CN111652395A - Health assessment method for high-speed railway contact network equipment - Google Patents

Health assessment method for high-speed railway contact network equipment Download PDF

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CN111652395A
CN111652395A CN202010537508.0A CN202010537508A CN111652395A CN 111652395 A CN111652395 A CN 111652395A CN 202010537508 A CN202010537508 A CN 202010537508A CN 111652395 A CN111652395 A CN 111652395A
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范国海
白文勇
郑欣
殷顺尧
何洪伟
张智钧
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Chengdu National Railways Electrical Equipment Co ltd
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Abstract

The invention discloses a health evaluation method for high-speed railway contact network equipment, which comprises the steps of establishing a contact network health evaluation index item set; collecting the operation data of the contact network and constructing a characteristic project; constructing a training set and a testing set by taking the quality evaluation as a label and taking the secondary index item as a feature dimension, and obtaining a classification model through a classification-random forest algorithm; extracting the weight ratio of each index in the secondary index items in the classification model; collecting the recorded values of the secondary indexes of each contact network device as original data; carrying out 0-1 standardization on the original data; calculating the weight ratio of each index between the secondary index items; synthesizing the weight ratios to obtain final weight ratios of all indexes in the secondary indexes; constructing gray class grades and gray clustering functions in the gray clusters; and calculating the coefficient of each gray grade to which each contact net object belongs, and taking the grade corresponding to the maximum coefficient as the health state of the contact net.

Description

Health assessment method for high-speed railway contact network equipment
Technical Field
The invention relates to the field of railways, in particular to a health assessment method for high-speed railway contact network equipment.
Background
With the rapid development of the Chinese economy, railway traffic becomes an important foundation for the development of the national economy, and an electrified railway is the development direction of the railway in the future. The contact net is the most important component in the electrified railway system, and the health assessment and the maintenance work of the contact net are very important. Meanwhile, as the contact network is exposed to the natural environment all the year round, no redundant standby equipment and the mechanical and electrical properties of the contact network are considered, once any tiny fault occurs in the contact network, the operation of the train can be affected, and huge economic loss is caused to a busy railway network trunk line.
In reality, the management work related to the overhead line system is based on maintenance performed after defective records, and a mechanism related to monitoring and feedback of the health state of the overhead line system is not established, so that the following problems exist:
firstly, most of the management and evaluation mechanisms of the existing contact network are binary, namely, a defect fault occurs and a defect fault does not occur; the change of the corresponding health degree of the contact net along with the change of time cannot be reflected.
Secondly, in a few health evaluations of the contact network equipment, the personal experience of experts is excessively depended on, the health evaluation of the contact network equipment is carried out by depending on the experience values of the experts, and the rarity and the one-sidedness of opinions of the experts in reality are not considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a health assessment method for high-speed railway contact network equipment, which comprises the following steps:
the method comprises the following steps of firstly, establishing a contact network health evaluation index item set based on a contact network system health state evaluation index system;
collecting operation data of the contact network, and constructing a characteristic project based on a secondary index;
thirdly, constructing a training set and a testing set by taking the quality evaluation as a label and taking the secondary index item as a characteristic dimension, and obtaining a classification model through a classification-random forest algorithm based on the training set and the testing set;
extracting the weight ratio of each index in the secondary index items in the classification model;
acquiring recorded values of secondary indexes of each contact network device at different kilometer mark positions on the same day, the same line, the same row and the same station as original data;
cleaning the original data by using a data cleaning mechanism, and carrying out 0-1 standardization on the cleaned data;
step seven, calculating the weight ratio of each index between the two-level index items by using the data after 0-1 standardization by using an entropy weight method;
step eight, synthesizing the weight ratios in the step four and the weight ratios in the step seven to obtain final weight ratios of the indexes in the secondary indexes;
constructing gray class grades and gray clustering functions in the gray clusters;
and step ten, calculating the coefficient of each gray grade to which each contact net object belongs by using the normalized data in the step six and the final weight ratio in the step eight through a gray clustering function, wherein the grade corresponding to the maximum coefficient is taken as the health state of the contact net.
Further, the index item set is divided into four items of first-level indexes: a safety index, a smoothness index, a current acceptance index and a network pressure; eight secondary index items: lead height, pull out, lead height difference across the interior, hard spot, off-line duration, wire slope, contact pressure, and catenary voltage.
