A life prediction method and system for a shared load system

Abstract

The present invention relates to a life prediction method and system for a co-carrying system. The method comprises using a sensor to obtain degradation trajectory data of each component in the co-carrying system during service; establishing a degradation model of the co-carrying system based on the Wiener process according to the degradation trajectory data; determining the total drift coefficient and total diffusion coefficient of each degradation stage according to the degradation trajectory data of each component; identifying the parameters of the degradation model according to the total drift coefficient and total diffusion coefficient of each degradation stage based on the degradation model; and determining the remaining life of the co-carrying system using the degradation model after identifying the parameters and the degradation trajectory data of each component. The present invention can improve the accuracy of the life prediction of the co-carrying system.

Description

Technical Field

[0001] The present invention relates to the field of reliability engineering analysis, and in particular to a life prediction method and system for a shared load system. Background Art

[0002] As a branch of the reliability field, the field of life prediction has experienced more than several decades of development and has achieved fruitful results in theoretical and practical applications. At present, the degradation modeling method is widely used by researchers to evaluate the health status of equipment during operation, so as to achieve the purpose of predicting the continuous normal working time (remaining service life) of the equipment. The main process of this method is to monitor the degradation data of the key components of the equipment based on the sensor, and then select a suitable degradation model based on this, and use maximum likelihood, Bayesian, expectation maximization and other algorithms to estimate the unknown parameters in the model, and then solve the analytical expression of the remaining life distribution of the selected model to obtain the remaining life of the monitored equipment.

[0003] In engineering practice, redundancy technology is widely used to improve system reliability. The core idea of ​​this technology is to redundant multiple key components to ensure the stable operation of the system. Even when one or several components fail, the system can still operate normally. At present, a large number of literatures assume that the internal components are independent of each other when considering the problem of remaining life prediction of redundant systems. However, in fact, most components in the system share the load. Such systems are also called shared load systems, such as lithium battery packs and cables in suspension bridges.

[0004] In a shared-carry system, each component will degrade over time during service, which will inevitably lead to a decline in the health performance of the shared-carry system, and eventually cause the system to fail. Unlike a system composed of multiple components in the conventional sense, in a shared-carry system, when a component fails, the degradation rate of the remaining surviving components will change accordingly, which also creates the problem of difficulty in estimating the life of the shared-carry system. Therefore, there is an urgent need for a method or system that can accurately predict the life of a shared-carry system to further effectively improve the reliability of the shared-carry system. Summary of the invention

[0005] The purpose of the present invention is to provide a life prediction method and system for a shared-carrying system, which can improve the accuracy of the life prediction of the shared-carrying system.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A life prediction method for a shared load system, comprising:

[0008] Use sensors to obtain degradation trajectory data of each component in the shared system during service;

[0009] According to the degradation trajectory data, establishing a degradation model of the co-carrying system based on a Wiener process;

[0010] The degradation trajectory data of each component is used to determine the total drift coefficient and total diffusion coefficient at each degradation stage;

[0011] According to the total drift coefficient and the total diffusion coefficient of each degradation stage, based on the degradation model, identifying parameters of the degradation model;

[0012] The remaining life of the shared load system is determined using the degradation model after identification of parameters and the degradation trajectory data of each component.

[0013] Optionally, establishing the degradation model of the co-carrying system based on the degradation trajectory data and the Wiener process specifically includes:

[0014] Using the formula Determine the degradation model of the shared load system;

[0015] Among them, λ and σ B are the intrinsic degradation rate and diffusion coefficient of the component, B(·) is the standard Brownian motion, x i is the initial degradation amount at the i-th stage, ρ i (θ) and is the degradation rate and diffusion coefficient due to the shared system load, t is the degradation stage, t≥0.

[0016] Optionally, the identifying the parameters of the degradation model based on the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage specifically includes:

[0017] Using formula M1: Determine sharing rule M1;

[0018] Using formula M2: Determine sharing rule M2;

[0019] Identifying the parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage, the sharing rule M1 and the sharing rule M2;

[0020] Where ρ0 is the initial value of the degradation rate caused by the load, is the initial value of the diffusion coefficient caused by the load, n is the total number of components in the system, and i is the number of failed components.

