Patent ID: 9630637
Date: 2017-04-25
CPC Classifications: B61L,G06F,G06N,H04L

Claim:
1. A complex network-based high speed train system safety evaluation method, comprising the following steps: Step 1, constructing a network model G(V, E) of a high speed train according to a physical structure relationship of the high speed train, wherein 1.1. a plurality of components in a high speed train system are abstracted as nodes, that is, V={v 1.2. physical connection relationships between the plurality of components are abstracted as connection sides, that is, E={e 1.3. a functional attribute degree value {tilde over (d)} wherein λ Step 2, by mean of analyzing operational fault data of the high speed train and combining a physical structure of the high speed train system, extracting the functional attribute degree value {tilde over (d)} 2.1. a calculation formula of the failure rate λ 2.2. the MTBF is obtained from fault time recorded in the fault data, that is, 2.3. samples are trained by using a support vector machine (SVM) Step 3, dividing safety levels of the samples by using a kNN-SVM; wherein 3.1. training samples in k safety levels are differentiated in pairs, and an optimal classification face is established for SVM classifiers respectively, of which an expression is as follows: wherein 1 is a number of samples in a ith safety level and a jth safety level, K(x 3.2. for one of the plurality of components to be tested, a safety level of the component is voted by combining the above two kinds of classifiers and using a voting method; the kind with the most votes is the safety level of the component; 3.3. as an operating environment of the high speed train system is complicated, it is easy to lead to a situation where classification is impossible when classification is carried out by using the SVM; therefore, a weighted kNN-based discrimination function is defined, and safety levels of the plurality of components are divided once again, which comprises steps as follows: in a training set {x wherein n i is a number of samples of the ith safety level, and the Euclidean distance from one of the plurality of components x j to the sample center of the ith safety level is wherein, in the formula: x a distance discrimination function is defined as tightness of weighted kNN-based different-category samples is defined as wherein m is a number of k neighbors; u and a classification discrimination function of a sample point is a tightness d