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CN110309550B - A reliability analysis method of high-speed train system based on potential energy field and network efficiency - Google Patents

A reliability analysis method of high-speed train system based on potential energy field and network efficiency Download PDF

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CN110309550B
CN110309550B CN201910495102.8A CN201910495102A CN110309550B CN 110309550 B CN110309550 B CN 110309550B CN 201910495102 A CN201910495102 A CN 201910495102A CN 110309550 B CN110309550 B CN 110309550B
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秦勇
付勇
寇淋淋
叶萌
程晓卿
贾利民
夏建军
王豫泽
李想
刘典
赵晓春
梁立鹏
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Beijing Jiaotong University
CRRC Qingdao Sifang Co Ltd
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Abstract

The invention provides a method for analyzing the reliability of a high-speed train system based on a potential energy field and network efficiency, which comprises the following specific steps: firstly, analyzing the topological structure characteristics of a high-speed train system, and establishing a high-speed train system network model based on a complex network theory; secondly, analyzing the whole dynamic process of the fault propagation of the high-speed train system based on a high-speed train complex network model and a potential energy field theory to obtain a system fault propagation failure component at each step; and finally, dynamically analyzing the reliability of the high-speed train system based on the network efficiency related indexes on the basis of the fault propagation of the high-speed train system. The reliability of the high-speed train system is analyzed by combining the related indexes such as train system fault propagation, system network efficiency and the like, the reliability change rule of the high-speed train system can be dynamically and gradually analyzed, and theoretical support is provided for key maintenance tasks of operation and maintenance personnel.

Description

High-speed train system reliability analysis method based on potential energy field and network efficiency
Technical Field
The invention relates to the field of reliability analysis of a high-speed train system, in particular to a method for analyzing the reliability of the high-speed train system based on a potential energy field and network efficiency.
Background
The high-speed railway system plays an irreplaceable global support role in the development of economic society and the promotion of international social status in China, and by the end of 2017, the operating mileage of high-speed railways in China reaches 2.5 kilometers, the largest-scale high-speed railway network in the world is formed, the motor train unit is operated on line to 2522 groups, 17.13 hundred million passengers are sent in the year, and the motor train unit is operated in 4761 rows in summer in 2017; the Chinese standard motor train unit 'Fuxing' realizes the commercial operation at the speed of 350 kilometers per hour, and establishes a new standard pole for the construction and operation of the high-speed railway in the world. The developed countries of the foreign rail transit also attach great importance to the development of the high-speed railway system, wherein the operating mileage of the European high-speed railway is about 1.2 kilometers, the Japan already builds a magnetic suspension high-speed railway with the speed of 500 kilometers per hour, and even the United states proposes to develop a super high-speed railway system with the speed of 1200 kilometers per hour.
Along with the rapid development of high-speed railway technology in the world, the technical composition complexity of a high-speed train is higher and higher, and different subsystems and parts of the high-speed train have more than forty thousand parts and have strong mutual dependence interaction relation, so that the high-speed train is a typical complex mechanical electronic information large system. The high-speed train is a complex, electromechanical and integrated high-technology system, and the reliability research work of pedigree is necessary.
At present, the reliability analysis method of the high-speed train system comprises Failure Mode and impact analysis (FMEA), Petri network model, Fault Tree Analysis (FTA) model, Bayesian Network (BN) model and the like, and the method is applied to reliability analysis of the high-speed train and key systems thereof. Most of these methods, however, combine historical fault data with expert experience and perform reliability analysis based on a certain risk event or fault pattern. The human subjective factors have large influence, and the overall characteristics of the system are neglected, so that certain errors are generated, and the correctness of results is influenced. Therefore, researchers have attempted to analyze high speed train reliability using complex network theory. The method comprises the steps of establishing related network topological characteristic indexes on the basis of complex network structure characteristics, and analyzing the influence degree of a high-speed train system structure on the system reliability better so as to perform subsequent reliability analysis work. However, the key points of these network-based reliability analysis methods are focused on the final result of the system reliability analysis, but neglect the dynamic process of the reliability analysis, and present a static analysis result, which is not favorable for providing guidance for the subsequent daily maintenance and repair work.