Further, the 0-1 standardization comprises the following processes of setting n evaluation objects, m evaluation indexes and xijN, i ═ 1,2.. n; if j is 1,2.. and m is a quantized value of the evaluation target i with respect to the evaluation index j, which is a data record for each dimension index, the standardized formula is:
Figure BDA0002537563280000021
in the above formula
Figure BDA0002537563280000022
The minimum value and the maximum value of the j index of the ith evaluation object are respectively;
Figure BDA0002537563280000023
for the j index x of the ith evaluation objectijNormalized values of (d).
Further, the weight ratios of the items in the fourth step and the weight ratios of the items in the seventh step are integrated, and the integrated mode is that the index weight obtained in the fourth step accounts for 0.4; and the index weight obtained in the step seven accounts for 0.6, so that the final index weight ratio of eight secondary indexes is obtained.
Further, the gray levels include five categories of poor, general, good and good,
Figure BDA0002537563280000024
indicates that the health state of the overhead line system is 'poor';
Figure BDA0002537563280000025
indicates that the health status of the overhead line system is "poor";
Figure BDA0002537563280000026
the health status of the overhead line system is indicated as 'normal';
Figure BDA0002537563280000027
the health status of the contact net is indicated as 'better';
Figure BDA0002537563280000028
the health status of the contact net is indicated as 'good'; wherein the content of the first and second substances,
Figure BDA0002537563280000029
Figure BDA0002537563280000031
Figure BDA0002537563280000032
Figure BDA0002537563280000033
Figure BDA0002537563280000034
the invention has the beneficial effects that: in the weight construction of the contact network object index system, the problem that when the number of experts is insufficient, an AHP analytic hierarchy process excessively depends on expert experience opinions is solved, and the influence proportion of each index of the contact network on the occurrence of a defect fault is objectively and comprehensively analyzed from a large amount of historical data.
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FIG. 1 is a flow chart of a health assessment method for high-speed railway catenary equipment;
fig. 2 is a schematic diagram of an evaluation index system for the health state of the overhead line system.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, the invention provides a health assessment method for high-speed railway contact network equipment, which comprises the following steps:
the method comprises the following steps of firstly, establishing a contact network health evaluation index item set based on a contact network system health state evaluation index system;
collecting operation data of the contact network, and constructing a characteristic project based on a secondary index;
thirdly, constructing a training set and a testing set by taking the quality evaluation as a label and taking the secondary index item as a characteristic dimension, and obtaining a classification model through a classification-random forest algorithm based on the training set and the testing set;
extracting the weight ratio of each index in the secondary index items in the classification model;
acquiring recorded values of secondary indexes of each contact network device at different kilometer mark positions on the same day, the same line, the same row and the same station as original data;
cleaning the original data by using a data cleaning mechanism, and carrying out 0-1 standardization on the cleaned data;
step seven, calculating the weight ratio of each index between the two-level index items by using the data after 0-1 standardization by using an entropy weight method;
step eight, synthesizing the weight ratios in the step four and the weight ratios in the step seven to obtain final weight ratios of the indexes in the secondary indexes;
constructing gray class grades and gray clustering functions in the gray clusters;
and step ten, calculating the coefficient of each gray grade to which each contact net object belongs by using the normalized data in the step six and the final weight ratio in the step eight through a gray clustering function, wherein the grade corresponding to the maximum coefficient is taken as the health state of the contact net.
The index item set is divided into four items of first-level indexes: a safety index, a smoothness index, a current acceptance index and a network pressure; eight secondary index items: lead height, pull out, lead height difference across the interior, hard spot, off-line duration, wire slope, contact pressure, and catenary voltage.
0-1 normalization, including the procedure set with n assessmentsObject, m evaluation indices, xijN, i ═ 1,2.. n; if j is 1,2.. and m is a quantized value of the evaluation target i with respect to the evaluation index j, which is a data record for each dimension index, the standardized formula is:
Figure BDA0002537563280000041
in the above formula
Figure BDA0002537563280000042
The minimum value and the maximum value of the j index of the ith evaluation object are respectively;
Figure BDA0002537563280000043
for the j index x of the ith evaluation objecti jNormalized values of (d).