[0021] Optionally, the determining the remaining life of the shared-carrying system by using the degradation model after identifying the parameters and the degradation trajectory data of each component specifically includes:

[0022] Using the formula Determine the reliability of the shared load system;

[0023] Determine the cumulative distribution function of the shared load system using the formula F(t)=1-R(t);

[0024] Determine the remaining life of the shared load system according to the cumulative distribution function of the shared load system;

[0025] Among them, R(t) is the reliability of the shared load system, R i (t) is the reliability function of the i-th component at time t, F(t) is the cumulative distribution function of the shared load system, and k is a component that works normally.

[0026] A life prediction system for a shared load system, comprising:

[0027] A degradation trajectory data acquisition module is used to acquire degradation trajectory data of each component in the shared load system during service using sensors;

[0028] A degradation model building module, used to build a degradation model of the co-carrying system based on the Wiener process according to the degradation trajectory data;

[0029] A total drift coefficient and total diffusion coefficient determination module, used for determining the total drift coefficient and the total diffusion coefficient of each degradation stage by using the degradation trajectory data of each component;

[0030] A parameter identification module of the degradation model, used for identifying the parameters of the degradation model based on the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage;

[0031] The remaining life determination module of the shared-carrying system is used to determine the remaining life of the shared-carrying system by using the degradation model after identification parameters and the degradation trajectory data of each component.

[0032] Optionally, the degradation model building module specifically includes:

[0033] Degradation model building unit, used to use the formula Determine the degradation model of the shared load system;

[0034] Among them, λ and σ B are the intrinsic degradation rate and diffusion coefficient of the component, B(·) is the standard Brownian motion, x i is the initial degradation amount at the i-th stage, ρ i (θ) and is the degradation rate and diffusion coefficient due to the shared system load, t is the degradation stage, t≥0.

[0035] Optionally, the parameter identification module of the degradation model specifically includes:

[0036] The shared rule M1 determines a unit for utilizing formula M1: Determine sharing rule M1;

[0037] The shared rule M2 determines the unit, which is used to use the formula M2: Determine sharing rule M2;

[0038] A parameter identification unit of the degradation model, used for identifying the parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage, the sharing rule M1 and the sharing rule M2;

[0039] Where ρ0 is the initial value of the degradation rate caused by the load, is the initial value of the diffusion coefficient caused by the load, n is the total number of components in the system, and i is the number of failed components.

[0040] Optionally, the remaining life determination module of the shared-carrier system specifically includes:

[0041] The reliability determination unit of the shared load system is used to use the formula Determine the reliability of the shared load system;

[0042] A cumulative distribution function determination unit of the co-carrying system, used to determine the cumulative distribution function of the co-carrying system using the formula F(t)=1-R(t);

[0043] a remaining life determination unit of the shared-carrying system, used to determine the remaining life of the shared-carrying system according to a cumulative distribution function of the shared-carrying system;

[0044] Among them, R(t) is the reliability of the shared load system, R i (t) is the reliability function of the i-th component at time t, F(t) is the cumulative distribution function of the shared load system, and k is a component that works normally.

[0045] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0046] The present invention provides a life prediction method and system for a shared-carrying system, which utilize sensors to obtain degradation trajectory data of each component in the shared-carrying system during service, and establish a degradation model of the shared-carrying system based on the Wiener process, that is, using the random process modeling method to scientifically and reasonably predict the life of the entire system, thereby ensuring the accuracy of life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A schematic flow chart of a life prediction method for a co-carrying system provided by the present invention;

[0049] Figure 2 It is a schematic diagram of the degradation trajectory of the shared load system;

[0050] Figure 3 Simulation degradation trajectory for the co-load system;

[0051] Figure 4 is the life distribution histogram under the same degradation rate and different diffusion coefficients;

[0052] Figure 5 for , a comparison chart between the method of the present invention and the method of Du et al.;

[0053] Figure 6 for , a comparison chart between the method of the present invention and the method of Du et al.;

[0054] Figure 7 for Comparison chart of reliability functions when no component fails in the system;

[0055] Figure 8 for Comparison of reliability functions when only one component fails in the system;

[0056] Fig. 9 This is a schematic diagram of the structure of a life prediction system for a shared-load system provided by the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The purpose of the present invention is to provide a life prediction method and system for a shared-carrying system, which can improve the accuracy of the life prediction of the shared-carrying system.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Figure 1 The following is a flow chart of a life prediction method for a co-carrying system provided by the present invention. Figure 1As shown, the present invention provides a life prediction method for a shared load system, comprising:

