Satellite fault diagnosing method based on AdaBoost algorithm
A satellite fault and diagnosis method technology, which is applied in computing, computer parts, special data processing applications, etc., can solve problems such as weak fault diagnosis ability, achieve the effect of increasing attention, enhancing diagnosis ability, and improving classification accuracy
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specific Embodiment approach 1
[0058] Specific embodiment one: a kind of satellite fault diagnosis method based on AdaBoost algorithm described in the present embodiment comprises the following steps: Step 1, data initialization: the information that satellite is provided is carried out statistics, constructs every kind of fault type and every different type The corresponding matrix of the occurrence times of the event features is used to establish the fault diagnosis model;
[0059] The data relationship between the status and event characteristics of the satellite and the status and fault type is shown in formula (1),
[0060]
[0061] In the formula, E i ——the characteristics of the i-th type of event, i=1,2,...N;
[0062] K - the total number of satellite states;
[0063] C j ——the jth state of the satellite, j=1,2,...K;
[0064] e ij ——the number of occurrences of the i-th event-type state monitoring data in the j-th state;
[0065] At this point, each sample in the training set is given the s...
specific Embodiment approach 2
[0083] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the specific process of assigning the same weight to each sample in the training set described in step 1 is:
[0084] Assign the same weight w to all samples in the training set, such as formula (3),
[0085] w ( i ) = 1 N - - - ( 3 )
[0086] In the formula, N - the number of state samples in the training set;
[0087] w (i) ——the weight of the i-th state sample, i=1,2...,N. Other steps are the same as in the first embodiment.
specific Embodiment approach 3
[0088] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the specific process of updating the weights of the training set described in step 3 is: in order to measure the classification accuracy of the weighted training set by the weak classifier, Calculate the parameter β, such as formula (4), and then use the parameter β to adjust the weight, such as formula (5),
[0089] β = e 1 - e - - - ( 4 )
[0090] w ′ ( i ) = w ( i ) ( β ) 1 - I ...
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