Complex network-based high speed train system safety evaluation method

Patent metadata

Field	Value
Publication number	US-9630637-B2
Application number	US-201515123684-A
Country	US
Kind code	B2
Filing date	Nov 27, 2015
Priority date	Dec 12, 2014
Publication date	Apr 25, 2017
Grant date	Apr 25, 2017

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.

First claim

The invention claimed is:

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 , v 2 , . . . , v n }, wherein V is a set of nodes, v 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 12 , e 13 , . . . , e ij }, i,j≦n; wherein E is a set of connection sides, and e ij 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 =λ i *k i   (1)

wherein λ i is a failure rate of the node i, and k i is a degree of the node i in a complex network theory, that is, k 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)} i , the failure rate λ 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 λ i is,

λ
⁢

⁢
i

=

a
⁢

⁢
number
⁢

⁢
of
⁢

⁢
times
⁢

⁢
of
⁢

⁢
fault

running
⁢

⁢
kilometers

2.2. the MTBF is obtained from fault time recorded in the fault data, that is,

MTBFi
=

∑

difference
⁢

⁢
of
⁢

⁢
fault
⁢

⁢
time
⁢

⁢
intervals

a
⁢

⁢
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 for

k
⁡

(

k
-
1

)

2

SVM classifiers respectively, of which an expression is as follows:

f
ij

⁡

(
x
)

=

sgn
⁡

(

∑

t
=
1

l

⁢

a
t

⁢

y
t

⁢

K
⁡

(

x
ij

,
x

)

+

b
ij

)

(
2
)

Assignees

• Univ Beijing Jiaotong

Inventors

• Jia Limin
• Qin Yong
• Wang Yanhui
• Lin Shuai
• Shi Hao
• Bi Lifeng
• Guo Lei
• Li Lijie
• Li Man

Classifications

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• G06F30/00

  Computer-aided design [CAD] · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06F17/50

  Physics · mapped topic