[0061] S101, using sensors to obtain degradation trajectory data of each component in the shared load system during service;

[0062] S102, establishing a degradation model of the co-carrying system based on the Wiener process according to the degradation trajectory data;

[0063] S102 specifically includes:

[0064] Using the formula Determine the degradation model of the shared load system;

[0065] Among them, λ and σ B are the intrinsic degradation rate and diffusion coefficient of the component, B(·) is the standard Brownian motion, x i is the initial degradation amount at the i-th stage, ρ i (θ) and is the degradation rate and diffusion coefficient due to the shared system load, t is the degradation stage, t≥0.

[0066] S103, determining a total drift coefficient and a total diffusion coefficient at each degradation stage using the degradation trajectory data of each component;

[0067] S104, identifying parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage and based on the degradation model;

[0068] S104 specifically includes:

[0069] Using formula M1: Determine sharing rule M1;

[0070] Using formula M2: Determine sharing rule M2;

[0071] Identifying the parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage, the sharing rule M1 and the sharing rule M2;

[0072] Where ρ0 is the initial value of the degradation rate caused by the load, is the initial value of the diffusion coefficient caused by the load, n is the total number of components in the system, and i is the number of failed components.

[0073] Rule M1 is to evenly distribute the load of the failed component to the remaining surviving components, and rule M2 is to directly increase the load borne by the failed component to each remaining surviving component.

[0074] Based on the above analysis, the parameters to be identified are Where (·)′ represents the transpose of a vector. In general, it is assumed that a shared load system consists of n components, and the monitoring data of all components are independent of each other, defined as X = (X1, X2, ..., X n )′. Since the degradation rate and diffusion coefficient of the remaining surviving components will change accordingly when a component fails in the shared load system, the degradation of each component has the characteristics of multiple stages. Let the number of measurements in the i-th stage be m i ,and Due to the independent increment characteristics of the Wiener process, the degradation state of the component at the i-th stage has the following properties:

[0075]

[0076] Among them, N(0,1) represents the standard normal distribution, so the overall likelihood function can be written as follows:

[0077]

[0078] Among them, μ i =λ+ρ i (θ), Δx i,k,j =x i,k,j -x i,k,j-1 ,Δt=t i,j -t i,j-1 ,η m represents the total number of stages. In order to simplify the difficulty of solving the problem, it is assumed that the time sampling intervals are equal, which is also generally satisfied in engineering practice. Then, by respectively adjusting the parameter μ i and σ i Taking partial derivatives, we get:

[0079]

[0080]

[0081] By setting equations (3) and (4) equal to 0, we can obtain μ for each stage: i and σ i As shown below:

[0082]

[0083] In view of this, based on the historical degradation data collected by the sensor, the total drift coefficient μ for each degradation stage is obtained: i and the total diffusion coefficient σ i Next, the present invention mainly introduces the model identification method of the shared rule M1, and the rule M2 can be obtained similarly. Therefore, based on the rule M1, μ i and σ iis equivalent to the following:

[0084]

[0085] It can be clearly seen that equation (6) is two overdetermined equations, and the number of unknown variables is significantly less than the number of equations. Therefore, we will use the least squares method to find its optimal solution. Define A as the coefficient matrix:

[0086]

[0087] Then, equation (6) can be transformed into the following:

[0088]

[0089] Where μ=(μ1,μ2,...μ n )′,σ=(σ1,σ2,...σ n )′. Then multiply both sides of the equation by Α′:

[0090]

[0091] Therefore, according to equation (9), all unknown parameters of the model have been solved.

[0092] S105 , determining the remaining life of the shared-carrying system by using the degradation model after identifying the parameters and the degradation trajectory data of each component.

[0093] S105 specifically includes:

[0094] Using the formula Determine the reliability of the shared load system;

[0095] Determine the cumulative distribution function of the shared load system using the formula F(t)=1-R(t);

[0096] Determine the remaining life of the shared load system according to the cumulative distribution function of the shared load system;

[0097] Among them, R(t) is the reliability of the shared load system, R i (t) is the reliability function of the i-th component at time t, F(t) is the cumulative distribution function of the shared load system, and k is a component that works normally.

