System fault diagnosis method based on Malek model
A system fault diagnosis method technology, applied in the direction of response error generation, instrumentation, electrical digital data processing, etc., can solve the problem of system fault diagnosis algorithm failure, etc., to solve the system fault diagnosis problem, efficiently search, reduce The effect of load
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Embodiment 1
[0085] In order to verify the performance of the algorithm of the present invention, the algorithm is written in Matlab language, and the experiment is carried out on a computer with a memory of 4.00GB and a CPU of Core(TM) i52.5GHz.
[0086] A system fault diagnosis method based on the Malek model, including the following steps:
[0087] Step 1: Under the Malek model, specify the fault-free node method to generate the initial population. Each individual in the generated initial population, that is, a binary string corresponds to the multi-machine system, and each individual bit, that is, a binary bit, corresponds to the node; the specific method includes the following steps:
[0088] (1) In a multi-machine system containing n nodes, randomly designate a node k as fault-free;
[0089] (2) According to the degree of node k, find the node j adjacent to node k, if S(k,j)=0, it means that the test results of node k and node j are the same, and the test result of node j The state...
Embodiment 2
[0130] In order to test and evaluate the beneficial effect of the algorithm of the present invention in system fault diagnosis, the average CPU time of the two diagnostic algorithms is compared. Diagnosis algorithm one is the system fault diagnosis algorithm under the Malek model of the present invention; The concrete steps of diagnosis algorithm two are as follows:
[0131] Step 1: Specify the fault-free node method to generate the initial population;
[0132] Step 2: Calculate the fitness of individuals in the population, and determine whether there is an individual with a fitness value of 1, if not, go to step 3;
[0133] Step 3: Perform the following genetic operations on the population:
[0134] 3.1 Select operation, same as diagnosis algorithm one;
[0135] 3.2 Variation operation, that is, variation method 2 described in Example 1;
[0136] 3.3 Crossover operation, same as diagnosis algorithm 1;
[0137] 3.4 After the crossover operation, judge whether the number t ...
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