CN101859146B - Satellite fault prediction method based on predictive filtering and empirical mode decomposition - Google Patents
Satellite fault prediction method based on predictive filtering and empirical mode decomposition Download PDFInfo
- Publication number
- CN101859146B CN101859146B CN2010102287440A CN201010228744A CN101859146B CN 101859146 B CN101859146 B CN 101859146B CN 2010102287440 A CN2010102287440 A CN 2010102287440A CN 201010228744 A CN201010228744 A CN 201010228744A CN 101859146 B CN101859146 B CN 101859146B
- Authority
- CN
- China
- Prior art keywords
- centerdot
- function
- satellite
- delta
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 238000001914 filtration Methods 0.000 title claims abstract description 36
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 35
- 238000001514 detection method Methods 0.000 claims abstract description 11
- 238000012731 temporal analysis Methods 0.000 claims abstract description 9
- 238000000700 time series analysis Methods 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims description 20
- 239000011159 matrix material Substances 0.000 claims description 16
- 238000012216 screening Methods 0.000 claims description 15
- 230000004083 survival effect Effects 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 5
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 3
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 claims description 2
- 238000003745 diagnosis Methods 0.000 abstract description 11
- 239000007787 solid Substances 0.000 description 4
- 230000035772 mutation Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000005309 stochastic process Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000005654 stationary process Effects 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a satellite fault prediction method based on predictive filtering and empirical mode decomposition, which belongs to the method for safe operation and fault prediction of the space satellite, and solves the problems of serious noise influence and inaccurate prediction of the fault trend in the traditional satellite fault prediction and diagnosis method. The method comprises the following specific steps: 1, estimating errors of the satellite control system through predictive filtering according to the dynamic relationship of the nonlinear attitude of the satellite, thereby obtaining a system model error; 2, carrying out empirical mode decomposition for the system model error obtained in Step 1 to obtain the IMF component and the residual component of the n-order intrinsic mode function; and 3, establishing a fault trend model related to the residual component obtained in Step 2 through time series analysis, thereby completing the prediction and detection of minor and slow-variation faults. The invention can be applied in the field of fault diagnosis of the satellite attitude control system.
Description
Technical field
The present invention relates to the safe operation and the failure prediction method of Aerospace Satellite, be specifically related to a kind of satellite attitude control system failure prediction method based on predictive filtering and empirical modal decomposition.
Background technology
Attitude of satellite control is to obtain and keep satellite in aspect-stabilized method and process.Present high precision three-axis attitude stabilization satellite during operate as normal, generally adopts the main topworks of momenttum wheel as attitude control system in orbit, realizes control to the attitude of satellite by the momentum-exchange between momenttum wheel and the satellite.The reliability of momenttum wheel will directly influence the feasibility and the reliability of the attitude control of whole satellite.The current method that mainly is based on hardware redundancy for the fault diagnosis and the reconstruct of satellite attitude control system, this satellite posture control system for complexity is far from being enough.
Safe operation and failure prediction to satellite adopts the predictive filtering method at present, the predictive filtering method is a kind of method of estimation that is applicable to the nonlinear system with unknown input or model error, the non-linear Proctor Central that the viewpoint that its thought source is controlled in Lu from system proposes, on this basis, Crassidis and Markley have proposed a kind of new Real-Time Filtering algorithm according to the least model error criterion---and predictive filtering (Predictive Filtering, PF).Predictive filtering is exported the model error of real-time estimating system by comparing and measuring output and prediction, thereby the correction wave filter state is realized the estimation to time of day.Because predictive filter has the ability of estimation model sum of errors system state simultaneously, domestic Li Ji, a big vast battle-axe used in ancient China have been incorporated into fault diagnosis field by fault being considered as a kind of special model error with the predictive filtering method.The noise that exists in the estimated result of predictive filtering can influence diagnosis performance, can adopt the low-pass filter method to suppress high frequency noise, but because characteristic the unknown of noise, the method that adopts the low-pass filter method to suppress noise often lost efficacy, cause and to make accurate prediction to fault, be unfavorable for further research ensuing fault detection and diagnosis.If can accurately predict, before taking place, fault, can improve the feasibility and the reliability of the attitude control of whole satellite to the estimation of time of day to satellite failure.
