A method for predicting reliability of reaction wheel under degradation process

Abstract

The application provides a reaction wheel reliability prediction method under degradation process, comprising collecting satellite reaction wheel bearing temperature history degradation data, and preprocessing the data; using Wiener process to establish a reaction wheel temperature performance degradation model and performing parameter estimation; establishing a reaction wheel reliability model and calculating the reaction wheel reliability; combining Kalman filtering algorithm and the reaction wheel temperature performance degradation model to establish a prediction model of the reaction wheel; and using the prediction model to predict the reliability of the reaction wheel. The application can more accurately and reliably predict the fault result, so as to achieve the purpose of satellite attitude control system fault prediction, promote the reliability and performance improvement of the satellite attitude control system; the application adds a random term in the Kalman filtering algorithm, fully gives play to the advantages of each model and algorithm, improves the accuracy and stability of the overall prediction, and improves the poor real-time problem in the reliability prediction method using the random degradation process.

Description

Technical Field

[0001] The present invention relates to the technical field of spacecraft fault prediction, and in particular to a method for predicting the reliability of a reaction wheel during a degradation process. Background Art

[0002] With the rapid development of aerospace, attitude control systems play a vital role in satellite systems, and reaction wheels are indispensable key actuators. As a key actuator in attitude control systems, reaction wheels undertake the important tasks of regulating moment of inertia and controlling angular momentum. However, because attitude control systems require long-term operation, they are one of the most susceptible systems in satellites to failure, and degradation of reaction wheels is a major factor in this failure.

[0003] Existing technologies primarily predict product reliability by measuring degradation data for specific performance parameters. For example, by establishing a temperature degradation model for a momentum wheel bearing and utilizing historical temperature data from similar momentum wheel bearings, the parameters in the degradation model were estimated. Based on this model, the sample path of the stochastic bearing temperature process was simulated, and the reliability of the momentum wheel was predicted. Results show that this method is effective in evaluating the reliability of momentum wheels. However, it relies on historical telemetry data and suffers from poor real-time performance. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for predicting the reliability of a reaction wheel under a degradation process. The present invention can provide more accurate and reliable fault prediction results, thereby achieving the purpose of fault prediction of the satellite attitude control system and promoting the reliability and performance improvement of the satellite attitude control system.

[0005] The technical solution of the present invention is: a method for predicting the reliability of a reaction wheel under a degradation process, comprising the following steps:

[0006] S1), collecting satellite reaction wheel bearing temperature historical degradation data and preprocessing the data;

[0007] S2), using the Wiener process to establish a reaction wheel temperature performance degradation model and perform parameter estimation;

[0008] S3), establishing a reaction wheel reliability model and calculating the reaction wheel reliability;

[0009] S4), establishing a prediction model for the reaction wheel by combining the Kalman filter algorithm and the reaction wheel temperature performance degradation model;

[0010] S5) using the prediction model to predict the reliability of the reaction wheel.

[0011] As preferred, in step S2), the bearing temperature of the reaction wheel is modeled with a Wiener process with drift to obtain a reaction wheel temperature performance degradation model:

[0012] Y(t) = a0 + a1t + σ B B(t) (1)

[0013] In the formula, Y(t) represents the reaction wheel temperature degradation amount at time t; a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the reaction wheel bearing temperature telemetry drift parameter, and σ B is a diffusion coefficient, and B(t) is a standard Wiener process.

[0014] As preferred, in step S2), the bearing temperature telemetry drift parameter a1 and the diffusion coefficient σ B in the reaction wheel temperature performance degradation model are different under different working conditions, and the unknown parameters a1 and σ B in the degradation model are estimated by combining the bearing temperature telemetry data under the working load and the maximum likelihood estimation method.

[0015] As preferred, in step S3), when the degradation trend Y(t) of the bearing temperature of the reaction wheel reaches a certain specific level, i.e., a failure threshold Xm, the reaction wheel fails, and thus the probability density function f(t|a0, a1, Xm, Y(t)) of the reaction wheel is obtained as:

[0016]

[0017] In the formula, a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the reaction wheel bearing temperature telemetry drift parameter, and σ B is a diffusion coefficient.

[0018] As preferred, in step S3), the reliability model of the reaction wheel at time t is:

[0019]

[0020] In the formula, P(X(t)) is the probability distribution of the temperature degradation amount X(t) at time t, and f(t|Xm) is the probability density function of the reaction wheel temperature degradation amount when the degradation amount threshold is Xm.

