Complex network-based high speed train system safety evaluation method

摘要

The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

主权项

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={v1, v2, . . . , vn}, wherein V is a set of nodes, vi is a node in the high speed train system, and n is a number of the nodes in the high speed train system; 1.2. physical connection relationships between the plurality of components are abstracted as connection sides, that is, E={e12, e13, . . . , eij}, i,j≦n; wherein E is a set of connection sides, and eij is a connection side between a node i and a node j; 1.3. a functional attribute degree value {tilde over (d)}i of a node is calculated based on the network model of the high speed train: a functional attribute degree of the node i is
{tilde over (d)}i=λi*ki  (1) wherein λi is a failure rate of the node i, and ki is a degree of the node i in a complex network theory, that is, ki is a number of sides connected with the node i; 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)}i, the failure rate λi and Mean Time Between Failures (MTBF) of one of the plurality of components as a training sample set, to normalize the training sample set, wherein 2.1. a calculation formula of the failure rate λi is,λ⁢⁢i=a⁢⁢number⁢⁢of⁢⁢times⁢⁢of⁢⁢faultrunning⁢⁢kilometers 2.2. the MTBF is obtained from fault time recorded in the fault data, that is,MTBFi=∑difference⁢⁢of⁢⁢fault⁢⁢time⁢⁢intervalsa⁢⁢total⁢⁢number⁢⁢of⁢⁢times⁢⁢of⁢⁢fault⁢-⁢1 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 fork⁡(k-1)2SVM classifiers respectively, of which an expression is as follows:fij⁡(x)=sgn⁡(∑t=1l⁢at⁢yt⁢K⁡(xij,x)+bij)(2) wherein 1 is a number of samples in a ith safety level and a jth safety level, K(xij, x) is as kernel function, x is a support vector, at is a weight coefficient of the SVM, and bij is an offset coefficient; 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 {xi, yi}, . . . , {xn, yn}, there is a total of one safety level, that is, ca1, ca2, . . . , ca1, a sample center of the ith safety level isci=1ni⁢∑j=1ni⁢xj,wherein ni is a number of samples of the ith safety level, and the Euclidean distance from one of the plurality of components xj to the sample center of the ith safety level isd⁡(xj,oi)=∑m=13⁢(xjm-cim)2(3) wherein, in the formula: xjm is an mth feature attribute of a jth sample point in a test sample; and cim is an mth feature attribute in an ith-category sample center; a distance discrimination function is defined assj⁡(x)=max⁡(d⁡(x,c))-d⁡(x,ci)max⁡(d⁡(x,c))-min⁡(d⁡(x,c))(4) tightness of weighted kNN-based different-category samples is defined asμi⁡(x)=1-∑j=1i⁢μi⁡(x(j))⁢d⁡(x,x(j))∑j=1k⁢d⁡(x,x(j))(5) wherein m is a number of k neighbors; ui(x) is a tightness membership degree at which a test sample belongs to the ith training data; and ui(x(j)) is the membership degree at which a jth neighbor belongs to the ith safety level, that is,μi⁡(x(j))={1,x∈cai0,x∉cai;and
a classification discrimination function of a sample point is
di(x)=si(x)×μi(x)  (6) a tightness di(x) at which a sample belongs to each safety level is calculated, and a category with a greatest value of di(x) is the sample point prediction result.