Complex network-based high speed train system safety evaluation method

Abstract

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.

Claims

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.sub.1, v.sub.2, . . . , v.sub.n}, wherein V is a set of nodes, v.sub.i 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={e.sub.12, e.sub.13, . . . , e.sub.ij}, i,jn; wherein E is a set of connection sides, and e.sub.ij is a connection side between a node i and a node j; 1.3. a functional attribute degree value {tilde over (d)}.sub.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)}.sub.i=.sub.i*k.sub.i(1) wherein .sub.i is a failure rate of the node i, and k.sub.i is a degree of the node i in a complex network theory, that is, k.sub.i 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)}.sub.i, the failure rate .sub.i 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 .sub.i is, $i=\frac{a\mathrm{number}\mathrm{of}\mathrm{times}\mathrm{of}\mathrm{fault}}{\mathrm{running}\mathrm{kilometers}}$ 2.2. the MTBF is obtained from fault time recorded in the fault data, that is, $\mathrm{MTBFi}=\frac{.Math.\mathrm{difference}\mathrm{of}\mathrm{fault}\mathrm{time}\mathrm{intervals}}{a\mathrm{total}\mathrm{number}\mathrm{of}\mathrm{times}\mathrm{of}\mathrm{fault}\text{-}1}$ 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 for $\frac{k\left(k-1\right)}{2}$ SVM classifiers respectively, of which an expression is as follows: $\begin{array}{cc}{f}_{\mathrm{ij}}\left(x\right)=\mathrm{sgn}\left(\underset{t=1}{\overset{l}{.Math.}}{a}_t{y}_{t}K\left(x_{\mathrm{ij}},x\right)+b_{\mathrm{ij}}\right)& \left(2\right)\end{array}$ wherein 1 is a number of samples in a ith safety level and a jth safety level, K(x.sub.ij, x) is as kernel function, x is a support vector, a.sub.t is a weight coefficient of the SVM, and b.sub.ij 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 {x.sub.i, y.sub.i}, . . . , {x.sub.n, y.sub.n}, there is a total of one safety level, that is, ca.sub.1, ca.sub.2, . . . , ca.sub.1, a sample center of the ith safety level is $c_i=\frac{1}{n_i}\underset{j=1}{\overset{n_i}{.Math.}}{x}_j,$ wherein n.sub.i is a number of samples of the ith safety level, and the Euclidean distance from one of the plurality of components x.sub.j to the sample center of the ith safety level is $\begin{array}{cc}d\left(x_j,o_i\right)=\sqrt{\underset{m=1}{\overset{3}{.Math.}}{\left(x_{\mathrm{jm}}-c_{\mathrm{im}}\right)}^2}& \left(3\right)\end{array}$ wherein, in the formula: x.sub.jm is an mth feature attribute of a jth sample point in a test sample; and c.sub.im is an mth feature attribute in an ith-category sample center; a distance discrimination function is defined as $\begin{array}{cc}{s}_j\left(x\right)=\frac{\max \left(d\left(x,c\right)\right)-d\left(x,c_i\right)}{\max \left(d\left(x,c\right)\right)-\min \left(d\left(x,c\right)\right)}& \left(4\right)\end{array}$ tightness of weighted kNN-based different-category samples is defined as $\begin{array}{cc}{}_i\left(x\right)=1-\frac{\underset{j=1}{\overset{i}{.Math.}}{}_i\left(x^{\left(j\right)}\right)d\left(x,x^{\left(j\right)}\right)}{\underset{j=1}{\overset{k}{.Math.}}d\left(x,x^{\left(j\right)}\right)}& \left(5\right)\end{array}$ wherein m is a number of k neighbors; u.sub.i(x) is a tightness membership degree at which a test sample belongs to the ith training data; and u.sub.i(x.sup.(j)) is the membership degree at which a jth neighbor belongs to the ith safety level, that is, ${}_i\left(x^{\left(j\right)}\right)=\{\begin{array}{c}1,x{\mathrm{ca}}_i\\ 0,x.Math.{\mathrm{ca}}_i\end{array};$ and a classification discrimination function of a sample point is
d.sub.i(x)=s.sub.i(x).sub.i(x)(6) a tightness d.sub.i(x) at which a sample belongs to each safety level is calculated, and a category with a greatest value of d.sub.i(x) is the sample point prediction result.