[0021] As preferred, in step S4), a prediction model of the reaction wheel is established by combining the Kalman filtering algorithm and the reaction wheel performance degradation model, and specifically includes the following steps:

[0022] S41), a state and an observation variable of an estimated parameter are established, and a discrete time linear state description equation is established:

[0023] u(k) = A(k)u(k) + E(k);

[0024] Z(k) = H(k)u(k) + W(k);

[0025] wherein u(k) is the state of the estimated parameter at time k, A(k) is the state transition matrix, E(k) and W(k) are zero mean and positive definite covariance matrices, E(k) and W(k) are subject to normal distribution of N(0, q) and N(0, r) respectively, and E(k) and W(k) are independent of each other; Z(k) is the observation vector at time k, which is the mean value of the degradation amount of the idler wheel within Δt time, and H(k) is the measurement matrix at time k;

[0026] S42), establishing a prediction equation and covariance of state estimation:

[0027] u(k+1|k) = A(k)u(k|k) + δ(k) + E(k);

[0028] G(k+1|k) = A(k)G(k|k) + E(k);

[0029] δ(k) = a1+ σ B (B(t+1) - B(t));

[0030] wherein u(k+1|k) is the state estimation value, representing the priori estimation of the degradation amount at time k+1; G(k|k) and G(k+1|k) are respectively the covariance matrix at time k and the priori estimation of the covariance matrix at time k+1 based on the state estimation at time k and the state transition matrix prediction; δ(k) is the random term;

[0031] S42), establishing a state update equation and error covariance update equation of the Kalman filtering algorithm in combination with the residual sequence and the Kalman gain:

[0032] u(k+1|k+1) = u(k+1|k) + K(k+1)γ(k+1)

[0033] = u(k+1|k) + K(k+1)[Z(k+1) - H(k+1)u(k+1|k)];

[0034] G(k+1|k+1) = [1 - K(k+1)H(k+1)]G(k+1|k);

[0035] wherein u(k+1|k+1) is the posteriori estimation of the state estimation value at time k+1; G(k+1|k+1) is the posteriori estimation of the covariance matrix at time k+1; and K(k+1) is the Kalman gain at time k+1;

[0036] γ(k+1) is the residual sequence at k+1 and k time; Z(k+1) is the observation value at k+1 time; H(k+1) is the measurement matrix at k+1 time;

[0037] S43), the reliability at k+1 time can be predicted after the priori estimation of the degradation state U(k+1|k) at k+1 time;

[0038]

[0039] In the formula, a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the reaction wheel bearing temperature telemetry value drift parameter, σ B is the diffusion coefficient; Xm is the failure threshold; U(k+1|k) is the state priori estimation value, which means the priori estimation of the temperature degradation amount in this Kalman filter.

[0040] As preferred, in step S5), the reliability of the reaction wheel is predicted by using the prediction model, and specifically includes the following steps:

[0041] S51), initialization parameters, including the failure threshold Xm, the initial value a0 of the reaction wheel bearing temperature telemetry, the reaction wheel bearing temperature telemetry value drift parameter a1, the diffusion coefficient σ B , the state initial value U(0|0), and the covariance initial value G(0|0);

[0042] S52), the mean value at the next time is predicted by using the state prediction equation U(k+1|k) = A(k)U(k|k) + δ(k) + E(k), and the optimal estimation value at k time is obtained as the prediction value at k+1 time;

[0043] S53), the probability density function at this time is calculated by using , and then the reliability calculation expression is used to calculate the predictable reliability P(X(t) < Xm) at k+1 time;

[0044] S54), n samples are collected in the k+1 th Δt time, and the matrix estimation value is calculated as the observation value Z(k+1) at k+1 time;

[0045] S55), the covariance G(k+1|k) = A(k)G(k|k) + E(k) is predicted;

[0046] S56), the gain K(k+1), the optimal state estimation value U(k+1|k+1) and the covariance G(k+1|k+1) are updated;

[0047] S56), k = k+1, and returning to step S52).

