0011] Furthermore: In step S33, the uncertainties existing in the performance degradation process of mechanical products are taken into consideration, and the performance degradation equation of mechanical products is expressed as: ; In the formula: This is the performance degradation equation, which represents the deterministic growth law of the performance degradation amount. Factors representing uncertainties in the degradation process.

[0012] Further: Step S4 includes the following sub-steps: S41. For degradation data of the same performance parameter of mechanical products, monitor and collect multiple sets of data in chronological order; S42. Use the maximum likelihood estimation method to estimate the unknown parameters in the performance degradation model constructed in step S3, and determine the relationship between the amount of performance degradation and time. S43. Construct the likelihood function; S44. Take the logarithm of the likelihood function in step S43 and solve the optimization problem. S45. Solve the analytical solution of the function in step S44 to obtain the performance degradation model; S46. Based on the performance degradation model obtained in step S45, specify when the performance is lower than a given performance threshold. When this happens, a major overhaul of the transmission machinery product is required, and the overhaul life of the machinery product is determined by combining the reliability formula of the machinery product.

[0013] Furthermore: the method for constructing the likelihood function in step S43 is as follows: Let For key performance parameters of mechanical products undergoing performance degradation testing, at each time... Key performance parameters collected Data on the amount of degradation, if there is If multiple devices are used for observation simultaneously, each time period will have its own... Data values When only one device is used for experimental observation, then in Each observation point has one data point. Assume the data points... It is in time The There are 100 observations, and they follow a certain probability distribution. Then the likelihood function is: .

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1、The present application comprehensively considers the influence of preventive maintenance on the performance of mechanical products, accurately describes the performance fluctuation and degradation process caused by maintenance activities by constructing a segmented performance degradation function, and determines the relationship between performance degradation and time by using maximum likelihood estimation method, so that the final determined overhaul life of the mechanical product is more in line with the actual engineering scene.

[0015] 2、The present application can effectively handle the uncertainty in the degradation process and improve the robustness of the overhaul life determination by introducing uncertainty factor quantization and constructing a performance degradation model using the sure reliability theory.

[0016] 3、The present application estimates the model parameters by using the maximum likelihood estimation method, which has a wide range of applications and can be applied to linear and nonlinear degradation models, and has high utilization rate of parameters, effectively improving the parameter estimation accuracy and laying a foundation for accurately determining the overhaul life.

[0017] 4、The present application is a general mechanical product performance degradation model, which can be applied to various mechanical products such as diesel engines, turbocharged mechanical products, etc., and provides a scientific basis for maintenance strategy formulation and health state evaluation, and has wide engineering application value. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The overall flowchart of the present application based on the preventive maintenance of the mechanical product overhaul life determination method is shown in the figure. Figure 2 The mechanical product performance degradation path schematic diagram of the training step S3 of the present application is shown in the figure. Figure 3 The mechanical product performance degradation schematic diagram based on preventive maintenance of the training step S4 of the present application is shown in the figure. Figure 4 The mechanical product performance degradation schematic diagram in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0019] Hereinafter, the embodiments of the present application will be described with reference to the accompanying drawings.

[0020] Example one: In the present application, based on the sure reliability theory, combined with the performance degradation characteristics of mechanical products and the influence of preventive maintenance, a performance degradation model based on preventive maintenance is constructed, and the overhaul life of the mechanical product is determined by parameter estimation and reliability analysis. Hereinafter, taking a diesel engine as an example, the method for determining the overhaul life of the mechanical product based on preventive maintenance is introduced.

[0021] The sub-mechanical products of the mechanical product are modeled respectively, the coupling of the sub-mechanical product models is realized, the complete mechanical product numerical model is established, the key degradation parameters such as the main oil way pressure of the diesel engine, the supercharger efficiency and the intake and exhaust pressures are identified based on the confirmed reliability theory, the experimental data driven method (such as piecewise regression, nonlinear fitting, etc.) is used to quantify the degradation trajectories of the key degradation parameters, the least square method is used for parameter estimation, the dynamic degradation parameters are integrated into the mechanical product numerical model, and the mechanical product performance degradation function is obtained: ; In the formula, is the maximum power of the mechanical product; are the intake temperature of the compressor, the exhaust temperature of the compressor, …, and the fuel mass flow rate in turn.