Summary of the invention
There is serious, the problem that can't accurately predict fault trend affected by noise in the present invention in order to solve the conventional satellite failure prediction method, and a kind of satellite failure Forecasting Methodology of decomposing based on predictive filtering and empirical modal is provided.
Detailed process of the present invention is as follows:
Step 1: utilize satellite nonlinear attitude kinetics relation, adopt the method for predictive filtering that the satellite control system error is estimated, obtain system model error term;
Step 2: the system model error term that step 1 obtains is carried out the empirical modal decomposition, E rank eigenmode state function IMF component and residual component before obtaining;
Step 3: utilize Time series analysis method to set up the model of the fault trend of the residual component that obtains about step 2, finish the forecast and the detection of small gentle change fault.
The present invention is according to the nonlinear attitude kinetics relation of satellite, fault amount and model uncertainty sum are considered as model error, compare with low-pass filter disposal route in the past, can eliminate The noise effectively, utilize predictive filtering method estimation model error, the model error estimated value that predictive filtering obtains is carried out the empirical modal decomposition, obtain its several in solid model attitude component and trend component, improved precision of prediction, utilize Time series analysis method that the trend of fault is predicted, can be to gradual early stage, small fault forecasts accurately and detects that method is concisely effective.The fault diagnosis field that is used for satellite attitude control system.
Description of drawings
Fig. 1 is the process flow diagram based on the satellite failure Forecasting Methodology of predictive filtering and empirical modal decomposition; Fig. 2 is embodiment three process flow diagrams; Predictive filtering estimated result when Fig. 3 for pitch axis gradual fault takes place; Fig. 4 is the empirical modal decomposition result of yaw axis (non-fault); Fig. 5 is the empirical modal decomposition result of pitch axis (gradual fault); Fig. 6 is the empirical modal decomposition result of wobble shaft (non-fault); Predicting the outcome of gradual fault takes place for pitch axis in Fig. 7; Fig. 8 predicts the outcome for yaw axis generation micromutation fault.
Embodiment
Embodiment one: a kind of satellite failure Forecasting Methodology based on predictive filtering and empirical modal decomposition, detailed process is as follows:
Step 1: utilize satellite nonlinear attitude kinetics relation, adopt the method for predictive filtering that the satellite control system error is estimated, obtain system model error term;
Step 2: the system model error term that step 1 obtains is carried out the empirical modal decomposition, E rank eigenmode state function IMF component and residual component before obtaining;
Step 3: utilize Time series analysis method to set up the model of the fault trend of the residual component that obtains about step 2, finish the forecast and the detection of small gentle change fault.
Empirical mode decomposition method (Empirical Mode Decomposition, EMD) be a kind of new signal decomposition method that proposed by scholar Huang E in 1998, can utilize the variation of signal internal time yardstick to do the parsing of energy and frequency, with signal be launched into solid model state function in several (Intrinsic Mode Function, IMF).Be different from and use the classic method of solid form window for the boundary basis function, the basis function of EMD extracts from signal and obtains, and promptly uses IMF to do substrate.And IMF must satisfy following condition:
1) in whole function, the number of extreme point equates with the number that passes through zero point or differs 1;
2) be zero by the defined envelope local mean value of local extremum envelope at any time.
Wherein, in first condition and the traditional gaussian stationary process narrow frequency range require similar.Second condition is a new idea: globality is required to change into the locality requirement, make instantaneous frequency can not cause unnecessary rocking because of the existence of asymmetric waveform.The EMD and the HHT that rely on these two conditions to make up are considered to handle forcefully adaptive approach non-linear, non-stationary signal, be in recent years to based on the linearity of Fourier transform and the important breakthrough of stable state analysis of spectrum, and obtained using widely.Utilize the empirical mode decomposition method can be adaptive with the composition of signal decomposition for different instantaneous frequencys, thus the noise contribution adaptively in the erasure signal.
The forecast model of setting up fault trend is the main contents of fault forecast.Autoregressive model (Autoregressive model, AR model) is a kind of common model in the time series analysis.Advantages such as it is simple that the AR model has modeling, and calculated amount is little.The above AR model of single order is applicable to stochastic process stably, and single order AR model has m>different characteristic of 1 model wherein with AR (m), and as the special case of AR model, single order AR model can be predicted nonstationary random process.