[0048] The beneficial effects of the present application are:​

[0049] 1. The present invention can provide more accurate and reliable fault prediction results, thereby achieving the purpose of fault prediction of satellite attitude control systems and promoting the reliability and performance improvement of satellite attitude control systems;

[0050] 2. The present invention uses the Wiener process to establish a reaction wheel temperature performance degradation model, and then uses maximum likelihood estimation to estimate the unknown parameters in the established model;

[0051] 3. The present invention adds a random term δ(k) containing the diffusion coefficient and the drift coefficient to the prediction equation of the state estimation in the Kalman filter algorithm, giving full play to the advantages of each model and algorithm, improving the accuracy and stability of the overall prediction, and improving the problem of poor real-time performance in the reliability prediction method using random degradation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the process of the present invention;

[0053] Figure 2 Schematic diagram of the process of reaction wheel reliability prediction of the present invention. DETAILED DESCRIPTION

[0054] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0055] like Figure 1 As shown, this embodiment provides a method for predicting the reliability of a reaction wheel during a degradation process, comprising the following steps:

[0056] S1) Collect historical degradation data on the reaction wheel bearing temperatures of multiple different models of satellites; and preprocess the historical degradation data. In this embodiment, considering that reaction wheel temperature degradation is a slow process, the preprocessing includes averaging the degradation amount of a reaction wheel within a day for which historical data has been collected, simplifying the degradation amount data within that day into a single data point, and performing parameter estimation on these simplified data points.

[0057] S2) Use the Wiener process to establish a reaction wheel temperature performance degradation model and perform parameter estimation; the details are as follows:

[0058] This embodiment uses a Wiener process with drift to model the degradation trend of the reaction wheel bearing temperature, and obtains a reaction wheel temperature performance degradation model:

[0059] Y(t)=a0+a1t+σ B B(t); (1)

[0060] Where Y(t) represents the temperature degradation of the reaction wheel at time t; a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the drift parameter of the reaction wheel bearing temperature telemetry value, and σ B is the diffusion coefficient, and B(t) is the standard Wiener process.

[0061] In addition, in this embodiment, under different working conditions, the bearing temperature telemetry drift parameter a1 and the diffusion coefficient σ in the reaction wheel temperature performance degradation model are B They are all different. The bearing temperature telemetry data under working load is combined with the maximum likelihood estimation method to estimate the unknown parameters a1 and σ in the degradation model. B Estimation includes the following steps:

[0062] S21) At a certain time interval, n+1 bearing temperature data are collected in sequence as (t0, y0).

[0063] (t1,y1),……,(t n ,y n ), where t i represents the sampling time, y i Indicates the corresponding time t i The sampled bearing temperature value, 0≤i≤n;

[0064] S22) Calculate the bearing temperature difference Δy according to the following formula i ,Right now:

[0065] Δy i =a1Δt i +σ B ΔB(t i ),i=1,2,3,...,n;(2)

[0066] Where a1 is the bearing temperature remote sensing drift parameter; σ B is the diffusion coefficient; Δt i =t i -t i-1 is the time difference; Δy i =y i -y i-1 is the bearing temperature difference; ΔB(t i )=B(t i )-B(t i-1 ) is the standard Wiener process difference, which conforms to the normal distribution;

[0067] S23), from the properties of the standard Wiener process, we know that ΔB(t i ):N(0,Δt i ),therefore,

[0068] And according to the property of the Wiener process with stable independent increments, the joint probability density function of the temperature increment is obtained as follows:

[0069] f(Δy1,Δy2,Δy3,...Δy n )=f(Δy1)f(Δy2)f(Δy3)...f(Δy n );(3)

[0070] The resulting likelihood function is:

[0071] L(a1,σ B )=f(Δy1)f(Δy2)f(Δy3)...f(Δy n ); (4)

[0072] Among them, the bearing temperature difference Δy i The probability density function of is expressed as:

[0073]

[0074] S24), the likelihood function L(a1,σ B ) and f(Δy i ) Take the logarithm and get:

[0075] lnL(a1,σ B )=lnf(Δy1)f(Δy2)f(Δy3)...f(Δy n )=lnf(Δy1)+lnf(Δy2)+....+lnf(Δy n ); (6)

[0077]

[0078] S25) and lnf(Δy i ) into lnL(a1,σ B )get:

[0079]

[0080] S26), a1 and σ of formula (8) B Taking the partial derivative we get:

[0081]

[0082] S27), let (9) and (10) be equal to 0, so that the parameter estimates are:

[0083]

[0084] Where, an estimated value of a drift parameter a1 of the bearing temperature telemetry; an estimated value of a diffusion coefficient σ B .