2. The complex network-based high speed train system safety evaluation method according to claim 1, wherein safety of the high speed train is divided into levels as follows according to Grade-one and Grade-two repair regulations and fault data records of a motor train unit: TABLE-US-00003 y = 1 Safe: Not Affected, Continue Running y = 5 Safer: Temporary Repair and Odd Repair, Behind Schedule y = 10 Not Safe: Out of Operation and Not Out of the Rail Yard that is, Safety Level 1 corresponding to y=1 is Safe, which comprises running states of a train: Not Affected, Continue Running; Safety Level 2 corresponding to y=5 is Safer, which comprises running states of the train: Temporary Repair and Odd Repair, Behind Schedule; Safety Level 3 corresponding to y=10 is Not Safe, which comprises running states of the train: Out of Operation and Not Out of a Rail Yard.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a flow chart of a high speed train safety evaluation method based on a complex network and a weighted kNN-SVM.

(2) FIG. 2 illustrates a network model of a physical structure of a high speed train system.

(3) FIG. 3 illustrates a region where classification cannot be carried out with a SVM method.

(4) FIG. 4 illustrates a training set sample.

DETAILED DESCRIPTION OF THE INVENTION

(5) The present invention provides a complex network-based high speed train system safety evaluation method, and the present invention is further described below with reference to the accompanying drawings.

(6) FIG. 1 is a flow chart of steps of high speed train system safety evaluation. As shown in FIG. 1, at first, 33 components in a high speed train bogie system are extracted for functional structure characteristics of the bogie system (Step 1.1). Interaction relationships between the 33 components are abstracted based on a physical structure relationship of the bogie system (Step 1.2). The components are abstracted as nodes, and the interaction relationships between the components are abstracted as sides, to construct a network model of the high speed train bogie system as shown in FIG. 2.

(7) A functional attribute degree {tilde over (d)}.sub.i=.sub.i.Math.k.sub.i of a node is selected as an input quantity from the perspective of the structure of the component based on the network model of the bogie (Step 1.3); a failure rate .sub.i and MTBF are selected as input quantities from the perspective of reliability attribute of the component in combination with operational fault data of the high speed train (Steps 2.1 and 2.2). For the same component in the high speed train bogie system, {tilde over (d)}.sub.i, .sub.i and MTBF thereof in different operation kilometers are calculated respectively as a training set. For example, when the train runs to 2450990 kilometers, a gearbox assembly of a node 14 has {tilde over (d)}.sub.14,1=0.027004, .sub.14,1=0.013502, MTBF.sub.14,1=150.2262. Safety levels of the high speed train are divided into three levels according to Grade-one and Grade-two repair regulations and fault data records of a motor train unit, that is, y=1 is Safe, y=5 is Safer, and y=10 is Not Safe.

(8) By taking a component gearbox assembly as an example, three safety levels of the gearbox assembly, that is, a total of 90 groups of input quantities, are selected as a training set, SVM training is carried out by using an LIBSVM software package, and the accuracy of the calculation result is only 55.7778%. It is found through analysis that an operating environment of the high speed train is relatively complicated, a situation where classification is impossible often occurs when classification is carried out by using a SVM (as shown in FIG. 3), and thus it is necessary to use kNN to make a secondary judgment.

(9) A sample center

(10) 0$c_i=\frac{1}{n_i}\underset{j=1}{\overset{n_i}{.Math.}}{x}_j$
of each of the three levels of the gearbox assembly that affect safety of the system and a distance

(11) $d\left(x,o_i\right)=\sqrt{\underset{m=1}{\overset{3}{.Math.}}{\left(x_m-c_{\mathrm{im}}\right)}^2}$
from a sample to be tested x(0.02746, 0.01443, 200.75) to the three safety levels are calculated. Then, the following calculation is carried out step by step in the three safety levels: i=1, 2, 3

(12) $\begin{array}{cc}{s}_i\left(x\right)=\frac{\max \left(d\left(x,c\right)\right)-d\left(x,c_i\right)}{\max \left(d\left(x,c\right)\right)-\min \left(d\left(x,c\right)\right)}& \left(4\right)\\ {}_i\left(x\right)=1-\frac{\underset{j=1}{\overset{k}{.Math.}}{}_i(x^{\left(j\right)}d\left(x,x^{\left(j\right)}\right)}{\underset{j=1}{\overset{k}{.Math.}}d\left(x,x^{\left(j\right)}\right)}& \left(5\right)\end{array}$

(13) Finally, a classification discrimination function g.sub.i(x)=s.sub.i(x).sub.i(x) of each of the three safety levels is calculated, and it is obtained that a final classification result of a test sample (as shown in FIG. 4) x(0.02746, 0.01443, 200.75) is the safety level. It is obtained through a great number of experiments that accuracy of classification carried out by the component gearbox assembly by using a kNN-SVM is 96.6667%. A training set is established for each component in the high speed train bogie system, and it is found through experimental comparison that use of the kNN-SVM classification method significantly improves accuracy of evaluation on safety of the system, as shown in Table 2.

(14) TABLE-US-00002 TABLE 2 Comparison between two methods Method Average accuracy % SVM 73.3333 kNN-SVM 95.5556