Aircraft telemetry parameter anomaly detection method based on uncertainty estimation
A technology of uncertainty and telemetry parameters, applied in the field of data processing, can solve problems such as abnormal detection of aircraft telemetry parameters, failure to reflect model estimation confidence, and overfitting, so as to reduce overfitting, prevent overfitting, and improve effect of effect
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Embodiment approach 2
[0068] Embodiment 2. This embodiment is a modification of the method for establishing an LSTM-based multivariate telemetry parameter uncertainty characterization and estimation model for an aircraft in the uncertainty estimation-based aircraft telemetry parameter anomaly detection method described in this embodiment. Further defined, the method specifically includes:
[0069] Obtain the training parameter set of the multivariate telemetry parameters of the aircraft and the training data of the parameters to be detected;
[0070] Preprocessing the training parameter set specifically includes:
[0071] Using the maximum mutual information coefficient method to perform feature extraction on the training parameter set, and obtain the feature parameter set of the parameter to be detected;
[0072] performing feature fusion on the feature parameter set by using a principal component analysis method to obtain a fusion feature parameter set;
[0073] According to the fusion feature ...
Embodiment approach 3
[0074] Embodiment 3. In this embodiment, in the method for anomaly detection of aircraft telemetry parameters based on uncertainty estimation described in Embodiment 2, the method of using the maximum mutual information coefficient method is used to perform feature extraction on the training parameter set to obtain the Further definition of the method of the feature parameter set of the parameter to be detected, the method includes:
[0075] According to the maximum mutual information coefficient method, obtain the maximum mutual information coefficient of the parameters to be detected and all parameters in the training parameter set;
[0076] Setting a threshold of the maximum mutual information coefficient, selecting a parameter related to the parameter to be detected according to the threshold, and obtaining a feature parameter set of the parameter to be detected according to the parameter related to the parameter to be detected.
Embodiment approach 4
[0077] Embodiment 4. In this embodiment, in the method for abnormal detection of aircraft telemetry parameters based on uncertainty estimation described in Embodiment 2, the feature fusion of the feature parameter set is performed using the principal component analysis method to obtain the fusion feature parameters The method of set is further defined, and the method specifically includes:
[0078] Standardizing the feature parameter set to obtain a standardized feature parameter set;
[0079] obtaining the covariance matrix of the standardized parameter feature set;
[0080] Obtain the eigenvector matrix and eigenmatrix of the covariance matrix;
[0081] Obtaining the principal component contribution rate of the standardized characteristic parameters according to the characteristic vector matrix and the characteristic matrix;
[0082] Set the parameter dimensions of the fusion feature parameter set, arrange the principal component contribution rates in descending order, and...
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