[0098] The degradation trajectory of each component in the shared load system is as follows: Figure 2 As shown in Figure 2, it can be seen that its degradation trajectory has obvious stage characteristics, where τ i represents the failure time of the i-th component, d i is defined as the time interval between the failure of the i-th component and the failure of the i+1-th component, x iis the initial degradation value of the i-th degradation stage. It is obvious that the above three parameters are all random variables.

[0099] The life of a device is usually defined as the first time that the device degradation process {X(t), t≥0} reaches the preset failure threshold ω, the device is considered to be unable to continue to work normally, that is, failure. i Defined as:

[0100] L i :=inf{t:X(t)≥ω|X(0)<ω} (10)

[0101] For a shared load system consisting of n components, in order to ensure the normal operation of the system, at least k components in the system must be able to work normally. Therefore, the life of the shared load system is defined as follows:

[0102] T:=inf{t:N(t)≥n-k+1|t≥0} (11)

[0103] First, consider the failure of a single component. For the i-th stage, at τ i The remaining life of the components that have not failed at the time (l κ ) distribution analysis expression is as follows:

[0104]

[0105] Therefore, the corresponding cumulative distribution function (CDF) is as follows:

[0106]

[0107] where Φ(·) represents the CDF of the standard normal distribution, and ω i =ω-x i , l κ =t-τ i The reliability function at this stage can be expressed as follows:

[0108] Ψ i (l κ |x i )=1-H i (l κ |x i ) (14)

[0109] Then, l κ =t-τ i Substituting into equations (12) to (14), we can obtain h i (τ i+1 |τi ), H i (τ i+1 |τ i ), i (τ i+1 |τ i ). In addition, we need to know the initial degradation amount x at each stage i Based on the above analysis, we know that the initial degradation amount x i is a random variable, and x i <ω. Therefore, x i The probability distribution of can be obtained according to the state transition probability of the Wiener process.

[0110]

[0111] Where Δτ i+1 =τ i+1 -τ i , μ i =λ+ρ i (θ),

[0112] Therefore, the reliability function of a component can be obtained as follows:

[0113]

[0114] Next, the life distribution of the entire shared-load system will be derived. However, the life distribution of the shared-load system under the first arrival time cannot be directly obtained because the order in which the components fail is unclear during the degradation process. For n components, there are many combinations of failure orders. In view of this, the life distribution of the shared-load system can be obtained by solving the inverse problem, that is, first solving the reliability function of the system at the current moment. The reliability of the shared-load system is equal to the sum of the reliability functions of each stage of the system. Therefore, the probability of failure of i components at time t is as follows:

[0115] P i (t) = Pr (N (t) = i) = F i (t)-F i+1 (t) (17)

[0116] Where F i (t) represents the CDF of the failure time of the i-th component, and its corresponding probability density function is f i (t), F i (t) can be obtained according to the relationship between CDF and reliability function:

[0117] F i (t) = 1-R i (t);

[0118] Where R i (t) is the reliability function of the ith component at time t.

[0119] Furthermore, the probability that no component of the system fails at time t is:

[0120] R0(t)=[Ψ0(t)] n ;

[0121] When only one component of the system fails at time t, the probability that the remaining components do not fail is as follows:

[0122]

[0123] In view of this, if ni (≥2) components fail before time t, the probability that the remaining components do not fail is as follows:

[0124]

[0125] The reliability of the system can be calculated by the following formula:

[0126]

[0127] Then, CDFF(t)=1-R(t) and probability density function f(t) of the co-load system life T can be further obtained. It should be noted that R(t) does not have an accurate analytical expression, but can be calculated by numerical simulation.

[0128] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail by using a simulation verification method. First, the parameter estimation method is simulated and verified. Based on rule M1, the Euler discrete method is used to generate Figure 3 The four degenerate trajectories shown in Figure 1 are expressed as follows:

[0129]

[0130] Wherein Y is a random variable and obeys the standard normal distribution, Δt is the discretization step size, which is set to 0.01 in the present invention. The real parameters and estimated parameter values ​​of the simulation parameter setting are shown in Table 1.

[0131] Table 1

[0132]

[0133] It can be seen from Table 1 that the parameter estimation method proposed in the present invention can estimate the model parameters more accurately. Further, in order to illustrate the advantages of the method proposed in the present invention, our method is compared with the method of Du et al. using the Monte Carlo method. The flow of the simulation algorithm is as follows:

[0134] Step 1: Initialize parameters λ, ρ0, σ B , N,k,Δt,M, where M is the total number of co-load systems, and let m=1.