Basic thought of the present invention is major part or a kind of important component that fault is considered as the system model error, utilize the model error item of satellite nonlinear attitude kinetics relation design predictive filter estimating system, utilize empirical mode decomposition method that estimated result is handled then, so that can diagnose the small gentle change initial failure of satellite attitude control system.
The objective of the invention is to be achieved through the following technical solutions: the model error item that utilizes satellite nonlinear attitude kinetics relation design predictive filter estimating system, estimated result is carried out empirical modal to be decomposed, obtain some rank IMF and trend term, utilize Time series analysis method to set up the model of trend term, carry out the forecast and the detection of small gentle change fault.
The present invention compared with prior art has following advantage:
1) failure prediction method proposed by the invention utilizes empirical mode decomposition method, compares with the low-pass filter disposal route, can more effectively eliminate The noise, extracts the tendency information of fault amount.
2) fault failure prediction method proposed by the invention is utilized empirical mode decomposition method, and signal is divided into the residual component of several IMF and non-stationary, sets up the AR model on this basis, can improve precision of prediction.
3) the present invention forecasts the fault amount and detects, and directly compares based on the diagnostic method of threshold value, can improve the responsive ability for gradual early stage, small fault.
Embodiment two, present embodiment are further specifying embodiment one, utilize satellite nonlinear attitude kinetics relation in the step 1, adopt the method for predictive filtering the satellite control system error to be estimated the process that obtains system model error term is:
Set the model error of satellite control system and be made up of satellite executing mechanism fault and model uncertainty, the measurement equation of predictive filtering system and real system is respectively:
State equation:
The predictive filtering equation is:
X (t) ∈ R wherein
sBe state vector,
Be the estimated value of state vector, f ∈ R
sBe function of state that can be little,
Be known model error distribution matrix,
Be the measurement functions vector,
Be the estimation of unknown error, the measurement of real system is output as discrete form,
Be shown in t
kMeasured value constantly, v
k∈ R
mBe the measurement noise, and set v
kBe that average is zero, covariance matrix is Q ∈ R
M * mWhite Gaussian noise;
Constantly will measure function at t+ Δ t and carry out Taylor expansion, obtain:
Δ t=t wherein
K+1-t
kIt is the sampling period; This sampling period is normal value, y (t+ Δ t)=y
K+1, matrix
I element be:
P wherein
iDifferential exponent number when occurring d (t) in the Taylor expansion first,
Be c
iK rank Lie derivative;
Matrix Λ (Δ t) is a diagonal matrix, and its diagonal element is:
Matrix
The element that its i is capable is:
Get performance index function:
W ∈ R wherein
Q * qBe positive semidefinite weighting matrix, adopt the gradient optimizing algorithm that performance index are optimized, the estimated value that obtains the model error item is:
Because the estimated value of model error item
Contain bigger noise component, can not be directly used in fault diagnosis, be necessary to carry out next step and handle.
Embodiment three, present embodiment are to the further specifying of concrete enforcement one, and the process of E rank eigenmode state function IMF component and residual component was before step 2 obtained:
The estimated value of initialization system model error item is
Time t=1,2 ..., N,
Step a, IMF decomposable process initialization: n=1, and satisfy relational expression
Set up, wherein r
N-1(t) be remaining residual error function after (n-1) inferior decomposition;
Step b, screening process initialization: k=1, and satisfy relational expression h
N (k-1)(t)=r
N-1(t) set up, wherein h
N (k-1)(t) be through the survival function after the k-1 time screening during the n time IMF decomposes;
Step c, obtain the estimated value of system model error term according to screening sequence
Survival function h after screening through the k time in the remaining residual error function of decomposing through the n time intrinsic mode function
Nk(t);
Steps d, the survival function h that adopts the judgement of standard deviation criterion to obtain
Nk(t) whether satisfy the condition of eigenmode state function, promptly
Whether less than threshold value T, 0.2≤T≤0.3;
Judged result is for being, execution in step e, and judged result is not for, and then k=k+1 returns execution in step c,
Step e, the n time intrinsic mode function IMF component c of acquisition
n(t)=h
Nk(t);
Step f, obtain the estimated value of system model error term
Remaining residual error function through the n time intrinsic mode function decomposition
Step g, make n=n+1, return execution in step b, E rank eigenmode state function IMF component and residual component before satisfying n=E and obtaining.
