CN111626439A - Steel rail overhaul decision support method based on artificial intelligence - Google Patents
Steel rail overhaul decision support method based on artificial intelligence Download PDFInfo
- Publication number
- CN111626439A CN111626439A CN201911058756.0A CN201911058756A CN111626439A CN 111626439 A CN111626439 A CN 111626439A CN 201911058756 A CN201911058756 A CN 201911058756A CN 111626439 A CN111626439 A CN 111626439A
- Authority
- CN
- China
- Prior art keywords
- rail
- formula
- substituting
- change
- factor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 42
- 239000010959 steel Substances 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 15
- 230000008859 change Effects 0.000 claims abstract description 63
- 230000006378 damage Effects 0.000 claims abstract description 25
- 102000016550 Complement Factor H Human genes 0.000 claims abstract description 11
- 108010053085 Complement Factor H Proteins 0.000 claims abstract description 11
- YBJHBAHKTGYVGT-ZKWXMUAHSA-N (+)-Biotin Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)O)SC[C@@H]21 YBJHBAHKTGYVGT-ZKWXMUAHSA-N 0.000 claims abstract description 9
- 208000027418 Wounds and injury Diseases 0.000 claims abstract description 9
- 208000014674 injury Diseases 0.000 claims abstract description 9
- FEPMHVLSLDOMQC-UHFFFAOYSA-N virginiamycin-S1 Natural products CC1OC(=O)C(C=2C=CC=CC=2)NC(=O)C2CC(=O)CCN2C(=O)C(CC=2C=CC=CC=2)N(C)C(=O)C2CCCN2C(=O)C(CC)NC(=O)C1NC(=O)C1=NC=CC=C1O FEPMHVLSLDOMQC-UHFFFAOYSA-N 0.000 claims abstract description 9
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000012937 correction Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000008832 photodamage Effects 0.000 claims description 4
- 230000008439 repair process Effects 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 230000004927 fusion Effects 0.000 abstract 1
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000037974 severe injury Diseases 0.000 description 1
- 230000009528 severe injury Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a steel rail overhaul decision support method based on artificial intelligence, which comprises the following specific steps: s1, collecting characteristic data related to steel rail overhaul, and preprocessing the collected characteristic data; s2, substituting the processed light and heavy damage characteristics into an injury judging formula to obtain a key influence factor F; s3, substituting the processed vertical grinding side grinding damage characteristics into an appraising formula to obtain a secondary influence factor S; s4, substituting the processed steel rail year characteristics into a year discrimination formula to obtain a history factor H; s5, substituting the major influencing factor F, the minor influencing factor S and the historical factor H into a formula to obtain a track change grade; s6, substituting the track change grade of each section into a formula to calculate and obtain track change trend change data; and S7, substituting the track change trend change data of each section into a formula to be added to obtain a track change interval. The invention provides a rail overhaul decision-making method based on multi-source data fusion, which intelligently recommends a rail change interval, changes the fixed mode of 'one-cutting' in the past, provides a more scientific and reasonable decision-making reference basis, realizes accurate rail change and promotes the reasonable utilization of resources.
Description
Technical Field
The invention relates to the field of maintenance and repair of railway steel rails and the technical field of data analysis, in particular to a method for calculating a steel rail overhaul interval.
Background
The steel rail is one of main technical equipment of the railway, is the basis of driving safety and is responsible for important transportation tasks. With the development of high speed and heavy load of railways, trip tasks are continuously increased, the running density is improved, the steel rail bears huge operation pressure, the fatigue aging speed is accelerated, the service life is seriously reduced, and safety risks exist. In order to avoid safety accidents, railway departments overhaul or change rails by judging whether the service life of the steel rails is 10 years or whether the total load exceeds 7 hundred million tons, and the aging condition of the actual steel rails is influenced by various factors such as geographic environment, steel rail positions, load distribution and the like, so that the overhaul of 'one-cutting' causes the fatigue damage of the lines to be frequent but the steel rails with the age limit less than 10 years or the load less than 7 hundred million tons exist in large quantity, the accurate rail change cannot be realized, and the waste of resources is also caused. Therefore, a scientific and reasonable rail overhaul decision method is needed, and rail replacement decision support can be made according to the actual damage state of the rail.