Integrating the weight ratios of the items in the fourth step and the weight ratios of the items in the seventh step, wherein the index weight obtained in the fourth step accounts for 0.4; and the index weight obtained in the step seven accounts for 0.6, so that the final index weight ratio of eight secondary indexes is obtained.
The gray levels include five major categories of poor, normal, good, and good,
Figure BDA0002537563280000051
indicates that the health state of the overhead line system is 'poor';
Figure BDA0002537563280000052
indicates that the health status of the overhead line system is "poor";
Figure BDA0002537563280000053
the health status of the overhead line system is indicated as 'normal';
Figure BDA0002537563280000054
the health status of the contact net is indicated as 'better';
Figure BDA0002537563280000055
the health status of the contact net is indicated as 'good'; wherein the content of the first and second substances,
Figure BDA0002537563280000056
Figure BDA0002537563280000057
Figure BDA0002537563280000058
Figure BDA0002537563280000059
Figure BDA00025375632800000510
according to the relevant contact network detection evaluation standard of 'high-speed railway contact network operation maintenance rule' issued by China railway general company, a contact network health evaluation index set is established, and the index set is divided into four first-level indexes: a safety index, a smoothness index, a current acceptance index and a network pressure; eight secondary index items: lead height, pull out, lead height difference across the interior, hard spot, off-line duration, wire slope, contact pressure, and catenary voltage.
And collecting the existing result data set of the related catenary evaluation information based on the defect type occurrence mechanism, constructing a characteristic project based on all secondary index items, and cleaning the data. Specific data dimension items include: line, station, kilometer post, time of occurrence, defect type, defect grade, lead height value, pull-out value, inter-span lead height difference, hard spot, off-line duration, wire slope, contact pressure, contact network voltage, and quality evaluation (unqualified, qualified, excellent).
And (3) constructing a training set and a test set by using a classification-random forest algorithm and taking the quality evaluation as a label and the secondary index item as a characteristic dimension, and storing a classification model when the classification accuracy on the test set data set meets the precision requirement.
And extracting a second-level index item lead height value, a pull-out value, an inter-span lead height difference, a hard point, off-line duration, a wire gradient, contact pressure and a contact network voltage weight ratio from the training and storing model.
And screening out the recorded values of eight secondary indexes of each contact network device at different kilometer post positions (post numbers) on the same day, the same line, the same row and the same station in the newly acquired contact network routing inspection database, and taking the recorded values as original data, namely different contact network objects of the health state to be evaluated.
In the obtained original data, a data cleaning mechanism is utilized, the specific data cleaning mechanism is threshold segmentation, and specifically, when a first-level defect and a second-level defect of the contact network occur according to each second-level index in 'high-speed rail maintenance regulations', the data record values exceeding the standard values are converted into the standard values respectively corresponding to the standard values.
The data after washing were normalized to 0-1. Specifically, n evaluation objects, m evaluation indexes, x are providedijIf (i 1,2.. n; j 1,2.. m) is a data record for each dimension index, the quantized value of the evaluation target i with respect to the evaluation index j is standardized in the following manner:
Figure BDA0002537563280000061
in the above formula
Figure BDA0002537563280000062
The minimum value and the maximum value of the j index of the ith evaluation object are respectively;
Figure BDA0002537563280000063
for the j index x of the ith evaluation objectijNormalized values of (d).
In the obtained 0-1 standardized data, the weight ratio among eight secondary index items is calculated by applying an entropy weight method.
Respectively obtaining eight secondary indexes, and integrating the weight proportions among the eight secondary indexes, wherein the specific integration mode is that a secondary index item lead height value, a pull-out value, an inter-span lead height difference, a hard point, off-line time, a lead gradient, contact pressure and a contact network voltage weight ratio account for 0.4 in a training and storing model; and (4) calculating the weight ratio of the eight secondary index items to be 0.6 by using an entropy weight method. The final weight ratio of the eight secondary indexes is obtained.
Constructing gray class grades and gray clustering functions in the gray clusters, wherein the gray classes comprise five categories of poor, general, good and good, and the gray clustering functions under each grade are given aiming at five different gray classes, and specifically comprise the following steps:
Figure BDA0002537563280000071
indicates that the health state of the overhead line system is 'poor';
Figure BDA0002537563280000072
indicates that the health status of the overhead line system is "poor";
Figure BDA0002537563280000073
the health status of the overhead line system is indicated as 'normal';
Figure BDA0002537563280000074
the health status of the contact net is indicated as 'better';
Figure BDA0002537563280000075
indicating that the health status of the catenary is "good".