Embodiment four, present embodiment are that step c obtains the estimated value of system model error term according to screening sequence to the further specifying of concrete enforcement three
Survival function h after screening through the k time in the remaining residual error function of decomposing through the n time intrinsic mode function
Nk(t) process is:
Step c1, the survival function h after utilizing cubic spline function to obtain in the residue trend function that system model error term d (t) decomposes through the n time intrinsic mode function to screen through the k-1 time
N (k-1)(t) upper and lower enveloping curve;
Step c2, the described survival function h of calculating
N (k-1)(t) upper and lower enveloping curve is at time t=1, and 2 ..., the average in the N
Step c3, obtain the estimated value of system model error term
Survival function after screening through the k time in the residue trend function of decomposing through the n time intrinsic mode function
Embodiment five, present embodiment are to the further specifying of concrete enforcement three, and among the step f system model error term x (t) are carried out 4 intrinsic mode functions and decompose, and obtain 4 eigenmode state function component IMF1, IMF2, IMF3 and IMF4:{c
1(t), c
2(t), c
3(t), c
4(t) }; And acquisition is through the remaining residual error function r of 4 intrinsic mode function decomposition
4(t).
Embodiment six, present embodiment are further specifying concrete enforcement one, utilize Time series analysis method to set up the model of the fault trend of the residual component that obtains about step 2 in the step 3, finish the forecast of small gentle change fault and the process of detection and be:
Residual component as the trend signal, is set up its autoregressive model, and expression formula is:
r(t)=a
1r(t-1)+a
2r(t-2)+…+a
pr(t-p)+ε(t)
Wherein, r (t) is the time series of residual error function, and p is a model order, a
iBe model parameter, ε (t) is a white noise sequence;
Observed reading is r (0), r (1) ..., r (N), the order of forecast model are p, get following system of equations by the autoregressive model expression formula:
Order:
Then the matrix form of following formula is:
R=BA+Δ
The least square solution of model parameter is:
A=(B
TB)
-1B
TR
Obtain the model parameter A of autoregressive model, utilize model parameter A to carry out failure prediction and detection.
The above AR model of single order is applicable to stochastic process stably, and single order AR model has and the different characteristic of AR (m) (m>1) model, and as the special case of AR model, single order AR model can be predicted nonstationary random process.
Set forth the specific embodiment of the present invention below by satellite executing mechanism Fault Diagnosis Simulation example:
Execution in step one: estimate topworks's fault amount with the predictive filtering method.
Dynamic (dynamical) state equation form of the attitude of satellite and discrete measurement equation are as follows:
In the formula, state variable x (t) is the attitude angular velocity of satellite, I
x, I
y, I
zBe the main shaft inertia of satellite, f
x(t), f
y(t), f
z(t) be actuator momenttum wheel fault.According to the predictive filtering theory, obtain the Fault Estimation amount and be:
Wherein
With pitch axis generation slope when the t=19.6s is the gradual fault f of actuator of 0.004Nm/s
yBe example, the Fault Estimation result of each as shown in Figure 3, wherein, three curves of Fig. 3 respectively are x, y, three change in coordinate axis direction of z (driftage, pitching, wobble shaft) Fault Estimation value, horizontal ordinate express time, unit is second, and ordinate is represented the Fault Estimation value, and unit is a Newton meter.Owing to there is bigger noise contribution in the estimated result, can't be directly used in fault diagnosis, must handle to extract fault signature signal.
Execution in step two: the predictive filtering result is carried out empirical modal decompose, obtain several IMF and residual component.
For avoiding the fault signature of mass loss rates gyro, promptly carry out an empirical modal through 128 samplings and decompose.Empirical modal decomposes to the 4th layer can be finished.
When the t=19.6s pitch axis breaks down, the empirical modal decomposition result of yaw axis (non-fault) Fault Estimation amount is Fig. 4, Fig. 5 is the empirical modal decomposition result of the Fault Estimation amount of pitch axis, and Fig. 6 is the empirical modal decomposition result of wobble shaft (non-fault) Fault Estimation amount.Fig. 4, Fig. 5, Fig. 6 are respectively driftage, pitching and lift-over three axis signals of gathering are carried out the result that empirical modal decomposes, each figure is respectively the residual component after 1,2,3,4,5 rank IMF (interior solid model state function) component and the decomposition from top to bottom, the horizontal ordinate express time, unit is second, ordinate is represented IMF component or residual component, and unit is a Newton meter.
