Undercarriage remaining use frequency model training method and device and computer equipment
A model training and landing gear technology, which is applied to computer parts, calculation, character and pattern recognition, etc., can solve problems affecting the performance of the landing gear retraction system, leakage of the actuator, and extended retraction time.
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Embodiment 1
[0027] like figure 1 As shown, the embodiment of the present invention provides a model training method for the number of remaining times of use of the landing gear, including the following steps:
[0028] S10, acquiring the time-series historical sample data of the retracting or lowering time of the landing gear of the aircraft.
[0029] Among them, the time series historical sample data includes m historical samples {S 1 , S 2 ,...,S m}, each historical sample {S 1 , S 2 ,...,S m} contains the monotonically increasing sequence of landing gear retraction or extension time, and the lowering process is taken as an example here. The kth historical sample is denoted as x 1 represents the time required for the landing gear to be lowered for the first time, x 2 Represents the time required for the landing gear to be lowered for the second time, historical samples {x 1 , x 2 ,...,x m} contains the landing gear down time is increasing, that is, x i+1 time greater than x...
Embodiment 2
[0038] like figure 2 As shown, in one embodiment provided by the present invention, after step S10 obtains the time-series historical sample data of the landing gear down time of the aircraft, the method further includes:
[0039] S101, if the number of historical samples is less than a predetermined value, then divide the historical samples into multiple subsequences
[0040] Among them, n k =L k -n 0 +1 for said historical sample S k The total number of subsequences that can be divided, n 0 is the minimum segment length, historical sample S k The maximum subsequence length before an abnormal element in is l k , which is S k The corresponding number of normal use of the landing gear. For example, historical sample S k ={x 1 , x 2 , x 3 , x 4 , x 5}, n 0 is 2, the subsequences that can be divided are:
[0041] S k1 ={x 1 , x 2}
[0042] S k2 ={x 1 , x 2 , x 3}
[0043] S k3 ={x 1 , x 2 , x 3 , x 4}
[0044] S k4 ={x 1 , x 2 , x 3 , x 4 , ...
Embodiment 3
[0061] like image 3 As shown, in one embodiment provided by the present invention, a method for predicting the number of remaining uses of landing gear is provided, including the following steps:
[0062] S11, acquiring the time series data of landing gear down to be predicted.
[0063] Wherein, the time series data of landing gear down to be predicted may be the time series data from the time when the landing gear fails to the current time.
[0064] S12, extracting the predicted length feature, the predicted degradation rate feature, the predicted differential degradation rate feature and the predicted relative degradation rate feature from the landing gear down time series data to be predicted.
[0065] In the embodiment of the present invention, the length feature to be predicted, the degradation rate feature to be predicted, the differential degradation rate feature to be predicted, and the relative degradation rate feature to be predicted are extracted from the landing ...
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