The emphasis of reliability analysis of a high-speed train system is based on system failure, which is caused by the failure of one component of the system to cause the function of the associated component to be reduced or failed, and further, the system is physically or information separated from the whole system, thereby reducing the efficiency of the whole system. Faulty couplings between these components can create complex network relationships that present an unexpected "domino" operational risk to the system. Therefore, in the dynamic process of researching the reliability analysis of the high-speed train system, the fault propagation critical path and the propagation influence range of the system fault are identified in combination with the fault propagation of the train system, the whole process of the fault propagation of the system is described, and the reliability of the train system is further dynamically analyzed.
Disclosure of Invention
The present invention is directed to overcoming the problems in the art and providing an effective method for reliability of a high speed train system.
The purpose of the invention is realized by the following technical scheme:
the method for analyzing the reliability of the high-speed train system based on the potential energy field and the network efficiency comprises the following steps:
(1) establishing a high-speed train system complex network model which takes high-speed train components as nodes and takes the coupling relationship between the components as a connecting edge;
(2) on the basis of the complex network model of the high-speed train system, a potential energy field theory is combined to establish a fault propagation model of the high-speed train system, and a system fault propagation failure component is obtained;
(3) and analyzing the reliability of the high-speed train system by combining the network efficiency of the high-speed train system on the basis of the fault propagation model of the high-speed train system.
Preferably, the high-speed train system in the step (1) consists of n parts, m connecting edges exist among the parts, and a complex network model of the high-speed train system is established
G＝(V,E,R)
Wherein: v ═ V (V)1,v2,…,vj,…,vn) Representing a set of n nodes of the network, the properties of which depend on the fault state of the component; e ═ E (E)12,…,eij,…,ern) Is a set of m connecting edges of the network, R ═ R12,…,rij,…,rrn) Indicating the degree of interaction and coupling between the components of the high speed train system.
Preferably, the fault propagation model S of the high-speed train system is established in the step (2)k
Sk＝<V,E,R,Mk,Wk,Ik>
Wherein, IkAn incidence matrix W representing a system network model after the k-th fault propagation of the high-speed train systemkShowing the state of the high-speed train system that each component in the kth step is affected by the fault, MkRepresenting the system state of the high-speed train system after the k-th fault propagation occurs;
Mkis shown as
Figure BDA0002088305500000031
M(l)kThe system state set of the propagated and marked nodes on the l fault propagation path after the k fault propagation of the high-speed train system is shown, W (l)k-1Representing the state set influenced by the fault of the propagated marking node on the ith fault propagation path after the fault propagation of the kth-1 step of the high-speed train system occurs,
the symbol "·" is defined as:
if A is equal to Rm×nAnd B ∈ R1×nThen, there are:
Figure BDA0002088305500000032
(symbol)
Figure BDA0002088305500000033
is defined as:
if A is equal to Rm×nAnd B ∈ R1×nThen, there are:
Figure BDA0002088305500000034
(symbol)
Figure BDA0002088305500000035
is defined as:
if A is equal to Rm×nAnd B ∈ R1×nThen, there are:
Figure BDA0002088305500000041
propagation probability P in fault propagation model of high-speed train systemijIs composed of
Figure BDA0002088305500000042
Wherein, PijRepresenting virus slave node viPropagation to node vjThe probability of (d);
Figure BDA0002088305500000043
representing a virus node viNode v adjacent theretojAverage propagation rate in between; lambda [ alpha ]iRepresenting a virus node viThe disease course time of (1), G is a constant parameter; m isi、mjThe masses, r, of objects i and j, respectivelyijIs the distance between objects i and j;
propagation probability on the ith fault propagation link of high-speed train system
Figure BDA0002088305500000044
And when the fault propagation is less than a certain threshold value, the initial node fault is terminated, the node state on the link stops the iterative process, and the first fault propagation path and the states of the high-speed train system and the high-speed train component at the moment are obtained.
Preferably, the network efficiency of the high-speed train system in the step (3) is
Figure BDA0002088305500000045
Efk (G) represents the network efficiency of the whole high-speed train system after the k-th fault propagation of the system occurs;
Figure BDA0002088305500000046
representing the shortest time for a path between any two nodes after the fault propagation of the kth step occurs in the system;
calculating the reliability Rk (G) of the high-speed train system as
Figure BDA0002088305500000047
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a diagram of a train bogie system component coupling relationship matrix.
