Satellite fault prediction method based on predictive filtering and empirical mode decomposition
An empirical mode decomposition and predictive filtering technology, applied in attitude control, position/direction control, non-electric variable control, etc., can solve the problems of being affected by noise and unable to accurately predict the fault trend, and achieve noise elimination, accurate prediction and Detect and improve the effect of prediction accuracy
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specific Embodiment approach 1
[0011] Specific implementation mode one: A satellite fault prediction method based on predictive filtering and empirical mode decomposition, the specific process is as follows:
[0012] Step 1: Estimate the error of the satellite control system by using the nonlinear attitude dynamic relationship of the satellite, and use the method of predictive filtering to obtain the error term of the system model;
[0013] Step 2: Perform empirical mode decomposition on the error item of the system model obtained in step 1 to obtain the first n order intrinsic mode function IMF components and residual components;
[0014] Step 3: Use the time series analysis method to establish a model of the fault trend of the residual component obtained in Step 2, and complete the prediction and detection of small and slowly changing faults.
[0015] Empirical Mode Decomposition (EMD) is a new signal decomposition method proposed by the scholar Huang E in 1998. It can use the change of the internal tim...
specific Embodiment approach 2
[0026] Specific implementation mode two, This implementation mode is a further description of the specific implementation mode 1. In step 1, the satellite control system error is estimated by using the satellite nonlinear attitude dynamic relationship and the predictive filtering method, and the process of obtaining the system model error item is as follows:
[0027] It is assumed that the model error of the satellite control system is composed of satellite actuator failure and model uncertainty, and the measurement equations of the predictive filtering system and the actual system are respectively:
[0028] Equation of state:
[0029]
[0030] The prediction filter equation is:
[0031]
[0032] in is the state vector, is the estimated value of the state vector, is a differentiable state function, is the known model error distribution matrix, is the measurement function vector, is an estimate of the unknown model error, and the measurement output of the ac...
specific Embodiment approach 3
[0047] Specific implementation mode three, This embodiment is a further description of the specific implementation one. The process of obtaining the first n-order intrinsic mode function IMF component and residual component in step two is:
[0048] Set the estimated value of the error term of the system model to be ,time ,
[0049] Step a, IMF decomposition process initialization: , and satisfy the relation established, of which for the first The remaining residual function after the second decomposition;
[0050] Step b. Screening process initialization: k =1, and satisfy the relation established, of which for the first n After the second IMF decomposition k- The remaining function after 1 screening;
[0051] Step c. Obtain the estimated value of the error term of the system model according to the screening procedure After the first n In the remaining residual function of the sub-eigenmode function decomposition after the first k Residual function aft...
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