[0022] Further, the embodiment defines the preventive maintenance item set, the influence of preventive maintenance on the performance of the mechanical product is brought into the performance degradation model, and the piecewise performance degradation function is established: ; In the formula, is the maintenance coefficient used to describe the gradual weakening of the effect of maintenance with each maintenance, is a maintenance item function used to describe the maintenance items contained in each maintenance, represents the set of all possible preventive maintenance items that can improve the performance of the mechanical product, that is , represents the performance at the end of the stage, , is the performance degradation equation, is the diffusion coefficient, represents the normal distribution with an expected value of 0 and a standard deviation of 1, and is represented as , represents the normal distribution with an expected value of and a standard deviation of .

[0023] Based on the collected performance degradation data, the maximum likelihood estimation method is used to estimate the unknown parameters in the mechanical product performance degradation model based on preventive maintenance, the likelihood function is constructed and the logarithm is calculated, the numerical optimization method is used to solve, and the unknown parameters are obtained. Finally, according to the performance degradation model, the reliability of the mechanical product is calculated in combination with the given performance threshold and reliability threshold: ; In the formula, is the performance degradation amount, ​Pr is the probability that the mechanical product performance index can still meet the normal work requirements at time t.

[0024] When the reliability is lower than the given reliability threshold, the corresponding time is the overhaul life of the mechanical product, which can be obtained from the given reliability and performance threshold, and the overhaul life of the mechanical product is: ; In the formula: is the overhaul life of the mechanical product; is the reliability of the mechanical product at time ; is the given reliability threshold; represents the supremum, that is, the maximum time that satisfies .

[0025] In this embodiment, a method for determining the overhaul life of a mechanical product based on preventive maintenance is provided, as shown in Figures 1-3 , the specific steps include: S1, mechanical product discipline intersection equation construction, modeling and coupling of 6 main components of diesel engine, compressor, turbine, etc. of the two-stage turbocharged diesel engine average value model is constructed to form the discipline intersection equation.

[0026] S2, mechanical product performance degradation model construction, based on the sure reliability theory, the degradation law of key parameters such as main oil way pressure, supercharger efficiency and intake and exhaust pressure is modeled by using experimental data driven method, and the mechanical product performance degradation model is integrated.

[0027] S3, mechanical product performance degradation model based on preventive maintenance, based on the degradation equation definition in the sure reliability theory, the uncertainty factors are included in the model, the mechanical product performance degradation equation is constructed, the unknown parameters are estimated by using the maximum likelihood estimation method, and the relationship between the performance degradation amount based on preventive maintenance and time is determined.

[0028] S4, parameter estimation of performance degradation model and final determination of overhaul life of mechanical product: Based on the collected performance degradation data, the unknown parameters of the model are estimated by using the maximum likelihood estimation method, the likelihood function is constructed and the logarithm is taken, and then the parameters are solved by analytical solution or numerical optimization method, and the overhaul life of the mechanical product can be obtained from the given reliability and performance threshold according to the mechanical product reliability formula.

[0029] Specifically, the specific implementation process of step S1 is: S11, modeling each sub-mechanical product of the two-stage turbocharged diesel engine.

[0030] S12, by modeling and coupling 6 main components in the engine of the mechanical product, such as the compressor, the turbine rotor mechanism, the intercooler, etc., a numerical model of the multi-stage supercharged diesel engine is obtained, and the maximum power is a function of the intake and exhaust temperatures and other parameters; ; In the formula: is the maximum power of the mechanical product; is the intake temperature of the compressor, the exhaust temperature of the compressor, …, the mass flow of fuel, respectively.