Figure BDA0002537563280000076
Figure BDA0002537563280000077
Figure BDA0002537563280000078
Figure BDA0002537563280000079
And calculating the coefficient of each gray level to which each contact net object belongs by using the gray clustering function and the gray theory of the standard data and the obtained secondary index weight, and finding out the level corresponding to the maximum coefficient as the health state of the contact net. And (3) applying the health state value of each contact net object to the Theil imbalance index:
Figure BDA00025375632800000710
in the formula
Figure BDA0002537563280000081
And (4) clustering coefficients of the decision object i belonging to the k gray class. Balance
Figure BDA0002537563280000082
Is the clustering coefficient vector of the decision object i. The higher the Theil imbalance index, the more reliable the evaluation result.
The invention also provides a high-speed railway contact net equipment health monitoring device based on the classification algorithm and gray clustering comprehensive evaluation. The method comprises the following steps:
the external service is used for receiving the dimension information of the latest contact network object;
the memory is used for storing a computer program, historical dimensional data of the contact net object and real-time data;
the processor is used for executing the computer program method so as to realize the high-speed railway contact network equipment health device based on a classification algorithm and gray clustering comprehensive evaluation;
external service: the calculation result provides interface service for the outside;
a display module: and providing a display interface between the terminal equipment and the user, and displaying the early warning result including videos, characters, images and the like.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. A health assessment method for high-speed railway contact network equipment is characterized by comprising the following steps:
the method comprises the following steps of firstly, establishing a contact network health evaluation index item set based on a contact network system health state evaluation index system;
collecting operation data of the contact network, and constructing a characteristic project based on a secondary index;
thirdly, constructing a training set and a testing set by taking the quality evaluation as a label and taking the secondary index item as a characteristic dimension, and obtaining a classification model through a classification-random forest algorithm based on the training set and the testing set;
extracting the weight ratio of each index in the secondary index items in the classification model;
acquiring recorded values of secondary indexes of each contact network device at different kilometer mark positions on the same day, the same line, the same row and the same station as original data;
cleaning the original data by using a data cleaning mechanism, and carrying out 0-1 standardization on the cleaned data;
step seven, calculating the weight ratio of each index between the two-level index items by using the data after 0-1 standardization by using an entropy weight method;
step eight, synthesizing the weight ratios in the step four and the weight ratios in the step seven to obtain final weight ratios of the indexes in the secondary indexes;
constructing gray class grades and gray clustering functions in the gray clusters;
and step ten, calculating the coefficient of each gray grade to which each contact net object belongs by using the normalized data in the step six and the final weight ratio in the step eight through a gray clustering function, wherein the grade corresponding to the maximum coefficient is taken as the health state of the contact net.
2. The method for evaluating the health of the high-speed railway contact network equipment according to claim 1, wherein the index item set is divided into four first-level indexes: a safety index, a smoothness index, a current acceptance index and a network pressure; eight secondary index items: lead height, pull out, lead height difference across the interior, hard spot, off-line duration, wire slope, contact pressure, and catenary voltage.
3. The method for evaluating the health of the equipment of the high-speed railway contact network according to claim 1, wherein the 0-1 standardization comprises the following process of setting n evaluation objects, m evaluation indexes and xijN, i ═ 1,2.. n; if j is 1,2.. and m is a quantized value of the evaluation target i with respect to the evaluation index j, which is a data record for each dimension index, the standardized formula is:
Figure RE-FDA0002599473810000011
in the above formula
Figure RE-FDA0002599473810000012
The minimum value and the maximum value of the j index of the ith evaluation object are respectively;
Figure RE-FDA0002599473810000013
for the j index x of the ith evaluation objectijNormalized values of (d).
4. The method for evaluating the health of the high-speed railway contact network equipment according to claim 1, wherein the weight ratios of the four steps and the seven steps are integrated in a way that the index weight obtained in the four steps accounts for 0.4; and the index weight obtained in the step seven accounts for 0.6, so that the final index weight ratio of eight secondary indexes is obtained.