[0085] S3), establishing a reliability model of the reaction wheel and calculating the reliability of the reaction wheel; specifically as follows:

[0086] In this embodiment, the reaction wheel fails when the degradation trend Y(t) of the bearing temperature of the reaction wheel reaches a certain specific level, i.e., a failure threshold Xm, and thus the probability density function f(t|a0, a1, Xm, Y(t)) of the reaction wheel is obtained as:

[0087]

[0088] In the formula, a0 is an initial value of the bearing temperature telemetry of the reaction wheel, a1 is a drift parameter of the bearing temperature telemetry value of the reaction wheel, σ B is a diffusion coefficient;

[0089] Therefore, the reliability model of the reaction wheel at time t is:

[0090]

[0091] In the formula, P(X(t)) is the probability distribution of the temperature degradation amount X(t) at time t, and f(t|Xm) is the probability density function of the temperature degradation amount of the reaction wheel when the degradation threshold is Xm.

[0092] S4), a prediction model of the reaction wheel is established by combining the Kalman filtering algorithm and the temperature performance degradation model of the reaction wheel; specifically including the following steps:

[0093] S41), a state and an observation variable of an estimated parameter are established, and a discrete time linear state description equation is established:

[0094] U(k) = A(k)U(k) + E(k);

[0095] Z(k) = H(k)U(k) + W(k);

[0096] In the formula, U(k) is the state of the estimated parameter at time k, A(k) is a state transition matrix, E(k) and W(k) are zero-mean and positive definite covariance matrices, E(k) and W(k) respectively obey the normal distribution of N(0, q) and N(0, s), i.e., the Gaussian distribution observation noise and measurement noise of the error Q(k), S(k) are respectively normal distribution; E(k) and W(k) are independent of each other; Z(k) is an observation vector at time k, which is the mean value of the degradation amount of the reaction wheel within Δt time, H(k) is a measurement matrix at time k;

[0097] S42), a prediction equation and a covariance of state estimation are established:

[0098] U(k+1|k) = A(k)U(k|k) + δ(k) + E(k);

[0099] G(k+1|k) = A(k)G(k|k) + E(k);

[0100] δ(k) = a1+ σ B (B(t+1)-B(t));

[0101] wherein, U(k+1|k) is the state estimation value, indicating the priori estimation of the degradation amount at k+1 time; G(k|k), G(k+1|k) are respectively the covariance matrix at k time and the priori estimation of the covariance matrix at k+1 time based on the state estimation at k time and the state transition matrix prediction; δ(k) is a random term;

[0102] S42), combined with the residual sequence and the Kalman gain, the state update equation and the error covariance update equation of the Kalman filtering algorithm are established:

[0103] U(k+1|k+1) = U(k+1|k) + K(k+1)γ(k+1)

[0104] = U(k+1|k) + K(k+1)[Z(k+1) - H(k+1)U(k+1|k)];

[0105] G(k+1|k+1) = [1 - K(k+1)H(k+1)]G(k+1|k);

[0106] wherein, U(k+1|k+1) is the posteriori estimation of the state estimation value at k+1 time; G(k+1|k+1) is the posteriori estimation of the covariance matrix at k+1 time;

[0107] K(k+1) is the Kalman gain at k+1 time; γ(k+1) is the residual sequence at k+1 and k time; Z(k+1) is the observation value at k+1 time; H(k+1) is the measurement matrix at k+1 time;

[0108] wherein, the expression of the Kalman gain is:

[0109] K(k+1) = P(k+1|k)H T (k+1) + S -1 (k+1);

[0110] wherein, S(k+1) is the prediction error covariance;

[0111] S(k+1) = H(k+1)G(k+1|k)H T (k+1) + R(k+1);

[0112] The residual sequence is:

[0113] γ(k+1) = Z(k+1|k+1) - Z(k+1|k) = Z(k+1|k+1) - H(k+1|k)U(k+1|k);

[0114] In the formula, Z(k+1|k+1) is the actual observation value at k+1 time, Z(k+1|k) is the predicted observation value at k+1, H(k+1|k) is the measurement matrix, and U(k+1|k) is the state prior estimate value at k+1 time; R(k+1) is the error at k+1 time;

[0115] S43), when the prior estimate of the degradation state U(k+1|k) at k+1 time is obtained, the reliability at k+1 time can be predicted;

[0116]

[0117] In the formula, a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the reaction wheel bearing temperature telemetry value drift parameter, σ B is the diffusion coefficient; xm is the failure threshold; U(k+1|k) is the state prior estimate value, which means the prior estimate of the temperature degradation amount in this Kalman filter.