[0031] The specific implementation process of step S2 is as follows: S21, by quantifying the gradual degradation trends of key parameters such as the main oil passage pressure, the supercharger efficiency, and the intake and exhaust pressures of the supercharged diesel engine during long-term operation, the performance degradation law of the core performance indicator, the maximum output power, is determined.

[0032] S22, by introducing the dynamic degradation mechanism of these parameters in step S21 into the existing numerical model, the performance degradation trend of the engine in the real service environment is simulated, and a prediction model that can reflect the long-term performance degradation of the diesel engine is constructed.

[0033] S23, an experimental data-driven method is adopted, based on historical operation data or bench test data, the degradation trajectories of various parameters are quantified through piecewise regression, nonlinear fitting or machine learning algorithms (such as Gaussian process regression), and the degradation laws of key parameters such as the main oil passage pressure, the supercharger efficiency, and the intake and exhaust pressures are accurately modeled.

[0034] S24, obtain the performance degradation data of a certain mechanical product, select the optimal fitting model through statistical testing in step S23, ensure that it conforms to the physical law and can capture the nonlinear characteristics in the data, and use the least squares method for parameter estimation.

[0035] S25, analyze the degradation amount data of the mechanical product in a specific environment and working state until failure obtained in step S24, taking the friction torque as an example, assuming that the parameter degradation form is exponential, the parameters are estimated by the least squares principle; the observation data is the time series , and the degradation model is: ; In the formula: is the initial performance value, is the degradation rate, which is fitted and solved by using the logarithmic transformation and the least squares estimation method.

[0036] ​S26, the least square fitting is performed on the engine friction torque, the residual square sum is minimized after the natural logarithm of the model is taken, the parameters are restored after the solution is obtained, and the natural logarithm of the model is taken as: ; Let , then; ; The result of minimizing the residual square sum is solved for the transformed equation, and the parameters are restored; .

[0037] S27, after the parameter degradation modeling of step S26 is completed, the set of dynamic changing parameters will be integrated into the established engine high-fidelity numerical model to obtain the maximum power Mechanical product performance degradation function degraded with intake air temperature and other parameters; ; In the formula: is the maximum power of the mechanical product; is the intake air temperature of the compressor, the exhaust temperature of the compressor, …, and the fuel mass flow rate.

[0038] The specific implementation process of step S3 is: S31, the effects of preventive maintenance are included in the mechanical product performance degradation model, and the timing maintenance strategy, the on-demand maintenance strategy and the active maintenance strategy in preventive maintenance are analyzed according to the characteristics of the diesel-electric hybrid vehicle.

[0039] S32, combined with the characteristics of traditional diesel mechanical products, the preventive maintenance effects that need to be considered for diesel engine mechanical products mainly include: regular replacement of engine oil and filter, especially in desert / dusty environment, inspection of fuel mechanical products such as inspection of high-pressure common rail mechanical products, fuel nozzle atomization, etc., adjustment of valve clearance, adjustment of timing belt and tensioner, etc. In subsequent modeling, all possible sets of such preventive maintenance items are represented by These maintenance will bring temporary improvement of mechanical product performance and the effect will decrease with the increase of maintenance times, that is .

[0040] S33, the uncertainty factors existing in the mechanical product performance degradation process are considered, and the mechanical product performance The degradation equation is expressed as: ; In the formula: is the performance degradation equation of the mechanical product performance degradation, that is, the deterministic growth law in the performance degradation amount, is the factor of the uncertainty factor in the degradation process.

[0041] S34、Since the preventive maintenance activities divide the whole degradation process into several discontinuous phases, in order to better distinguish the maintenance activities, the left and right boundaries are given, which are represented by the left boundary and the right boundary ; the maintenance is performed at the left boundary , which is the end of the discontinuous phase ; the maintenance activity ends at the right boundary , which indicates the beginning of the performance degradation function phase, thereby establishing the performance degradation function of the mechanical product in the phase The performance of the phase can be represented as: ; In the formula, is the maintenance coefficient used to describe the gradual weakening of the effect of maintenance with each maintenance, is the maintenance item function used to describe the maintenance items contained in each maintenance, represents the set of all possible preventive maintenance items that can improve the performance of the mechanical product, i.e. , represents the performance at the end of the phase , , is the performance degradation equation, is the diffusion coefficient, represents a normal distribution with an expected value of 0 and a standard deviation of 1, represented as , represents a normal distribution with an expected value of and a standard deviation of .