5. The method for evaluating the health of the equipment of the high-speed railway contact network according to claim 1, wherein the gray grades comprise five categories of poor, general, good and good,
Figure RE-FDA0002599473810000021
indicates that the health state of the overhead line system is 'poor';
Figure RE-FDA0002599473810000022
indicates that the health status of the overhead line system is "poor";
Figure RE-FDA0002599473810000023
the health status of the overhead line system is indicated as 'normal';
Figure RE-FDA0002599473810000024
the health status of the contact net is indicated as 'better';
Figure RE-FDA0002599473810000025
the health status of the contact net is indicated as 'good'; wherein the content of the first and second substances,
Figure RE-FDA0002599473810000026
Figure RE-FDA0002599473810000027
Figure RE-FDA0002599473810000028
Figure RE-FDA0002599473810000029
Figure RE-FDA00025994738100000210
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* Cited by examiner, † Cited by third party
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CN112633677A (en) * 2020-12-19 2021-04-09 大秦铁路股份有限公司 Method for evaluating line quality based on railway power supply specialty
CN112700037A (en) * 2020-12-28 2021-04-23 成都国铁电气设备有限公司 Method and device for predicting number of foreign body defects of high-speed railway contact net
CN113723827A (en) * 2021-09-01 2021-11-30 山东大学 Subway electromechanical equipment operation risk diagnosis and operation and maintenance control method and system
CN113780896A (en) * 2021-10-11 2021-12-10 辽宁工程技术大学 Health assessment method for hard rock tunneling system
CN114638556A (en) * 2022-05-18 2022-06-17 成都唐源电气股份有限公司 Contact network quality evaluation method based on weighted rank-sum ratio algorithm
CN114897197A (en) * 2022-07-14 2022-08-12 中铁电气化勘测设计研究院有限公司 Contact net maintenance strategy adjusting method based on defect statistics and prediction
CN115796661A (en) * 2022-11-22 2023-03-14 交控科技股份有限公司 Device health state estimation method, device and storage medium

Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2408991A1 (en) * 2001-10-22 2003-04-22 Siemens Medical Solutions Health Services Corporation A user interface system for maintaining organization related information for use in supporting organization operation
US20140086401A1 (en) * 2012-09-12 2014-03-27 Genesys Telecommunications Laboratories, Inc. System and method for montioring health of deployment states for a contact center
CN104634493A (en) * 2015-01-12 2015-05-20 西南交通大学 Irregularity diagnosis and positioning method for pantograph contact pressure of electrified railway
KR101535075B1 (en) * 2014-01-20 2015-07-10 한국과학기술연구원 Non-invasive health indicator monitoring system and using method thereof
CN106096164A (en) * 2016-06-21 2016-11-09 西南交通大学 A kind of contact net structure wavelength extraction method
CN106203829A (en) * 2016-07-11 2016-12-07 西南交通大学 A kind of contact net reliability estimation method considering state of weather
CN108537448A (en) * 2018-04-12 2018-09-14 西南交通大学 High iron catenary health state evaluation method based on Set Pair Analysis and evidence theory
CN109472461A (en) * 2018-10-18 2019-03-15 中国铁道科学研究院集团有限公司基础设施检测研究所 Contact net section quality determination method and device
CN109948204A (en) * 2019-03-05 2019-06-28 清华大学 A kind of high iron catenary dynamic risk analysis method based on Bayesian network
CN110011864A (en) * 2019-05-17 2019-07-12 北京鼎兴达信息科技股份有限公司 A kind of high-speed railway touching net method for monitoring safety status
US20190225249A1 (en) * 2013-04-16 2019-07-25 Prodose Sarl Device for communicating and monitoring railway tracks and operating method
CN110321944A (en) * 2019-06-26 2019-10-11 华中科技大学 A kind of construction method of the deep neural network model based on contact net image quality evaluation
CN110672158A (en) * 2019-10-22 2020-01-10 中车株洲电力机车有限公司 Pantograph health management state detection method
CN110795692A (en) * 2019-09-16 2020-02-14 浙江工业大学 Active power distribution network operation state evaluation method
CN110880062A (en) * 2019-10-31 2020-03-13 广西电网有限责任公司电力科学研究院 Method for determining state maintenance time of power distribution equipment
CN110929432A (en) * 2020-02-07 2020-03-27 西南交通大学 Double-pantograph interval determination method for improving current collection quality of double pantograph of high-speed railway