FIG. 3 is a graph of the degree of interaction and coupling between train bogie system components.
Fig. 4 is an initial state diagram of train bogie system components.
FIG. 5 train bogie system component failure propagation probability matrix.
Fig. 6 is a diagram of a train bogie system fault propagation process.
Fig. 7 is a reliability variation trend chart of the train bogie system.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The method implementation of this embodiment specifically includes the following steps (the method flow is shown in fig. 1):
(1) analyzing and researching the physical topological structure characteristics of the high-speed train system, and establishing a high-speed train system complex network model taking high-speed train components as nodes and coupling relations among the components as connecting edges. And performing analog simulation by analyzing and researching the topological structure characteristics and fault propagation characteristics of the bogie system of the high-speed train of a certain model. 35 parts (shown in table 1) of the high-speed train bogie system are extracted, and the coupling action relationship among the parts of the system is analyzed to perform complex network modeling on the high-speed train bogie system.
TABLE 1 high speed train bogie system parts List
Figure BDA0002088305500000051
Figure BDA0002088305500000061
The high-speed train system is an electromechanical information large system which is composed of a plurality of components and has complex interaction relation. The complex network theory is applied, the bogie system components are nodes, the interaction relationship among the components is a connecting edge, and a complex network model of the high-speed train system is constructed. Assuming that the high-speed train system is composed of n components, and m connecting edges exist between the components, the complex network model of the high-speed train system can be expressed as G ═ V, E, R.
Wherein:
V＝(v1,v2,…,vj,…,vn) Representing a set of n nodes of the network, the properties of which depend on the fault state of the component; e ═ E (E)12,…,eij,…,ern) Is a set of m connecting edges of the network, which indicates whether there are mechanical, electronic, information, control, and other interactions and coupling relationships between the bogie system components, and may define E ═ Eij]Is provided with
Figure BDA0002088305500000062
R＝(r12,…,rij,…,rrn) The degree of interaction and coupling between the components of the high speed train system may be expressed by defining R ═ Rij]。
In the bogie system of the high-speed train, there are three connection relationships, i.e., mechanical connection, electrical connection, and information connection, and therefore, the bogie system network G is a network of the bogie systemZIn (V, E, R), we convert element "1" in the train bogie system component coupling relationship matrix into "x", convert element "0" into "Null", and draw a train bogie system component coupling relationship matrix diagram, as shown in fig. 2; since there may be 2 or 3 kinds of coupling relationships (connection relationships) between two or adjacent members, it is preferable that
Figure BDA0002088305500000063
The degree of the interaction and coupling relationship between the components of the bogie system of the high-speed train can be as shown in fig. 3, and since only the mechanical connection and the information connection exist between the traction motor and the speed sensor 1, the element "1" in the coupling action relationship matrix of the components of the bogie system of the train is converted into the x, the element "2/3" is converted into the o, and the element "0" is converted into the Null.
(2) On the basis of a complex network model of a high-speed train system, a potential energy field theory is combined, the propagation probability among system components is researched, the whole process of fault propagation of the high-speed train system is analyzed, and a fault propagation model of the high-speed train system is established. S02: a fault propagation model of the high-speed train bogie system is constructed, and a fault occurs in a component 16 (a traction motor). Therefore, the train bogie system 35 components are initialized to the state M0(FIG. 4) and the state W in which each component of the system is affected by a fault when component 16 fails0Solving is carried out by substituting the propagation probability model to obtain the fault propagation probability between adjacent nodes (the first step propagation probability between system nodes), and a system incidence matrix I is constructed1As shown in fig. 5, the x-axis represents the component where the failure starts to propagate, the y-axis represents the affected component, and the z-axis represents the propagation probability between adjacent components.
