CN112257176B - Analysis method for fault propagation of urban rail train system - Google Patents
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Abstract
The embodiment of the invention provides a method for analyzing fault propagation of a urban rail train system based on a disaster propagation dynamic model, which comprises the following steps: step S110, constructing a topology network model of a urban rail train system; step S120, dividing a network community structure for the urban rail train system according to a topological network model of the urban rail train system; step S130, determining service life distribution and fault probability of each node in a topological network of the urban rail train system; step S140: and constructing a fault propagation model of the urban rail train system based on the disaster propagation dynamics model by combining the network community structure of the urban rail train system and the fault probability of the nodes, and analyzing the possible fault propagation state of the urban rail train system.
Description
Technical Field
The invention relates to the field of traffic, in particular to an analysis method for fault propagation of a urban rail train system.
Background
Urban rail trains are used as core vehicles of urban rail transit systems and are complex electromechanical systems with huge component scale and complex interaction relationship. Due to the working mechanism of tight connection among components in the urban rail train system, failure of any one component not only can cause self failure of the component, but also can influence the states of other components, thereby causing abnormal functions of the whole system and finally causing serious consequences. Therefore, the fault propagation process of the research component in the system is of great importance for maintaining the safe and reliable operation of the urban rail train system.
The visual display of the topological structure of the system through the network is a common method for researching a complex electromechanical system such as a urban rail train system, and is helpful for exploring the propagation process of faults in the system. Existing topology network-based system fault propagation analysis methods can be broadly divided into two categories: modeling methods based on node propagation intensity and modeling methods based on propagation dynamics. The modeling method based on the node propagation intensity generally provides the node propagation intensity according to the principle of stepwise propagation, so that the propagation process of faults in a network is researched, the method considers the influence of a topological structure on the propagation, but the method has the defect that only a unique path with the maximum occurrence probability in the network can be analyzed, and multiple possible propagation results of the same component cannot be considered at the same time. Whereas modeling methods based on propagation dynamics generally divide nodes into three types, normal, abnormal and infected states, and simulate fault propagation as that of viruses, the method has the disadvantage that the influence of network heterogeneity on propagation is ignored, and most models do not consider the difference of community structures. Second, the classical virus propagation model believes that the risk of transmission is at once, without considering the regulatory capability of the node itself.
Disclosure of Invention
The embodiment of the invention provides an analysis method for fault propagation of a urban rail train system, which can accurately reflect the propagation condition of faults in the system and provides references for urban rail train maintenance and repair staff.
An analysis method for fault propagation of urban rail train system based on a disaster propagation dynamic model,
step S110, constructing a topology network model of a urban rail train system;
step S120, dividing a network community structure for the urban rail train system according to a topological network model of the urban rail train system;
step S130, determining service life distribution and fault probability of each node in a topological network of the urban rail train system;
step S140: and constructing a fault propagation model of the urban rail train system based on the disaster propagation dynamics model by combining the network community structure of the urban rail train system and the fault probability of the nodes, and analyzing the possible fault propagation condition of the urban rail train system.
According to the technical scheme provided by the embodiment of the invention, the transmission condition of the fault in the system can be accurately reflected, the safety and reliability of the urban rail train system can be improved, and references are provided for urban rail train maintenance and repair staff.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an analysis method of fault propagation of a urban rail train system based on a disaster propagation dynamics model according to an embodiment of the present invention;
FIG. 2 is a diagram of a topological network model of a bogie system of a urban rail train system according to an embodiment of the invention;
fig. 3 is a schematic diagram of a topology network community structure of a bogie system of a urban rail train according to an embodiment of the present invention;
fig. 4 is a schematic diagram of failure probability of a bogie system traction motor of a urban rail train system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a possible fault propagation condition of the bogie system of the urban rail train system according to the embodiment of the present invention when the operation mileage is 40 ten thousand kilometers.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The invention provides a city rail train system fault propagation analysis method based on a disaster propagation dynamics model, and belongs to the technical field of city rail traffic safety. Firstly, constructing a topological network model of a urban rail train system; then dividing a network community structure according to the topological network model of the urban rail train system; determining the fault probability of each component in the topological network of the urban rail train system; and finally, constructing a urban rail train system fault propagation model based on the disaster propagation dynamics model by combining the urban rail train system network community structure and the fault probability of the nodes, and analyzing the possible fault propagation condition of the urban rail train system by using the system fault propagation model.