[0042] S35、The degradation path of performance is shown in Figure 2 , assuming that the maintenance coefficient after each maintenance in step S34 conforms to the following parameter estimation model: .

[0043] Since is a function related to , the maximum likelihood estimation method is used to realize the parameter estimation of the performance degradation distribution, and the unknown parameters and are determined in the subsequent step S4, and then the relationship between the performance degradation amount and time is determined.

[0044] ​The step accurately describes the performance fluctuation and degradation process caused by maintenance activities by constructing a segmented performance degradation function, and determines the relationship between performance degradation and time using maximum likelihood estimation method, so that the final determined overhaul life of mechanical products is more in line with actual engineering scenarios, and by introducing uncertainty factors, using the sure reliability theory to construct the performance degradation model, the uncertainty in the degradation process can be effectively handled, and the determined overhaul life is more accurate.

[0045] The specific implementation process of step S4 is: S41, for the degradation data of the same performance parameter of the mechanical product, a plurality of data are monitored and collected in time sequence, for example, from a plurality of devices or a plurality of observation points of a single device, if the performance degradation problem of the mechanical product is studied, The key performance parameters of the mechanical product are detected in the performance degradation state, it is known that at each time The degradation amount data of the key performance parameters collected , and if there are devices for observation at the same time, there are data values at each time , if only one device is used for experimental observation, there is one data at each observation point.

[0046] S42, the unknown parameters in the performance degradation model constructed in step S3 are estimated by maximum likelihood estimation method, and the relationship between performance degradation and time is determined.

[0047] S43, the likelihood function is constructed, assuming that the data point is the observation value at time , and it is subject to a certain probability distribution , then the likelihood function is: .

[0048] S44, take the logarithm to calculate and solve the optimization problem: ; .

[0049] S45, the analytical solution of the function in step S44 is solved, if the analytical solution exists such as linear model, the derivative can be directly solved. If it is nonlinear such as exponential model, the solution can be obtained by numerical optimization method such as gradient descent, Newton method, etc.

[0050] S46, according to the performance degradation model obtained by solving step S45, when the performance is lower than the given performance threshold , the transmission mechanical product needs to be overhauled, combined with the reliability formula of mechanical product;​ .

[0051] When the reliability is lower than the given reliability threshold, the corresponding time is the overhaul life of the mechanical product, which can be obtained from the given reliability and performance threshold, and the overhaul life of the mechanical product is: ; In the formula: is the overhaul life of the mechanical product; is the reliability of the mechanical product at time ; is the given reliability threshold; represents the supremum, that is, the maximum time satisfying .

[0052] This step can be applied to linear and nonlinear degradation models by using the maximum likelihood estimation method to estimate the model parameters, and the utilization rate of the parameters is high, especially suitable for the case that the parameter sample is large, and the parameter estimation accuracy can be effectively improved.

[0053] Example two: In another preferred embodiment of the application, the mechanical product has preventive maintenance items such as replacing the filter, adjusting the lubricating oil level, etc., that is, The degradation trend of the key performance over time and the performance improvement brought by maintenance are shown in Table 1: Table 1 Degradation of key performance of mechanical product and change of maintenance Maximum output power Time Notes 1000 kw 0 Initial value 947 kw 200 days First minor repair: adjust lubricating oil level 969 kw After minor repair 856 kw 400 days Second minor repair: replace filter, replace lubricating oil 918 kw After minor repair 813 kw 600 days Third minor repair: replace hydraulic mechanical product filter, adjust lubricating oil level 878 kw After minor repair 771 kw 800 days Fourth minor repair: replace filter, replace lubricating oil 849 kw After minor repair 743 kw 1000 days Below threshold, should be overhauled The application is applied to the mechanical product embodiment for verification. It is assumed that the function of performance improvement brought by maintenance is: ; ; Each parameter is brought into the mechanical product performance degradation model based on preventive maintenance of the mechanical product, and the parameters in the degradation model can be estimated by the maximum likelihood method, that is, .7574.