W0＝(01 L 116 L 035)
Initial state M of high-speed train bogie system0Initial fault affected state W0And system incidence matrix I1Substituting into the fault propagation model SkPerforming state transitions and iterative changes of system components, and performing propagation termination condition determination (herein 10)-8As a propagation threshold), the high-speed train bogie system finally obtained by simulation propagates all possible paths, as shown in table 2 and fig. 6. In fig. 6, component 16 (traction motor) suddenly fails (marked green), causing degradation of the performance of its associated component, bySimulating to find that the component 1 (framework assembly) and the component 13 (coupling) reach a failure state firstly, and marking the components as yellow; propagation probability on the fault propagation link at this time
Figure BDA0002088305500000071
Greater than 10-8Propagation continues.
TABLE 2 simulation results of fault propagation analysis of high-speed train bogie system
Figure BDA0002088305500000072
Figure BDA0002088305500000081
Subsequent simulations with this were performed, finding that component 5 (pressure cylinder), component 6 (spring component), component 17 (height adjustment device), component 19 (air spring), component 21 (drag link) and component 14 (gearbox component) first reached a failure state, which was marked orange; propagation probability on the fault propagation link at this time
Figure BDA0002088305500000082
Greater than 10-8Propagation continues. When the third transmission is performed, the component 2 (brake caliper), the component 5 (pressure cylinder), the component 7 (axle box), the component 8 (primary vertical damper), the component 10 (wheel), the component 11 (axle), the component 15 (grounding device), the component 25 (main air pipe), the component 27 (speed sensor 2) and the component 32 (gearbox bearing temperature sensor) reach a fault state, and the transmission probability on the fault transmission link is at this time
Figure BDA0002088305500000083
Has been less than 10-8Therefore, the propagation of the fault of the bogie system of the high-speed train is terminated, and the bogie system fails.
On the basis of a high-speed train system complex network model G (V, E, R), a high speed train system complex network model G (V, E, R) is established according to a distribution diffusion principle by combining a change rule of a component stateFault propagation model S of fast train systemk。
Sk＝<V,E,R,Mk,Wk,Ik> (2)
Wherein, IkThe incidence matrix of the system network model after the k-th fault propagation of the high-speed train system is expressed as
Figure BDA0002088305500000084
PijSlave node v representing failure trendiPropagation to node vjThe probability of (c).
WkIndicating the condition of each component affected by the fault.
MkThe state of the system after the k-th fault propagation of the high-speed train system is shown, and the state is formed by the current state of each component in the system. And (4) formulating a change rule of the state of each component in the system according to a distributed diffusion principle and by combining a state transition iteration method of the components in the system. Thus, MkCan be expressed as
Figure BDA0002088305500000085
W0The state composition of each component of the system affected by the fault when a certain component is in fault can be expressed as
W0＝(01 L 1i L 0n) (5)
M0The initial state of the high-speed train system is represented and is composed of the initial states of all components in the system and can be represented as
Figure BDA0002088305500000091
M(l)kRepresenting the state of the propagated and marked node on the ith fault propagation path after the k fault propagation of the system occursAnd (4) collecting.
The symbol "·" is defined as:
if A is equal to Rm×nAnd B ∈ R1×nThen, there are:
Figure BDA0002088305500000092
(symbol)
Figure BDA0002088305500000093
is defined as:
if A is equal to Rm×nAnd B ∈ R1×nThen, there are:
Figure BDA0002088305500000094
(symbol)
Figure BDA0002088305500000095
is defined as:
if A is equal to Rm×nAnd B ∈ R1×nThen, there are:
Figure BDA0002088305500000101
propagation probability on the first fault propagation link of bogie system
Figure BDA0002088305500000102
And when the fault propagation is less than a certain threshold value, the initial node fault is terminated, the node state on the link stops the iterative process, and the first fault propagation path and the states of the bogie system and the components at the moment are obtained.
The fault propagation probability among the system components in the first step is P (1), and when the system fault propagates to the k step, the fault propagation probabilities among all the components on the path P (1), P (2), …, P (k) and the corresponding system fault propagation link { v (v) } v1，v2，…，vk}。
Fault propagation model S in high-speed train systemkIn (1), how to characterize the propagation probability P between system components (nodes) is crucialij. Therefore, we define by means of the theory of viral transmission
Figure BDA0002088305500000103
Wherein, PijRepresenting virus slave node viPropagation to node vjThe probability of (d);
Figure BDA0002088305500000104
representing a virus node viNode v adjacent theretojAverage propagation rate in between; lambda [ alpha ]iRepresenting a virus node viThe course time of a node can be defined as the average repair time of a high-speed train system component.