The method comprehensively considers factors such as the difference of community structures in the topological network of the urban rail train system, the failure rate of the nodes, the repair capability, the failure threshold value and the like, is based on a disaster propagation dynamic model, and provides a method for describing the failure propagation process of the urban rail train system based on the disaster propagation dynamic model, so that possible failure conditions of the urban rail train system can be analyzed, and theoretical support is provided for train maintenance and repair staff.
As shown in fig. 1, the method for analyzing the fault propagation of the urban rail train system based on the disaster propagation and propagation dynamics model provided by the invention comprises the following steps:
step S110: and constructing a topological network model of the urban rail train system.
Step S120: and dividing a network community structure according to the topological network model of the urban rail train system.
Step S130: determining service life distribution and fault probability of each node in a topological network of the urban rail train system;
step S140: and constructing a city rail train system fault propagation model based on the disaster propagation dynamics model by combining the network community structure of the city rail train system and the fault probability of the nodes, and analyzing fault propagation paths under different operation mileage by using the city rail train system fault propagation model.
Preferably, the step S110 specifically includes:
the topological network model G (N, E) of the urban rail train system is composed of a component N in the system i (n i E, N) is taken as a node, and the mechanical, electrical and information connection relation e between the components is used ij (e ij E) is a connecting edge. The constructed topological network model of the urban rail train system is an undirected and unauthorized network for characterizing the structural characteristics of the urban rail train system.
Preferably, the step S120 specifically includes:
according to a topological network model G (N, E) of the urban rail train system, a Fast-Newman (FN) algorithm is applied, and a network community structure is divided based on a Modularity measure. The Modularity measure is:
wherein e ii Is community C i Inter-node connection in a networkThe number of edges is a percentage of the number of all connected edges in the network, a i For the network and community C i The number of linked edges of the internal node is a percentage of the number of all connected edges in the network.
Preferably, the step S130 specifically includes:
based on urban rail train system operation fault data, using Minitab 17 software to fit the service lives of the components by adopting a least square method, and calculating the fault probability lambda of each node under different service life distribution types i (l) A. The invention relates to a method for producing a fibre-reinforced plastic composite The failure probability lambda under different life distribution types i (l) Comprising the following steps:
if the service life of the component obeys the exponential distribution, the failure probability of the component is lambda when the operation mileage is l.
If the service life of the component is subject to two-parameter Weibull distribution, the failure probability of the component when the operation mileage is l is:
wherein m is the shape parameter of the two-parameter Weibull distribution, and eta is the proportion parameter of the two-parameter Weibull distribution.
If the service life of the component is subject to three-parameter Weibull distribution, the failure probability of the component when the operation mileage is l is:
wherein m is the shape parameter of the three-parameter Weibull distribution, eta is the proportion parameter of the three-parameter Weibull distribution, and gamma is the threshold parameter of the three-parameter Weibull distribution.
If the service life of the component is subject to normal distribution, the failure probability of the component when the operation kilometer is l is:
wherein μ is a normally distributed position parameterIs a normally distributed scale parameter.
Preferably, the step S140 specifically includes:
it is assumed that a component in a fault state will transmit a quantifiable risk to other components, i.e. the fault is in the form of a risk that can propagate to other components through the physical connection between the components. By F k For node v k A fault threshold of (2); by X k (l) Node v when operating mileage is l k The accumulated risk amount; all components in the system are divided into normal and fault two states:
if X a (l) When the operation mileage is l, the node v is described as a There is no risk, nor is there any risk of transmission to the outside, in a normal state. If 0 is<X a (l)≤F a Then it is explained that when the operation mileage is l, the node v a A certain risk is accumulated but the fault threshold F is not exceeded a . At this time, node v a Is also in a normal state, and does not risk outward propagation.