Execution in step three: set up the AR model of residual component, carry out failure prediction and detection.
With the forecast model of single order AR model, utilize aforementioned the least square estimation method to obtain the model parameter value of AR model as residual component.Predicting the outcome of driftage, pitching and wobble shaft as Fig. 7.Fig. 7 is for to the described example of Fig. 3 (pitch axis breaks down when the t=19.6s), the x that adopts the inventive method to obtain, y, three change in coordinate axis direction of z (driftage, pitching, wobble shaft) Fault Estimation value, the horizontal ordinate express time, unit is second, and ordinate is represented the Fault Estimation value, and unit is a Newton meter.Adopt suitable threshold (0.02Nm), can realize detecting in advance small, initial failure.
In addition, also can verify the validity of failure prediction method proposed by the invention by small mutation failure.When t=20s, the yaw axis fault of undergoing mutation, amplitude is 0.01Nm, the failure prediction result of then driftage, pitching, wobble shaft such as Fig. 8.Fig. 8 is when 20s, when yaw axis generation amplitude is the mutation failure of 0.01N.m, and the x that adopts the inventive method to obtain, y, three change in coordinate axis direction of z (driftage, pitching, wobble shaft) Fault Estimation value, horizontal ordinate express time, unit is second, and ordinate is represented the Fault Estimation value, and unit is a Newton meter.
Above-mentioned fault diagnosis result can be verified the validity of failure prediction method proposed by the invention.
Claims (6)
1. satellite failure Forecasting Methodology of decomposing based on predictive filtering and empirical modal is characterized in that detailed process is as follows:
Step 1: utilize satellite nonlinear attitude kinetics relation, adopt the method for predictive filtering that the satellite control system error is estimated, obtain system model error term;
Step 2: the system model error term that step 1 obtains is carried out the empirical modal decomposition, E rank eigenmode state function IMF component and residual component before obtaining;
Step 3: utilize Time series analysis method to set up the model of the fault trend of the residual component that obtains about step 2, finish the forecast and the detection of small gentle change fault.
2. a kind of satellite failure Forecasting Methodology according to claim 1 based on predictive filtering and empirical modal decomposition, it is characterized in that utilizing in the step 1 satellite nonlinear attitude kinetics relation, adopt the method for predictive filtering the satellite control system error to be estimated the process that obtains system model error term is:
Set the model error of satellite control system and be made up of satellite executing mechanism fault and model uncertainty, its nonlinear state equation and measurement equation are respectively:
State equation:
The predictive filtering equation is:
X (t) ∈ R wherein
sBe state vector,
Be the estimated value of state vector, f ∈ R
sBe function of state that can be little,
Be known model error distribution matrix,
Be the measurement functions vector, d (t) ∈ R
qBe unknown error,
Be its estimated value,
Be illustrated in t
kThe measurement output of real system constantly is discrete form, v
k∈ R
mBe the measurement noise, and set v
kBe that average is zero, covariance matrix is Q ∈ R
M * mWhite Gaussian noise;
Constantly will measure function at t+ Δ t and carry out Taylor expansion, obtain:
Δ t=t wherein
K+1-t
kIt is the sampling period; This sampling period is normal value, y (t+ Δ t)=y
K+1, matrix
I element be:
P wherein
iDifferential exponent number when occurring d (t) in the Taylor expansion first,
Be c
iK rank Lie derivative;
Matrix A (Δ t) is a diagonal matrix, and its diagonal element is:
Matrix
The element that its i is capable is:
Get performance index function:
W ∈ R wherein
Q * qBe positive semidefinite weighting matrix, adopt the gradient optimizing algorithm that performance index are optimized, obtain being estimated as of model error item:
3. a kind of satellite failure Forecasting Methodology of decomposing according to claim 2 based on predictive filtering and empirical modal, it is characterized in that step 2 obtain before the process of E rank eigenmode state function IMF component and residual component be:
Step a, IMF decomposable process initialization: n=1, and satisfy relational expression
Set up, wherein r
N-1(t) be remaining residual error function after (n-1) inferior decomposition;
Step b, screening process initialization: k=1, and satisfy relational expression h
N (k-1)(t)=r
N-1(t) set up, wherein h
N (k-1)(t) be through the survival function after the k-1 time screening during the n time IMF decomposes;
Step c, obtain system model error term estimated value according to screening sequence
Survival function h after screening through the k time in the remaining residual error function of decomposing through the n time intrinsic mode function
Nk(t);
Steps d, the survival function h that adopts the judgement of standard deviation criterion to obtain
Nk(t) whether satisfy the condition of eigenmode state function, promptly
Whether less than threshold value T, 0.2≤T≤0.3;
Judged result is for being, execution in step e, and judged result is not for, and then k=k+1 returns execution in step c,
Step e, the n time intrinsic mode function IMF component c of acquisition
n(t)=h
Nk(t);
Step f, obtain system model error term estimated value
Remaining residual error function through the n time intrinsic mode function decomposition
Step g, make n=n+1, return execution in step b, E rank eigenmode state function IMF component and residual component before satisfying n=E and obtaining.