However, in the virus propagation literature, the definition and solution of the average propagation rate between nodes is very difficult, and is usually obtained through experience and statistical data, and the state attribute of each virus node is not involved. In order to solve the problem, a potential energy field-based high-speed train system fault propagation model is provided on the basis of a virus propagation model by analogy of the law of conversion and conservation of energy in an energy field theory in physics.
The concept of fields is also mentioned in biology, mechanical engineering, materials science and behavioral risk studies, in addition to being widely applied within the scope of physics. The field is a basic form of existence of a substance, has related attribute characteristics of energy, momentum, mass and the like, and can transmit interaction between real objects, and a part of physical quantity in space or time can be called as the field.
Each part of the high-speed train system can generate faults which exist objectively, can be propagated and evolved according to a certain relation among the parts and has a certain space-time characteristic. Therefore, we analogize fault divergence in high speed train system components to fields.
According to the potential field theory, we define the potential energy E of an object i in a potential energy field formed by two objects i and jiCan be expressed as
Figure BDA0002088305500000111
Wherein G is a constant parameter; m isi、mjThe mass of objects i and j, respectively; a isiPropagating the acceleration of motion for object i; h isiIs the propagation displacement of object i; r isijIs the distance between objects i and j.
We define that the object l is located at the midpoint of the line formed by the objects i and j, and then the object l receives the repulsive force from the object i and the attractive force from the object j at the midpoint of the line formed by the objects i and j, and then the velocity at this time can be calculated according to the energy conservation law formula:
Figure BDA0002088305500000112
wherein m islIs the mass of the object l;
Figure BDA0002088305500000113
the speed of the object l moving to the midpoint position on the connecting line of the object i and the object j in a static state at the object i; x is half the distance between objects i and j, i.e.
Figure BDA0002088305500000114
Thus, the velocity of object l at the midpoint between objects i and j can be calculated as
Figure BDA0002088305500000115
Therefore, propagation probability in fault propagation model of high-speed train systemPijIs composed of
Figure BDA0002088305500000116
In conclusion, a complete fault propagation model S of the high-speed train system is obtainedkThe method is used for simulating and analyzing the whole process of fault propagation of the high-speed train system and identifying all paths of fault propagation.
(3) On the basis of a fault propagation model of the high-speed train system, the change condition of the index is analyzed by combining the complex network characteristic index-network efficiency of the high-speed train system, and the reliability of the high-speed train system is analyzed on the basis. S03: through the whole fault propagation process of the high-speed train bogie system, the change condition of the network efficiency of the high-speed train system at each step in the fault propagation process is analyzed, and then the reliability of the high-speed train bogie system is analyzed, as shown in fig. 7. In the Y-axis of fig. 7, which is the initial propagation state of the system, the reliability of the system is calculated as the ratio of the network efficiency after removing the node 16 when the component 16 (traction motor) suddenly fails to the network efficiency when 35 nodes of the network completely exist, i.e. the reliability of the system at this time; the network efficiency of the subsequent first-step propagation until the propagation is terminated (the third-step propagation) and the network efficiency when 35 nodes of the network completely exist are the system reliability of each step. As can be seen from fig. 7, the reliability of the system when the component 16 (traction motor) suddenly fails is 0.899, which falls within the range that can ensure the normal operation of the train; and when the fault propagates and spreads, the number of fault components is gradually increased until the propagation is terminated, and the system has 19 components in total which are in fault and completely fails.
In the step (3), the learners analyze the reliability of the complex network under the destructive action of a certain attack strategy by quantitatively describing the survivability index of the network system. Network efficiency is the ratio of the reciprocal of the shortest time taken for a path between any two points in the network to all paths between nodes in the network, i.e. the ratio
Figure BDA0002088305500000121
Wherein d isijRepresenting the shortest time taken for a path between any two nodes in the network.