[0135] Step 2: If m<M, based on the formula Generate N degenerate trajectories, otherwise jump to step 4.

[0136] Step 3: Record the time when the first n-k+1 degradation trajectories reach the failure threshold for the first time, i.e. Then make As the failure time of the system (T (m) ), and let m=m+1.

[0137] Step 4: Based on time T (1) ,…,T (M) Draw a histogram of the system lifetime distribution.

[0138] In addition, if Figure 4 The figure shows the lifetime distribution histogram of the system under different diffusion coefficients, from which it can be seen that the difference in diffusion coefficients has a strong influence on lifetime prediction. Therefore, the present invention will compare the method of Du et al. with that of Du et al. under different diffusion coefficients.

[0139] The present invention takes the 3-out-of-4 system as an example, that is, the simulated shared load system consists of 4 components. When 2 components fail, the entire system is considered to have failed. The parameter values ​​are set as shown in Table 2. The comparison results are as follows: Figure 5 , Figure 6 As shown in the figure, it can be seen that as the diffusion coefficient increases, the prediction results of the method of the present invention are more accurate than those of the method of Du et al. In addition, in order to further compare with the method of Du et al., we compared The probability of no component failure and the probability of only one component failure in the case are compared as follows: Figure 7 , Figure 8 It can be found that as the number of component failures increases, the errors generated by the method of Du et al. and the method of the present invention gradually increase, which further illustrates the advantages of the method of the present invention.

[0140] Table 2

[0141]

[0142] Fig. 9 This is a schematic diagram of the life prediction system structure of a co-carrying system provided by the present invention, such as Fig. 9 As shown, the present invention provides a life prediction system for a shared load system, comprising:

[0143] The degradation trajectory data acquisition module 901 is used to acquire the degradation trajectory data of each component in the shared load system during service using sensors;

[0144] A degradation model building module 902 is used to build a degradation model of the co-carrying system based on the Wiener process according to the degradation trajectory data;

[0145] A total drift coefficient and total diffusion coefficient determination module 903 is used to determine the total drift coefficient and total diffusion coefficient of each degradation stage using the degradation trajectory data of each component;

[0146] A degradation model parameter identification module 904, used to identify the parameters of the degradation model based on the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage;

[0147] The remaining life determination module 905 of the shared-carrying system is used to determine the remaining life of the shared-carrying system by using the degradation model after identification of parameters and the degradation trajectory data of each component.

[0148] The degradation model building module 902 specifically includes:

[0149] Degradation model building unit, used to use the formula Determine the degradation model of the shared load system;

[0150] Among them, λ and σ B are the intrinsic degradation rate and diffusion coefficient of the component, B(·) is the standard Brownian motion, x i is the initial degradation amount at the i-th stage, ρ i (θ) and is the degradation rate and diffusion coefficient due to the shared system load, t is the degradation stage, t≥0.

[0151] The parameter identification module 904 of the degradation model specifically includes:

[0152] The shared rule M1 determines a unit for utilizing formula M1: Determine sharing rule M1;

[0153] The shared rule M2 determines the unit, which is used to use the formula M2: Determine sharing rule M2;

[0154] A parameter identification unit of the degradation model, used for identifying the parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage, the sharing rule M1 and the sharing rule M2;

[0155] Where ρ0 is the initial value of the degradation rate caused by the load, is the initial value of the diffusion coefficient caused by the load, n is the total number of components in the system, and i is the number of failed components.

[0156] The remaining life determination module 905 of the shared load system specifically includes:

[0157] The reliability determination unit of the shared load system is used to use the formula Determine the reliability of the shared load system;

[0158] A cumulative distribution function determination unit of the co-carrying system, used to determine the cumulative distribution function of the co-carrying system using the formula F(t)=1-R(t);

[0159] a remaining life determination unit of the shared-carrying system, used to determine the remaining life of the shared-carrying system according to a cumulative distribution function of the shared-carrying system;

[0160] Among them, R(t) is the reliability of the shared load system, R i (t) is the reliability function of the i-th component at time t, F(t) is the cumulative distribution function of the shared load system, and k is a component that works normally.