[0054] As shown in Figure 4 , for the threshold value 75% of the key performance parameter efficiency of the given mechanical product, the reliability of the mechanical product can be obtained as the probability that the key performance efficiency is greater than the threshold value 75%: .

[0055] For the given reliability threshold value 98%, the overhaul life of the mechanical product is the task time of the mechanical product corresponding to the reliability greater than 98%: . According to the historical data and parameter estimation, at the time of 1000 task days, the probability of the performance function of the key performance of the mechanical product, i.e., the mechanical product efficiency, degrading to not less than the 75% threshold has been less than or equal to 98%, and the mechanical product needs to be overhauled.

[0056] It can be seen that the mechanical product overhaul life determination method based on preventive maintenance disclosed in the present application can comprehensively consider the influence of preventive maintenance on the performance of the mechanical product, introduce an uncertainty factor for quantification, be more suitable for actual engineering scenarios, obtain more accurate prediction results of the overhaul life of the mechanical product, and be beneficial to the safe maintenance of the mechanical product.

[0057] The above-described embodiments are only used to describe the preferred embodiments of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A method for determining overhaul life of a mechanical product based on preventive maintenance, characterized by, It comprises the following steps: S1, constructing a mechanical product discipline intersection equation: Modeling and coupling the components of the mechanical product, constructing an average value model to form a discipline intersection equation; S2, constructing a mechanical product performance degradation model: Based on the theory of confirmed reliability, modeling the degradation law of the key parameters affecting the performance of the mechanical product during operation, and integrating to obtain a mechanical product performance degradation model; S3. Construct a performance degradation model for mechanical products based on preventive maintenance. Preventive maintenance divides the degradation process into multiple discontinuous stages. Use the left boundary and right boundary These represent discontinuous stages. The left and right boundaries are used to establish the performance degradation function of mechanical products. Piecewise performance degradation function for each stage Represented as: ； wherein: is a repair coefficient to describe a degradation of repair result, is a repair item function to describe a repair item, is a performance degradation function at the end of a phase, , is a performance degradation equation, is a diffusion coefficient and , denotes a normal distribution function with expectation 0 and standard deviation 1, which is denoted as ~N(0, 1), represents a normal distribution function with expectation and standard deviation ; Maintenance coefficient based on preventive maintenance multi-stage division Parameter estimation model consistent with multi-stage performance degradation attenuation: ； The maximum likelihood estimation method is used to estimate the unknown maintenance coefficient and the diffusion coefficient The preventive maintenance performance degradation and time relationship are determined by evaluation. S4, parameter estimation of the performance degradation model and final determination of the overhaul life of the mechanical product: In a performance degradation model based on preventive maintenance, the reliability of a mechanical product at time is calculated in combination with a given performance threshold and a reliability threshold . ； Prescribed performance is below a threshold Overhaul is performed at a time determined to satisfy The maximum value of the time The overhaul life of the mechanical product: ； wherein: is the overhaul life of the mechanical product; is the reliability of the mechanical product at time is the reliability of the mechanical product at time is a given reliability threshold; denotes the supremum.