On the basis of the fault propagation model of the high-speed train system, the network efficiency of the high-speed train system is defined as
Figure BDA0002088305500000122
Efk(G) The network efficiency of the whole high-speed train system after the k-th fault propagation of the system is shown;
Figure BDA0002088305500000123
represents the shortest time taken by a path between any two nodes after the k-th fault propagation of the system occurs, where
Figure BDA0002088305500000124
The degree of mutual influence and coupling relation among the components of the high-speed train system can be calculated.
The definition of reliability refers to the possibility that a component, product, system performs or maintains a specified function for a certain time, under certain conditions, without failure. The effectiveness of the network is one of the measure indexes reflecting the reliability of the network, and the effectiveness of the network can be the ratio of the network efficiency after the network receives the attack to the initial network efficiency. Thus in a high speed train system, we define the train system reliability: when a certain part of the train system fails, the function of the part coupled with the part is degraded, and the effect of fault propagation is generated until the whole time period that the function of the high-speed train system is failed, the network effectiveness of the high-speed train system changes. Therefore, we can calculate the reliability of the high-speed train system as
Figure BDA0002088305500000131
Therefore, through the system fault propagation process, the reliability change condition of the high-speed train system from the tiny component fault to the whole system failure process can be analyzed step by step.

Claims (1)

1.基于势能场与网络效率的高速列车系统可靠性分析的方法，其特征在于，该方法包括如下步骤：1. the method for reliability analysis of high-speed train system based on potential energy field and network efficiency, is characterized in that, this method comprises the steps: (1)建立以转向架为节点、部件之间的耦合关系为连接边的高速列车系统复杂网络模型；(1) Establish a complex network model of the high-speed train system with the bogie as the node and the coupling relationship between the components as the connecting edge; (2)在所述高速列车系统复杂网络模型的基础上，结合势能场理论，建立高速列车系统故障传播模型，得到系统故障每步传播失效部件；(2) On the basis of the complex network model of the high-speed train system, combined with the potential energy field theory, a fault propagation model of the high-speed train system is established, and the failure components of each step of the system fault propagation are obtained; (3)在所述高速列车系统故障传播模型的基础上，结合高速列车系统网络效率，分析高速列车系统可靠性；(3) On the basis of the high-speed train system fault propagation model, combined with the high-speed train system network efficiency, analyze the high-speed train system reliability; 步骤(1)中in step (1) 高速列车系统由n个部件组成，部件之间存在m条连接边，建立高速列车系统复杂网络模型The high-speed train system consists of n components, and there are m connecting edges between the components, and a complex network model of the high-speed train system is established G＝(V,E,R)G=(V,E,R) 其中：V＝(v1,v2,…,vj,…,vn)表示网络n个节点的集合，其属性取决于部件的故障状态；E＝(e12,…,eij,…,ern)是网络m条连接边的集合，表示转向架系统部件之间是否存在机械、电子、信息及控制的相互影响和耦合关系，R＝(r12,…,rij,…,rrn)表示高速列车系统部件之间相互影响和耦合关系的程度；Where: V=(v 1 , v 2 ,...,v j ,...,v n ) represents the set of n nodes in the network, whose properties depend on the fault state of the components; E=(e 12 ,...,e ij ,... ,e rn ) is the set of m connecting edges of the network, indicating whether there is the mutual influence and coupling relationship between mechanics, electronics, information and control among the bogie system components, R=(r 12 ,...,r ij ,...,r rn ) represents the degree of mutual influence and coupling relationship between the components of the high-speed train system; 步骤(2)中in step (2) 建立高速列车系统故障传播模型Sk Establishment of fault propagation model Sk for high-speed train system Sk＝<V,E,R,Mk,Wk,Ik>S k =<V,E,R,M k ,W k ,I k > 其中，Ik表示高速列车系统发生第k步故障传播后系统网络模型的关联矩阵，Wk表示高速列车系统发生第k步各个部件受到故障影响的状态，Mk表示高速列车系统发生第k步故障传播后的系统状态；Among them, I k represents the correlation matrix of the system network model after the failure propagation of the high-speed train system at the k-th step, W k represents the state of each component affected by the fault at the k-th step of the high-speed train system, and M k represents the k-th step of the high-speed train system. System state after fault propagation; Mk表示为 Mk is expressed as