[0161] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0162] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A life prediction method for a shared load system, characterized in that: include: Use sensors to obtain degradation trajectory data of each component in the shared system during service; According to the degradation trajectory data, establishing a degradation model of the co-carrying system based on a Wiener process; The degradation trajectory data of each component is used to determine the total drift coefficient and total diffusion coefficient at each degradation stage; According to the total drift coefficient and the total diffusion coefficient of each degradation stage, based on the degradation model, identifying parameters of the degradation model; Determine the remaining life of the shared load system using the degradation model after identification of parameters and the degradation trajectory data of each component; The step of establishing the degradation model of the shared-carrying system based on the Wiener process according to the degradation trajectory data specifically includes: Using the formula Determine the degradation model of the shared load system; Among them, λ and σ B are the intrinsic degradation rate and diffusion coefficient of the component, B(·) is the standard Brownian motion, x i is the initial degradation amount at the i-th stage, ρ i (θ) and is the degradation rate and diffusion coefficient due to the load of the shared system, t is the degradation stage, t≥0; The step of identifying the parameters of the degradation model based on the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage specifically includes: Using formula M1: Determine sharing rule M1; Using formula M2: Determine sharing rule M2; Identifying the parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage, the sharing rule M1 and the sharing rule M2; Where ρ0 is the initial value of the degradation rate caused by the load, is the initial value of the diffusion coefficient caused by the load, n is the total number of components in the system, and i is the number of failed components.

2. The life prediction method of a co-carrying system according to claim 1, characterized in that: The method of determining the remaining life of the shared load system by using the degradation model after identifying the parameters and the degradation trajectory data of each component specifically includes: Using the formula Determine the reliability of the shared load system; Determine the cumulative distribution function of the shared load system using the formula F(t)=1-R(t); Determine the remaining life of the shared load system according to the cumulative distribution function of the shared load system; Among them, R(t) is the reliability of the shared load system, R i (t) is the reliability function of the i-th component at time t, F(t) is the cumulative distribution function of the shared load system, and k is a component that works normally.

3. A life prediction system for a shared load system, characterized in that: include: A degradation trajectory data acquisition module is used to acquire degradation trajectory data of each component in the shared load system during service using sensors; A degradation model building module, used to build a degradation model of the co-carrying system based on the Wiener process according to the degradation trajectory data; A total drift coefficient and total diffusion coefficient determination module, used for determining the total drift coefficient and the total diffusion coefficient of each degradation stage by using the degradation trajectory data of each component; A parameter identification module of the degradation model, used for identifying the parameters of the degradation model based on the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage; A module for determining the remaining life of the shared-carrying system, for determining the remaining life of the shared-carrying system by using a degradation model after identifying parameters and degradation trajectory data of each component; The degradation model establishment module specifically includes: Degradation model building unit, used to use the formula Determine the degradation model of the shared load system; Among them, λ and σ B are the intrinsic degradation rate and diffusion coefficient of the component, B(·) is the standard Brownian motion, x i is the initial degradation amount at the i-th stage, ρ i (θ) and is the degradation rate and diffusion coefficient due to the load of the shared system, t is the degradation stage, t≥0; The parameter identification module of the degradation model specifically includes: The shared rule M1 determines a unit for utilizing formula M1: Determine sharing rule M1; Shared rule M2 determines the unit for using the formula Determine sharing rule M2; A parameter identification unit of the degradation model, used for identifying the parameters of the degradation model according to the total drift coefficient and the total diffusion coefficient of each degradation stage, the sharing rule M1 and the sharing rule M2; Where ρ0 is the initial value of the degradation rate caused by the load, is the initial value of the diffusion coefficient caused by the load, n is the total number of components in the system, and i is the number of failed components.

4. The life prediction system of a co-carrying system according to claim 3, characterized in that: The remaining life determination module of the shared load system specifically includes: The reliability determination unit of the shared load system is used to use the formula Determine the reliability of the shared load system; A cumulative distribution function determination unit of the co-carrying system, used to determine the cumulative distribution function of the co-carrying system using the formula F(t)=1-R(t); a remaining life determination unit of the shared-carrying system, used to determine the remaining life of the shared-carrying system according to a cumulative distribution function of the shared-carrying system; Among them, R(t) is the reliability of the shared load system, R i (t) is the reliability function of the i-th component at time t, F(t) is the cumulative distribution function of the shared load system, and k is a component that works normally.

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