4. a kind of satellite failure Forecasting Methodology based on predictive filtering and empirical modal decomposition according to claim 3 is characterized in that step c obtains system model error term estimated value according to screening sequence
Survival function h after screening through the k time in the remaining residual error function of decomposing through the n time intrinsic mode function
Nk(t) process is:
Step c1, utilize cubic spline function to obtain system model error term estimated value
Survival function h after screening through the k-1 time in the residue trend function of decomposing through the n time intrinsic mode function
N (k-1)(t) upper and lower enveloping curve;
Step c2, the described survival function h of calculating
N (k-1)(t) upper and lower enveloping curve is at time t=1, and 2 ..., the average in the N
5. a kind of satellite failure Forecasting Methodology of decomposing based on predictive filtering and empirical modal according to claim 3 is characterized in that among the step f estimated value to system model error term
Carry out 4 intrinsic mode functions and decompose, obtain 4 eigenmode state function component IMF1, IMF2, IMF3 and IMF4:{c
1(t), c
2(t), c
3(t), c
4(t) }; And acquisition is through the remaining residual error function r of 4 intrinsic mode function decomposition
4(t).
6. a kind of satellite failure Forecasting Methodology according to claim 3 based on predictive filtering and empirical modal decomposition, it is characterized in that utilizing in the step 3 Time series analysis method to set up the model of the fault trend of the residual component that obtains about step 2, finish the forecast of small gentle change fault and the process of detection and be:
Residual component as the trend signal, is set up its autoregressive model, and expression formula is:
r(t)=a
1r(t-1)+a
2r(t-2)+…+a
pr(t-p)+ε(t)
Wherein, r (t) is the time series of residual error function, and p is a model order, and ai is a model parameter, and ε (t) is a white noise sequence;
Observed reading is r (0), r (1) ..., r (N), the order of forecast model are p, get following system of equations by the autoregressive model expression formula:
Order:
Then the matrix form of following formula is:
R=BA+Δ
The least square solution of model parameter is:
A=(B
TB)
-1B
TR obtains the model parameter A of autoregressive model, utilizes model parameter A to carry out failure prediction and detection.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2010102287440A CN101859146B (en) | 2010-07-16 | 2010-07-16 | Satellite fault prediction method based on predictive filtering and empirical mode decomposition |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2010102287440A CN101859146B (en) | 2010-07-16 | 2010-07-16 | Satellite fault prediction method based on predictive filtering and empirical mode decomposition |
Publications (2)
Publication Number | Publication Date |
---|---|
CN101859146A CN101859146A (en) | 2010-10-13 |
CN101859146B true CN101859146B (en) | 2011-11-30 |
Family
ID=42945103
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN2010102287440A Expired - Fee Related CN101859146B (en) | 2010-07-16 | 2010-07-16 | Satellite fault prediction method based on predictive filtering and empirical mode decomposition |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN101859146B (en) |
Families Citing this family (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102542159B (en) * | 2011-12-08 | 2014-10-08 | 北京空间飞行器总体设计部 | Method for predicting state of on-orbit spacecraft |
CN102789235B (en) * | 2012-06-18 | 2014-12-17 | 北京控制工程研究所 | Method for determining reconfigurability of satellite control system |
CN103364024B (en) * | 2013-07-12 | 2015-10-28 | 浙江大学 | Based on the sensor fault diagnosis method of empirical mode decomposition |