CN111209999A (en) * 2018-11-21 2020-05-29 成都唐源电气股份有限公司 Contact network performance degradation prediction method based on recurrent neural network

Patent Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2408991A1 (en) * 2001-10-22 2003-04-22 Siemens Medical Solutions Health Services Corporation A user interface system for maintaining organization related information for use in supporting organization operation
US20140086401A1 (en) * 2012-09-12 2014-03-27 Genesys Telecommunications Laboratories, Inc. System and method for montioring health of deployment states for a contact center
CN104823157A (en) * 2012-09-12 2015-08-05 格林伊登美国控股有限责任公司 System and method for providing dynamic elasticity of contact center resources
US20190225249A1 (en) * 2013-04-16 2019-07-25 Prodose Sarl Device for communicating and monitoring railway tracks and operating method
KR101535075B1 (en) * 2014-01-20 2015-07-10 한국과학기술연구원 Non-invasive health indicator monitoring system and using method thereof
CN104634493A (en) * 2015-01-12 2015-05-20 西南交通大学 Irregularity diagnosis and positioning method for pantograph contact pressure of electrified railway
CN106096164A (en) * 2016-06-21 2016-11-09 西南交通大学 A kind of contact net structure wavelength extraction method
CN106203829A (en) * 2016-07-11 2016-12-07 西南交通大学 A kind of contact net reliability estimation method considering state of weather
CN108537448A (en) * 2018-04-12 2018-09-14 西南交通大学 High iron catenary health state evaluation method based on Set Pair Analysis and evidence theory
CN109472461A (en) * 2018-10-18 2019-03-15 中国铁道科学研究院集团有限公司基础设施检测研究所 Contact net section quality determination method and device
CN111209999A (en) * 2018-11-21 2020-05-29 成都唐源电气股份有限公司 Contact network performance degradation prediction method based on recurrent neural network
CN109948204A (en) * 2019-03-05 2019-06-28 清华大学 A kind of high iron catenary dynamic risk analysis method based on Bayesian network
CN110011864A (en) * 2019-05-17 2019-07-12 北京鼎兴达信息科技股份有限公司 A kind of high-speed railway touching net method for monitoring safety status
CN110321944A (en) * 2019-06-26 2019-10-11 华中科技大学 A kind of construction method of the deep neural network model based on contact net image quality evaluation
CN110795692A (en) * 2019-09-16 2020-02-14 浙江工业大学 Active power distribution network operation state evaluation method
CN110672158A (en) * 2019-10-22 2020-01-10 中车株洲电力机车有限公司 Pantograph health management state detection method
CN110880062A (en) * 2019-10-31 2020-03-13 广西电网有限责任公司电力科学研究院 Method for determining state maintenance time of power distribution equipment
CN110929432A (en) * 2020-02-07 2020-03-27 西南交通大学 Double-pantograph interval determination method for improving current collection quality of double pantograph of high-speed railway

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘仕兵等: "基于灰色聚类和组合赋权法的高速铁路接触网健康状态评估研究" *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112633677A (en) * 2020-12-19 2021-04-09 大秦铁路股份有限公司 Method for evaluating line quality based on railway power supply specialty
CN112700037A (en) * 2020-12-28 2021-04-23 成都国铁电气设备有限公司 Method and device for predicting number of foreign body defects of high-speed railway contact net
CN113723827A (en) * 2021-09-01 2021-11-30 山东大学 Subway electromechanical equipment operation risk diagnosis and operation and maintenance control method and system
CN113723827B (en) * 2021-09-01 2023-09-15 山东大学 Operation risk diagnosis and operation management and control method and system for subway electromechanical equipment
CN113780896A (en) * 2021-10-11 2021-12-10 辽宁工程技术大学 Health assessment method for hard rock tunneling system
CN114638556A (en) * 2022-05-18 2022-06-17 成都唐源电气股份有限公司 Contact network quality evaluation method based on weighted rank-sum ratio algorithm
CN114897197A (en) * 2022-07-14 2022-08-12 中铁电气化勘测设计研究院有限公司 Contact net maintenance strategy adjusting method based on defect statistics and prediction
CN114897197B (en) * 2022-07-14 2022-10-04 中铁电气化勘测设计研究院有限公司 Contact net maintenance strategy adjusting method based on defect statistics and prediction
CN115796661A (en) * 2022-11-22 2023-03-14 交控科技股份有限公司 Device health state estimation method, device and storage medium

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Application publication date: 20200911