[0118] S5), the reliability of the reaction wheel is predicted by using the prediction model, as shown in the following formula: Figure 2 The specific steps include the following steps:

[0119] S51), the parameters are initialized, including the failure threshold xm, the initial value a0 of the reaction wheel bearing temperature telemetry, the reaction wheel bearing temperature telemetry value drift parameter a1, the diffusion coefficient σ B , the state initial value U(0|0), and G(0|0) is the covariance initial value;

[0120] S52), the mean value at the next time is predicted by using the state prediction equation U(k+1|k) = A(k)U(k|k) + δ(k) + E(k), and the optimal estimate value at k time is obtained for the predicted value at k+1 time;

[0121] S53), the probability density function of is used, and the reliability calculation expression of is used to calculate the predictable reliability P(X(t) < Xm) at k+1 time;

[0122] S54), n samples are collected within the k+1th Δt time, and the matrix estimate value is calculated as the observation value Z(k+1) at k+1 time;

[0123] S55), predict the covariance G(k+1|k) = A(k)G(k|k) + E(k) ;

[0124] S56), update the gain K(k+1), the optimal state estimate U(k+1|k+1) and the covariance G(k+1|k+1) ;

[0125] S56), k = k + 1, return to step S52).

[0126] The foregoing embodiments and descriptions in the specification are only to illustrate the principles and the best embodiments of the present application, and various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for predicting the reliability of a reaction wheel under degradation process, characterized in that: The following steps are involved: S1), collecting satellite reaction wheel bearing temperature historical degradation data and preprocessing the data; S2), using the Wiener process to establish a reaction wheel temperature performance degradation model and perform parameter estimation; S3), establishing a reaction wheel reliability model and calculating the reaction wheel reliability; S4) establishing a prediction model for the reaction wheel by combining the Kalman filter algorithm and the reaction wheel temperature performance degradation model; specifically comprising the following steps: S41) Establish the state of the estimated parameters and the observed variables and the discrete time linear state description equation: U(k)=A(k)U(k)+E(k); Z(k)=H(k)U(k)+W(k); Where U(k) is the state of the estimated parameters at time k, A(k) is the state transfer matrix, E(k) and W(k) are zero-mean and positive-definite covariance matrices, E(k) and W(k) obey the normal distribution of N(0,q) and N(0,r), respectively, and E(k) and W(k) are independent of each other; Z(k) is the observation vector at time k, which is the mean of the reaction wheel degradation amount within time Δt, and H(k) is the measurement matrix at time k; S42) Establish the prediction equation and covariance of state estimation: U(k+1|k)=A(k)U(k|k)+δ(k)+E(k); G(k+1|k)=A(k)G(k|k)+E(k); δ(k)=a1+σ B (B(t+1)-B(t)); Where U(k+1|k) is the state estimate, which represents the prior estimate of the degradation amount at time k+1; G(k|k) and G(k+1|k) are the covariance matrix at time k and the prior estimate of the covariance matrix at time k+1 predicted based on the state estimate and state transfer matrix at time k, respectively; δ(k) is a random term; S42) Combine the residual sequence and Kalman gain to establish the state update equation and error covariance update equation of the Kalman filter algorithm: U(k+1|k+1)=U(k+1|k)+K(k+1)γ(k+1) =U(k+1|k)+K(k+1)[Z(k+1)-H(k+1)U(k+1|k)]; G(k+1|k+1)=[1-K(k+1)H(k+1)]G(k+1|k); Where U(k+1|k+1) is the posterior estimate of the state estimate at time k+1; G(k+1|k+1) is the posterior estimate of the covariance matrix at time k+1; K(k+1) is the Kalman gain at time k+1; γ(k+1) is the residual sequence between k+1 and time k; Z(k+1) is the observation value at time k+1; H(k+1) is the measurement matrix at time k+1; S5) using the prediction model to predict the reliability of the reaction wheel.

2. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 1, characterized in that: In step S2), the degradation trend model of the reaction wheel bearing temperature is modeled using a Wiener process with drift to obtain a reaction wheel temperature performance degradation model: Y(t)=a0+a1t+σ B B(t); (1) Where Y(t) represents the temperature degradation of the reaction wheel at time t; a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the drift parameter of the reaction wheel bearing temperature telemetry value, and σ B is the diffusion coefficient, and B(t) is the standard Wiener process.

3. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 2, characterized in that: Step S2) Under different working conditions, the bearing temperature telemetry drift parameter a1 and the diffusion coefficient σ in the reaction wheel temperature performance degradation model are compared. B They are all different. The bearing temperature telemetry data under working load is combined with the maximum likelihood estimation method to estimate the unknown parameters a1 and σ in the degradation model. B Make an estimate.

4. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 3, wherein: Step S2), unknown parameters a1 and a B The estimated values ​​are: Where, is the estimated value of the bearing temperature telemetry drift parameter a1; is the diffusion coefficient σ B Estimated value of Δt i =t i -t i-1 is the time difference; Δy i =y i -y i-1 is the bearing temperature difference; n is the number of bearing temperature samples collected, t i represents the sampling time, y i Indicates the corresponding time t i The sampled bearing temperature value.

5. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 4, characterized in that: Step S2), the bearing temperature difference Δy i Expressed as: Δy i =a1Δt i +s B ΔB(t i ),i=1,2,3,...,n;(2) Where a1 is the bearing temperature remote sensing drift parameter; σ B is the diffusion coefficient; Δt i =t i -t i-1 is the time difference; ΔB(t i )=B(t i )-B(t i-1 ) is the standard Wiener process difference; According to the properties of the standard Wiener process, ΔB(t i )~N(0,Δt i ),therefore, And according to the property of the Wiener process with stable independent increments, the joint probability density function of the temperature increment is obtained as follows: f(Δy1,Δy2,Δy3,...Δy n )=f(Δy1)f(Δy2)f(Δy3)...f(Δy n ); (3) The resulting likelihood function is: L(a1,σ B )=f(Δy1)f(Δy2)f(Δy3)...f(Δy n ); (4) Among them, the bearing temperature difference Δy i The probability density function of is expressed as:

6. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 1, characterized in that: In step S3), when the degradation trend Y(t) of the reaction wheel bearing temperature reaches a certain level, namely, the failure threshold Xm, the reaction wheel fails. Therefore, the probability density function f(t|a0,a1,Xm,Y(t)) of the reaction wheel is obtained as follows: Where a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the drift parameter of the reaction wheel bearing temperature telemetry, σ B is the diffusion coefficient.

7. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 6, characterized in that: In step S3), the reliability model of the reaction wheel at time t is: Where P(X(t)) is the probability distribution of the temperature degradation X(t) at time t, and f(t|Xm) is the probability density function of the reaction wheel temperature degradation when the degradation threshold is Xm.

8. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 1, characterized in that: Step S4) also includes: S43), after the degradation state U(k+1|k) is estimated a priori at time k+1, the reliability at time k+1 can be predicted; Where a0 is the initial value of the reaction wheel bearing temperature telemetry, a1 is the drift parameter of the reaction wheel bearing temperature telemetry, σ B is the diffusion coefficient; Xm is the failure threshold; U(k+1|k) is the prior estimate of the state, which represents the prior estimate of the temperature degradation in this Kalman filter.

9. The method for predicting the reliability of a reaction wheel during a degradation process according to claim 8, characterized in that: In step S5), the reliability of the reaction wheel is predicted using the prediction model, which specifically includes the following steps: S51), initialization parameters, including failure threshold Xm, initial value a0 of reaction wheel bearing temperature telemetry, drift parameter a1 of reaction wheel bearing temperature telemetry, diffusion coefficient σ B , the initial state values ​​U(0|0) and G(0|0) are the initial covariance values; S52) Use the state prediction equation U(k+1|k)=A(k)U(k|k)+δ(k)+E(k) to predict the mean value at the next moment, and obtain the predicted value of the optimal estimate at time k for time k+1; S53) Utilization Calculate the probability density function at this time, and then use The calculation expression of reliability is used to calculate the predictable reliability P(X(t)<Xm) at the k+1th moment; S54), collecting n samples within the k+1th Δt moment, and calculating the moment estimate as the observation value Z(k+1) at the k+1th moment; S55), prediction covariance G(k+1|k)=A(k)G(k|k)+E(k); S56), update the gain K(k+1), the optimal state estimate U(k+1|k+1) and the covariance G(k+1|k+1); S56), k=k+1, return to step S52).

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