If X a (l)>F a Then it is explained that when the operation mileage is l, the node v a The accumulated risk exceeds the failure threshold of itself. At this time, node v a In a fault state, there is a possibility of an outbound propagation risk.
According to the fault propagation mechanism, the urban rail train system fault propagation model constructed according to the disaster propagation dynamics model is as follows:
wherein τ a For node v a Self-repairing factor C of (C) i For node v a F to which it belongs a Ith community, Γ a For node v a Is defined by a set of neighboring nodes of the network,for node v b And community C i The number of connection edges between other nodes in the interior, < >>For node v c And community C i The number of connecting edges lambda between other nodes a (l) When the operation mileage is l ten thousand kilometers, the node v a Failure rate of the device itself.
Preferably, the function Θ (x) in step S140 is a Sigmoid function:
where α is the gain parameter.
Preferably, in the step S140, the function f (x) is defined as:
wherein a and b are constants.
Preferably, exp (- η) in the step S140 is a loss function in the fault propagation process, η is the propagation strength, and the smaller the value, the less the risk is.
The invention has the beneficial effects that:
the invention takes factors such as community structure difference in a topological network of the urban rail train system, failure rate of the nodes and the like into consideration, builds the urban rail train system failure propagation model based on disaster propagation dynamics, provides a method for analyzing the failure propagation of the urban rail train system, and provides theoretical support for train maintenance and repair staff.
The following describes embodiments of the invention
According to the embodiment of the invention, a steering frame system of a certain urban rail B-type vehicle is used for explaining a fault propagation analysis method of an urban rail train system based on a disaster propagation dynamics model.
Step 1: with 35 parts of the bogie system as nodes n i E N (shown in Table 1), the three connection relations (mechanical, electrical, information) between the components are connection edges e ij E, building a topological network model G (N, E) of the bogie system of the urban rail train system, as shown in fig. 2.
Table 1 bogie system components list
Step 2: according to a topological network model G (N, E) of a bogie system of a urban rail train system, a Fast-Newman (FN) algorithm is applied, and the method is based on a Modularity measureThe network community structure is divided, and the result is shown in fig. 3.
Step 3: fitting the service lives of the components by using Minitab 17 software and adopting a least square method based on urban rail train system operation fault data to obtain the service life distribution type of each node and corresponding parameters, and calculating the fault probability lambda of each component according to the service life distribution type i (l) A. The invention relates to a method for producing a fibre-reinforced plastic composite If the traction motor of the bogie system obeys two-parameter Weibull distribution, each parameter is as eta=36.9972 and m= 2.73654, and the fault probability of the traction motor of the bogie system is given in fig. 4.
Step 4: and constructing a urban rail train system fault propagation model based on the disaster spread propagation dynamics model by combining the urban rail train system network community structure and the fault probability of the nodes.
Wherein each parameter takes the values of τ=4, a=4, b=3, α=10, o 1 ＝0.6，o 2 =0.4, η=0.01; table 2 gives the component failure threshold F expert scoring table.
TABLE 2 part failure threshold F expert scoring Table
With node v in the bogie system 26 (rotating arm shaft box) as a transmission source point, setting the initial risk amount of the transmission source point to be 0.8, simulating the risk amount change of each node through Matlab, and when the operation mileage is 40 ten thousand kilometers, setting the possible fault transmission path to be v 26 →v 16 ，v 26 →v 15 And v 26 →v 24 →v 22 As shown in fig. 5.