CN104750086B (en) * | 2013-12-26 | 2017-05-24 | 清华大学 | Fault and state estimation method and fault and state estimation device |
CN103840970B (en) * | 2014-01-24 | 2017-09-15 | 珠海多玩信息技术有限公司 | A kind of method and device for obtaining service operation state |
CN103825576B (en) * | 2014-03-14 | 2016-09-21 | 清华大学 | The polynomial filtering fault detection method of nonlinear system |
CN103973263B (en) * | 2014-05-16 | 2017-02-01 | 中国科学院国家天文台 | Approximation filter method |
CN104020671B (en) * | 2014-05-30 | 2017-01-11 | 哈尔滨工程大学 | Robustness recursion filtering method for aircraft attitude estimation under the condition of measurement interference |
CN104267732B (en) * | 2014-09-29 | 2017-07-28 | 哈尔滨工业大学 | Flexible satellite high stability attitude control method based on frequency-domain analysis |
CN104571088B (en) * | 2014-12-26 | 2018-01-05 | 北京控制工程研究所 | Satellite control system Multipurpose Optimal Method based on fault diagnosability constraint |
CN105574166A (en) * | 2015-12-16 | 2016-05-11 | 上海卫星工程研究所 | Fault dictionary based satellite fault diagnosis method |
CN105371836B (en) * | 2015-12-18 | 2018-09-25 | 哈尔滨工业大学 | Mixed type signal of fiber optical gyroscope filtering method based on EEMD and FIR |
CN106767898B (en) * | 2016-11-17 | 2019-04-09 | 中国人民解放军国防科学技术大学 | A method of detection measuring system of satellite attitude small fault |
CN106742068B (en) * | 2016-12-07 | 2019-01-04 | 中国人民解放军国防科学技术大学 | A method of diagnosis satellite attitude control system unknown failure |
CN107609685A (en) * | 2017-08-22 | 2018-01-19 | 哈尔滨工程大学 | It is a kind of based on floating motion when go through the job safety phase forecast system of enveloping estimation |
CN107830996B (en) * | 2017-10-10 | 2020-11-03 | 南京航空航天大学 | Fault diagnosis method for aircraft control surface system |
CN108877272B (en) * | 2018-08-02 | 2020-12-22 | 哈尔滨工程大学 | Vehicle navigation system and method based on destination state |
CN110209190B (en) * | 2019-03-01 | 2022-05-20 | 苏州纳飞卫星动力科技有限公司 | Satellite nominal orbit unbiased flight control method |
CN110703596B (en) * | 2019-08-01 | 2021-04-23 | 中国科学院力学研究所 | Master satellite attitude forecasting method and system of satellite-arm coupling system |
CN112949683B (en) * | 2021-01-27 | 2023-02-07 | 东方红卫星移动通信有限公司 | Intelligent fault diagnosis and early warning method and system for low-earth-orbit satellite |
CN112924996B (en) * | 2021-01-28 | 2023-11-03 | 湖南北斗微芯产业发展有限公司 | Method, equipment and storage medium for enhancing Beidou time sequence analysis reliability |
CN113189624B (en) * | 2021-04-30 | 2023-10-03 | 中山大学 | Self-adaptive classification multipath error extraction method and device |
CN114063456B (en) * | 2021-11-15 | 2023-06-02 | 哈尔滨工业大学 | Fault prediction and early warning method using autoregressive model and Kalman filtering algorithm |
CN115131943B (en) * | 2022-07-07 | 2023-10-31 | 杭州申昊科技股份有限公司 | Acousto-optic linkage early warning method based on deep learning |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101082494A (en) * | 2007-06-19 | 2007-12-05 | 北京航空航天大学 | Self boundary marking method based on forecast filtering and UPF spacecraft shading device |
CN101464125A (en) * | 2009-01-20 | 2009-06-24 | 西安交通大学 | Vertical rotation axis partial contact rubbing detection method |
CN101481019A (en) * | 2009-02-20 | 2009-07-15 | 华中科技大学 | Fault tolerant observing method of sensor for satellite attitude control system |
CN101590918A (en) * | 2009-06-19 | 2009-12-02 | 上海微小卫星工程中心 | Method for automatic fault diagnosis of satellite and diagnostic system thereof |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7451021B2 (en) * | 2003-05-06 | 2008-11-11 | Edward Wilson | Model-based fault detection and isolation for intermittently active faults with application to motion-based thruster fault detection and isolation for spacecraft |
FR2866423B1 (en) * | 2004-02-13 | 2006-05-05 | Thales Sa | DEVICE FOR MONITORING THE INTEGRITY OF THE INFORMATION DELIVERED BY AN INS / GNSS HYBRID SYSTEM |