2. The preventive maintenance-based mechanical product overhaul life determination method according to claim 1, characterized by, Step S2 includes the following sub-steps: S21, determining the performance degradation law of the maximum output power by quantifying the trend of the degradation of the key parameters over time during the operation of the mechanical product; S22, constructing a prediction model capable of reflecting the performance degradation of the mechanical product by introducing the dynamic degradation mechanism of the key parameters in step S21 into the numerical model to simulate the performance attenuation trend of the mechanical product under real service environment; S23, using an experimental data driven method, based on historical operation data or bench test data, quantifying the degradation trajectory of each parameter by piecewise regression, nonlinear fitting or machine learning algorithm to model the degradation law of the key parameters; S24, obtaining the performance degradation data of the mechanical product, selecting the optimal fitting model, and using the least squares method for parameter estimation; S25, analyzing the degradation amount data of the key parameters of the mechanical product under the specified environment and working state until failure; S26, modeling the degradation in step S25; S27, integrating the dynamically changing parameter set into the established numerical model of the mechanical product to obtain a mechanical product performance degradation function in which the performance of the mechanical product degrades and attenuates with the dynamic change of the parameters; S28, identifying the most significant degradation path that affects the maximum power most significantly by performing parameter sensitivity analysis on the mechanical product performance degradation function in step S27.

3. The preventive maintenance-based mechanical product overhaul life determination method according to Claim 2, characterized by, In step S25, the method for analyzing the degradation amount data of the key parameters of the mechanical product under the specified environment and working state until failure is: estimating the specified parameters using the least squares principle with the observation data being a time series where and the reduced model being ； In the formula: is the initial performance value, is the degradation rate, which is solved by fitting with logarithmic transformation and least square estimation method.

4. The preventive maintenance-based mechanical product overhaul life determination method according to Claim 3, characterized by, In step S26, the method for modeling the degradation is: For specified parameters Perform least squares fitting, minimize the sum of squared residuals after taking the natural logarithm of the model, solve for the restored parameters, and take the natural logarithm of the model as follows: ； Let then; ； Solving the result of minimizing the residual sum of squares of the transformed formula and restoring the parameters; 。 5. The preventive maintenance-based mechanical product overhaul life determination method according to Claim 1, characterized by, Step S3 includes the following sub-steps: S31, incorporating the effect of preventive maintenance into the mechanical product performance degradation model; S32、use to represent all possible sets of preventive maintenance items that bring about a temporary increase in the performance of the mechanical product and whose effect decreases with the number of maintenance operations. S33, considering the uncertainty factors existing in the performance degradation process of the mechanical product; S34, establishing a performance degradation function of the mechanical product and obtaining a performance expression of the degradation function stage; S35, using the maximum likelihood estimation method to realize parameter estimation of the performance degradation distribution and determine the relationship between the performance degradation amount and time.

6. The preventive maintenance-based mechanical product overhaul life determination method according to Claim 4, characterized by, In step S33, the uncertainty factors existing in the performance degradation process of the mechanical product are considered, and the mechanical product performance degradation equation is expressed as: ； In the formula: is a performance degradation equation, is a factor of uncertainty during degradation.

7. The preventive maintenance-based mechanical product overhaul life determination method according to Claim 1, characterized by, Step S4 includes the following sub-steps: S41, for the degradation data of the same performance parameter of the mechanical product, monitoring and collecting multiple sets of data in chronological order; S42, using the maximum likelihood estimation method to estimate the unknown parameters in the performance degradation model constructed in step S3, and determining the relationship between the performance degradation amount and time; S43, constructing a likelihood function; S44, calculate the log of the likelihood function in step S43 to solve the optimization problem; S45, solve the analytical solution of the function in step S44 to obtain the performance degradation model; S46, according to the performance degradation model obtained in step S45, it is determined that the transmission mechanical product needs to be overhauled when the performance is lower than a given performance threshold time, and the overhaul life of the mechanical product is determined in combination with the mechanical product reliability formula.

8. The preventive maintenance-based mechanical product overhaul life determination method according to Claim 7, characterized by, The method of constructing the likelihood function in step S43 is as follows: let The key performance parameters for the mechanical product to accept the performance degradation state detection are collected at each time The collected key performance parameters The degradation amount data, if there are The equipment simultaneously observes, each time has Data values When only one device is used for experimental observation, each observation point has one data, assuming that the data point It is the Observation value at time , and obeys a certain probability distribution , then the likelihood function is: ​ 。

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