In summary, according to the embodiment of the invention, the urban rail train system fault propagation model based on the disaster propagation dynamics model is established by comprehensively considering factors such as the difference of community structures in the topological network of the urban rail train system, the fault rate, the repair capability and the fault threshold value of the nodes, so that the propagation condition of the fault in the system can be accurately reflected, the safety and the reliability of the urban rail train system can be improved, and references are provided for urban rail train maintenance and repair staff.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. A method for analyzing fault propagation of a urban rail train system, characterized by comprising:
step S110, constructing a topology network model of a urban rail train system; the method specifically comprises the following steps:
the topology network model G (N, E) of the urban rail train system is composed of a component N in the system i (n i E, N) is taken as a node, and the mechanical, electrical and information connection relation e between the components is used ij (e ij E) is a connecting edge, and constructed urban railThe topological network model of the train system is an undirected and unauthorized network for characterizing the structural characteristics of the urban rail train system;
step S120, dividing a network community structure for the urban rail train system according to a topological network model of the urban rail train system; the method specifically comprises the following steps:
according to a topological network model G (N, E) of the urban rail train system, a Fast-Newman (FN) algorithm is applied, and a network community structure is divided based on a Modularity measure;
the Modularity measure is:
wherein e ii Is community C i The number of connecting edges between nodes in the network is a percentage of the number of all connecting edges in the network, a i For the network and community C i The number of the edges with links of the inner node accounts for a percentage of the number of all the connected edges in the network;
step S130, determining service life distribution and fault probability of each node in a topological network of the urban rail train system; the method specifically comprises the following steps:
based on the operation fault data of the urban rail train system, the Minitab 17 software is utilized to fit the service lives of the components by adopting a least square method, and the fault probability lambda of each node under different service life distribution types is calculated i (l)；
The failure probability lambda under different life distribution types i (l) Comprising the following steps:
if the service life of the component obeys the exponential distribution, the failure probability of the component is lambda when the operation mileage is l;
if the service life of the component is subject to two-parameter Weibull distribution, the failure probability of the component when the operation mileage is l is:
wherein m is the shape parameter of the two-parameter Weibull distribution, and eta is the proportion parameter of the two-parameter Weibull distribution;
if the service life of the component is subject to three-parameter Weibull distribution, the failure probability of the component when the operation mileage is l is:
wherein m is the shape parameter of the three-parameter Weibull distribution, eta is the proportion parameter of the three-parameter Weibull distribution, and gamma is the threshold parameter of the three-parameter Weibull distribution;
if the service life of the component is subject to normal distribution, the failure probability of the component when the operation kilometer is l is:
wherein mu is a position parameter of normal distribution, and sigma is a scale parameter of normal distribution;
step S140, constructing a fault propagation model of the urban rail train system based on a disaster propagation dynamics model by combining the network community structure of the urban rail train system and the fault probability of the nodes, and analyzing the possible fault propagation state of the urban rail train system; the method specifically comprises the following steps:
assuming that the component in the fault state transmits quantifiable risks to other components, namely that the fault is propagated to other components in the form of risks through the physical connection relation among the components;
by F k For node v k A fault threshold of (2); by X k (l) Node v when operating mileage is l k The accumulated risk amount; all components in the system are divided into normal and fault two states:
if X a (l) When=0, it indicates that the operation mileage is l, node v a The risk does not exist, the risk of outward transmission is avoided, and the system is in a normal state;
if 0 is<X a (l)≤F a When the operation mileage is l, the operation mileage is represented,node v a Accumulate a risk amount but do not exceed the failure threshold F a The method comprises the steps of carrying out a first treatment on the surface of the At this time, node v a The device is also in a normal state, and does not have risk of outward transmission;
if X a (l)>F a If the operation mileage is l, the node v a The accumulated risk exceeds the fault threshold F of the self a The method comprises the steps of carrying out a first treatment on the surface of the At this time, node v a In a fault state, the possibility of outward propagation risk exists;
according to the fault propagation mechanism, the urban rail train system fault propagation model constructed by utilizing the disaster propagation dynamics model is as follows:
wherein τ a For node v a Self-repairing factor C of (C) i For node v a The i-th community to which the game belongs, Γ a For node v a Is defined by a set of neighboring nodes of the network,for node v b And community C i The number of connection edges between other nodes in the interior, < >>For node v c And community C i The number of connecting edges lambda between other nodes a (l) When the operation mileage is l ten thousand kilometers, the node v a Failure rate of itself, o 1 、o 2 And respectively representing constants of influence degrees of the nodes on different types of neighbor nodes in the same community or different communities, wherein a function f (x) in the formula is used for reflecting the influence degrees of the neighbor nodes in the different communities.