-
2010
- 2010-07-16 CN CN2010102287440A patent/CN101859146B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101082494A (en) * | 2007-06-19 | 2007-12-05 | 北京航空航天大学 | Self boundary marking method based on forecast filtering and UPF spacecraft shading device |
CN101464125A (en) * | 2009-01-20 | 2009-06-24 | 西安交通大学 | Vertical rotation axis partial contact rubbing detection method |
CN101481019A (en) * | 2009-02-20 | 2009-07-15 | 华中科技大学 | Fault tolerant observing method of sensor for satellite attitude control system |
CN101590918A (en) * | 2009-06-19 | 2009-12-02 | 上海微小卫星工程中心 | Method for automatic fault diagnosis of satellite and diagnostic system thereof |
Non-Patent Citations (1)
Title |
---|
张筱磊等.《EMD 在卫星姿态控制系统未知故障诊断中的应用》.《华中科技大学学报(自然科学版)》.2009,第37卷(第增刊I期),204-206页. * |
Also Published As
Publication number | Publication date |
---|---|
CN101859146A (en) | 2010-10-13 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN101859146B (en) | Satellite fault prediction method based on predictive filtering and empirical mode decomposition | |
CN103577710B (en) | Aviation Power Converter faults Forecasting Methodology based on fractional order UPF | |
CN103389472B (en) | A kind of Forecasting Methodology of the cycle life of lithium ion battery based on ND-AR model | |
CN107358318A (en) | Based on GM(1,1)The urban power consumption Forecasting Methodology of model and Grey Markov chain predicting model | |
CN103383445A (en) | System and method for forecasting service life and reliability of intelligent electric meter | |
CN102968573A (en) | Online lithium ion battery residual life predicting method based on relevance vector regression | |
CN104506162A (en) | Fault prognosis method for high-order particle filter on basis of LS-SVR (least squares support vector regression) modeling | |
CN107844627A (en) | It is a kind of only to export Time variable structure modal parameter Bayesian Estimation method | |
CN105629958A (en) | Intermittence process fault diagnosis method based on sub-period MPCA-SVM | |
CN104019831B (en) | Gyroscope method for diagnosing faults based on EMD and entropy weight | |
CN106203693A (en) | A kind of system and method for Power Output for Wind Power Field climbing event prediction | |
CN104571087B (en) | Spacecraft control diagnosability determination method under a kind of influence of noise | |
CN104915534A (en) | Deformation analysis and decision-making method of electric power tower based on sequence learning | |
CN104881585A (en) | Flutter boundary prediction method of two-degree-of-freedom wing | |
CN105260568A (en) | Super high-rise building wind load inverse analysis method based on discrete Kalman filtering | |
Zhao et al. | On-line least squares support vector machine algorithm in gas prediction | |
CN109871625A (en) | Non-gaussian wind pressure analogy method based on Johnson transformation | |
Xu et al. | Damage detection of wind turbine blades by Bayesian multivariate cointegration | |
CN104063569A (en) | Equipment residual life predicting method based on EMD denoising and fading memory | |
CN104463347A (en) | Method for electronic product degenerate state trend prediction with singular signals | |
CN103353295B (en) | A kind of method of accurately predicting dam dam body vertical deformation amount | |
CN103778305A (en) | k-VNN- and LS-SVM-based modelling method for icing of electric transmission line | |
Guo et al. | Multi-variate probability density functions with dynamics for cloud droplet activation in large-scale models: Single column tests | |
CN108763250B (en) | Photovoltaic power station monitoring data restoration method | |
CN106772354B (en) | Method for tracking target and device based on parallel fuzzy gaussian sum particle filter |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20111130 |