Figure FDA0003036162830000011
Figure FDA0003036162830000011
 M(l)k表示高速列车系统发生第k步故障传播后第l条故障传播路径上被传播标记节点的系统状态集合，W(l)k-1表示高速列车系统发生第k-1步故障传播后第l条故障传播路径上被传播标记节点的受到故障影响的状态集合，M(l) k represents the system state set of the propagated marked nodes on the l-th fault propagation path after the k-th fault propagation occurs in the high-speed train system, and W(l) k-1 represents the k-1 fault in the high-speed train system The set of states affected by the fault of the propagated marked nodes on the lth fault propagation path after propagation, 符号“·”的定义为：The symbol "·" is defined as: 若A∈Rm×n，且B∈R1×n，则有：If A∈R m×n and B∈R 1×n , then:
Figure FDA0003036162830000021
Figure FDA0003036162830000021
 符号
Figure FDA0003036162830000022
的定义为：symbol
Figure FDA0003036162830000022
is defined as: 若A∈Rm×n，且B∈R1×n，则有：If A∈R m×n and B∈R 1×n , then:
Figure FDA0003036162830000023
Figure FDA0003036162830000023
 符号
Figure FDA0003036162830000024
的定义为：symbol
Figure FDA0003036162830000024
is defined as: 若A∈Rm×n，且B∈R1×n，则有：If A∈R m×n and B∈R 1×n , then:
Figure FDA0003036162830000025
Figure FDA0003036162830000025
 其中，A是运算符号“·”、
Figure FDA0003036162830000026
以及
Figure FDA0003036162830000027
运算定义时的辅助参数，表示一个n行n列的矩阵，其中，a11，a12，a21，a22，a1n，a2n，an1，an2，ann分别表示矩阵A中不同位置的元素；Among them, A is the operator symbol "·",
Figure FDA0003036162830000026
as well as
Figure FDA0003036162830000027
Auxiliary parameters in the operation definition, which represent a matrix with n rows and n columns, where a 11 , a 12 , a 21 , a 22 , a 1n , a 2n , a n1 , a n2 , and a nn represent different values in the matrix A, respectively element of position; B是运算符号“·”、
Figure FDA0003036162830000028
以及
Figure FDA0003036162830000029
运算定义时的辅助参数，表示一个1行n列的矩阵，其中，b1，b2，bn分别表示矩阵B中不同位置的元素；B is the operator symbol "·",
Figure FDA0003036162830000028
as well as
Figure FDA0003036162830000029
Auxiliary parameters in the operation definition, representing a matrix with 1 row and n columns, where b 1 , b 2 , and b n respectively represent elements at different positions in matrix B; 高速列车系统故障传播模型中的传播概率Pij为The propagation probability P ij in the high-speed train system fault propagation model is
Figure FDA0003036162830000031
Figure FDA0003036162830000031
 其中，Pij表示病毒从节点vi传播至节点vj的概率；
Figure FDA0003036162830000032
表示部件i与部件j之间的平均传播速率；λi表示病毒节点vi的病程时间，G为常量参数；mi、mj分别为物体i与j的质量，rij为物体i和j之间的距离；Among them, P ij represents the probability that the virus spreads from node v i to node v j ;
Figure FDA0003036162830000032
represents the average propagation rate between component i and component j; λ i represents the disease course time of virus node v i , G is a constant parameter; m i and m j are the masses of objects i and j, respectively, and r ij are objects i and j the distance between; 当高速列车系统第l条故障传播链路上的传播概率
Figure FDA0003036162830000033
小于一定阈值时，初始节点故障终止传播，链路上节点状态停止迭代过程，得到第l条故障传播路径及其此时的高速列车系统与部件的状态；When the propagation probability on the lth fault propagation link of the high-speed train system
Figure FDA0003036162830000033
When it is less than a certain threshold, the initial node failure terminates the propagation, the node state on the link stops the iterative process, and the lth fault propagation path and the state of the high-speed train system and components at this time are obtained; 步骤(3)中In step (3) 所述高速列车系统网络效率为The network efficiency of the high-speed train system is
Figure FDA0003036162830000034
Figure FDA0003036162830000034
 Efk(G)表示系统发生第k步故障传播后整个高速列车系统网络效率；其中f表示系统已经受到故障影响；
Figure FDA0003036162830000035
表示系统发生第k步故障传播后任意两节点间的通路所用的最短时间；E0(G)表示为正常条件下的高速列车系统网络效率；E fk (G) represents the network efficiency of the entire high-speed train system after the k-th step fault propagation occurs in the system; where f represents that the system has been affected by the fault;