2. The method according to claim 1, wherein the function Θ (x) in step S140 is a Sigmoid function:
where α is the gain parameter.
3. The method according to claim 1, wherein in the step S140, the function f (x) is defined as:
wherein a and b are constants.
4. The method according to claim 1, wherein exp (- η) is a loss function during fault propagation in the step S140, η is a propagation intensity, and the smaller the value, the smaller the risk loss.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103683473A (en) * | 2013-12-11 | 2014-03-26 | 华为技术有限公司 | Three-bridge-arm topology circuit, control method and uninterrupted power supply system |
| CN103944412A (en) * | 2014-04-14 | 2014-07-23 | 华为技术有限公司 | Control method and device for UPS and UPS |
| CN106602888A (en) * | 2016-11-30 | 2017-04-26 | 漳州科华技术有限责任公司 | Three-bridge-arm topology circuit, control method and uninterruptible power supply |
| WO2018107619A1 (en) * | 2016-12-15 | 2018-06-21 | 广东百事泰电子商务股份有限公司 | Pfc and llc resonance-based intelligent full-bridge sine-wave voltage conversion circuit |
| CN108304960A (en) * | 2017-12-29 | 2018-07-20 | 中车工业研究院有限公司 | A kind of Transit Equipment method for diagnosing faults |
| CN108536123A (en) * | 2018-03-26 | 2018-09-14 | 北京交通大学 | The method for diagnosing faults of the train control on board equipment of the long neural network of memory network combination in short-term |
| CN111242357A (en) * | 2020-01-06 | 2020-06-05 | 北京锦鸿希电信息技术股份有限公司 | Neural network learning-based train-mounted equipment fault prediction method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11816552B2 (en) * | 2017-10-26 | 2023-11-14 | International Business Machines Corporation | Dynamically reconfigurable networked virtual neurons for neural network processing |
-
2020
- 2020-10-19 CN CN202011117429.0A patent/CN112257176B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103683473A (en) * | 2013-12-11 | 2014-03-26 | 华为技术有限公司 | Three-bridge-arm topology circuit, control method and uninterrupted power supply system |
| CN103944412A (en) * | 2014-04-14 | 2014-07-23 | 华为技术有限公司 | Control method and device for UPS and UPS |
| CN106602888A (en) * | 2016-11-30 | 2017-04-26 | 漳州科华技术有限责任公司 | Three-bridge-arm topology circuit, control method and uninterruptible power supply |
| WO2018107619A1 (en) * | 2016-12-15 | 2018-06-21 | 广东百事泰电子商务股份有限公司 | Pfc and llc resonance-based intelligent full-bridge sine-wave voltage conversion circuit |
| CN108304960A (en) * | 2017-12-29 | 2018-07-20 | 中车工业研究院有限公司 | A kind of Transit Equipment method for diagnosing faults |
| CN108536123A (en) * | 2018-03-26 | 2018-09-14 | 北京交通大学 | The method for diagnosing faults of the train control on board equipment of the long neural network of memory network combination in short-term |
| CN111242357A (en) * | 2020-01-06 | 2020-06-05 | 北京锦鸿希电信息技术股份有限公司 | Neural network learning-based train-mounted equipment fault prediction method and device |
Non-Patent Citations (2)
| Title |
|---|
| 基于贝叶斯网络推理的列车可靠性评估方法;余博;;城市轨道交通研究(第04期);第23-26页 * |
| 面向城轨交通系统灾害事件网络的构建与表征;陈家旭;陈佳惠;李晓璐;赵晖;张彭;朱广宇;;综合运输(第03期);第38-42、52页 * |
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