Figure FDA0003036162830000035
Represents the shortest time for the path between any two nodes after the kth step of fault propagation occurs in the system; E 0 (G) represents the network efficiency of the high-speed train system under normal conditions; 计算高速列车系统可靠性Rk(G)为Calculate the reliability R k (G) of the high-speed train system as
Figure FDA0003036162830000036
Figure FDA0003036162830000036
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CN112345276B (en) * 2020-11-10 2022-03-15 北京交通大学 A state assessment and prediction system for key components of medium-speed maglev trains
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CN112766509A (en) * 2021-01-18 2021-05-07 合肥工业大学 Method for analyzing fault propagation path of electronic information system

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8700958B2 (en) * 2006-08-11 2014-04-15 Riverbed Technology, Inc. Multi-variate network survivability analysis
CN104392071A (en) * 2014-12-12 2015-03-04 北京交通大学 High-speed train system security evaluation method based on complex network
CN104992011A (en) * 2015-06-24 2015-10-21 东北大学 FMEA analysis based comprehensive reliability allocation method for numerical control machine tool
CN105760589A (en) * 2016-02-03 2016-07-13 北京交通大学 Reliability analyzing method based on high-speed train system action relation network
CN107846307A (en) * 2017-10-13 2018-03-27 天津大学 A kind of control method being used for information physical system fault propagation
CN108269211A (en) * 2016-12-30 2018-07-10 北京金风科创风电设备有限公司 The determining method and apparatus of the reliability index of wind power generating set system
CN109104328A (en) * 2018-06-14 2018-12-28 北京航空航天大学 A kind of group system reliability assessment method based on complex network
CN109544011A (en) * 2018-11-26 2019-03-29 北京交通大学 Bullet train system reliability estimation method based on inside and outside covariant
CN109543248A (en) * 2018-11-02 2019-03-29 北京交通大学 The generation method in the train system fault propagation path based on failure potential energy field

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8959006B2 (en) * 2006-03-10 2015-02-17 Power Analytics Corporation Systems and methods for automatic real-time capacity assessment for use in real-time power analytics of an electrical power distribution system

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8700958B2 (en) * 2006-08-11 2014-04-15 Riverbed Technology, Inc. Multi-variate network survivability analysis
CN104392071A (en) * 2014-12-12 2015-03-04 北京交通大学 High-speed train system security evaluation method based on complex network
CN104992011A (en) * 2015-06-24 2015-10-21 东北大学 FMEA analysis based comprehensive reliability allocation method for numerical control machine tool
CN105760589A (en) * 2016-02-03 2016-07-13 北京交通大学 Reliability analyzing method based on high-speed train system action relation network
CN108269211A (en) * 2016-12-30 2018-07-10 北京金风科创风电设备有限公司 The determining method and apparatus of the reliability index of wind power generating set system
CN107846307A (en) * 2017-10-13 2018-03-27 天津大学 A kind of control method being used for information physical system fault propagation
CN109104328A (en) * 2018-06-14 2018-12-28 北京航空航天大学 A kind of group system reliability assessment method based on complex network
CN109543248A (en) * 2018-11-02 2019-03-29 北京交通大学 The generation method in the train system fault propagation path based on failure potential energy field
CN109544011A (en) * 2018-11-26 2019-03-29 北京交通大学 Bullet train system reliability estimation method based on inside and outside covariant

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
A systematic modeling approach for failure indicators of complex software-intensive systems;Luyi Li等;《IEEE Xplore》;20190523;第43-51页 *
Internet 级联动力学分析与建模;王健等;《Journal of Software》;20100831;第21卷(第8期);第2050-2058页 *
非自然灾害下基础设施系统关联可靠性分析;刘文波;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20190215;第2019年卷(第02期);第C038-1064页的第3.3节 *

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