The invention provides an intelligent overhaul decision method based on steel rail multi-source data analysis mining, which predicts the development trend of steel rail damage by analyzing and mining the data of steel rail damage, abrasion, service life, total weight, environmental data and the like, intelligently recommends a rail change interval, provides digitalized and visual decision support for steel rail overhaul, avoids 'one-time cutting', improves the scientificity of decision, improves the safety of railway operation, and adapts to the railway transportation requirement of high-speed development.
Disclosure of Invention
In view of the above, the present invention provides a rail major repair decision support method based on artificial intelligence, so as to effectively solve the problems in the above technical background.
The invention is realized by adopting the following technical scheme:
s1, collecting characteristic data related to steel rail overhaul, and preprocessing the collected characteristic data;
s2, substituting the processed light and heavy damage characteristics into an injury judging formula to obtain a key influence factor F;
s3, substituting the processed vertical grinding side grinding damage characteristics into an appraising formula to obtain a secondary influence factor S;
s4, substituting the processed steel rail year characteristics into a year discrimination formula to obtain a history factor H;
s5, substituting the major influencing factor F, the minor influencing factor S and the historical factor H into a formulaTo the track change level L and calculating the minimum track change interval L according to the track change level Lmin；
S6, substituting the track change grade of each section into a formula to calculate track change trend change data FL;
and S7, substituting the track change trend change data of each section into a formula to be added to obtain a track change interval.
The processing step of S1 is as follows:
s11, collecting characteristic data related to steel rail overhaul, such as vertical grinding of the steel rail, side grinding of the steel rail, gross weight, historical rail change interval, heavy damage number of 1km, light damage number of 1km and steel rail year;
and S12, preprocessing the acquired characteristic data, and processing missing values and abnormal values in the data.
The key influencing factor F is the severe injury number N of 1kmZAnd substituting the total weight M into a formula, wherein the formula is as follows:
The secondary influencing factor S is vertical grinding V of the steel rail and side grinding S of the steel railiAnd a number of light injuries N of 1kmSSubstituting into a formula to obtain the formula:
The historical factor H is the track change interval H from the historynSubstituting into a formula to obtain the formula:
wherein t is HnThe years of rail replacement in the last 6 years, when t is 0, the rail section is not replaced; when t is 1 or 2, the rail is just recently changed, and the priority level should be reduced; when t is more than 2, the section of the railway rail is replacedThe frequency is high but is still vulnerable, and the priority of rail change is improved.
The processing steps of S5 are as follows:
s51, calculating by a formula to obtain a rail change grade evaluation, wherein the rail change grade L is obtained by substituting a major influence factor F, a minor influence factor S and a historical factor H into the formula, and the formula is as follows:
s52, calculating the minimum track change interval L according to the track change grade substitution formulamin. Minimum track change interval LminThe calculation formula is as follows:
The rail change trend change data FL is obtained by substituting the basic length of 0.05km into the rail according to the mileage N and the rail change grade L, wherein the formula is as follows:
The track-changing interval is obtained by substituting track-changing trend change data FL into a formula, wherein the formula is as follows:
the interval ∑ max (fl (Hl) -Hl,0), Hl being the defined mileage Hl as the standard threshold value.
The invention has the advantages of
The invention provides a rail overhaul decision support method based on artificial intelligence.
Drawings
FIG. 1 is a flow chart of the steps of an artificial intelligence rail major repair decision support method;
FIG. 2 is a graph of the original characteristics of the rail (vertical, lateral, actual gross heavy rail damage, actual gross light damage, year of rail, actual gross pass weight);
FIG. 3 is a trend analysis chart of rail changing trend;
fig. 4 is a diagram of recommended track change intervals.
Detailed Description
The invention will be further described by the following specific examples in conjunction with the drawings, which are provided for illustration only and are not intended to limit the scope of the invention.
The data acquisition and calculation were performed in this example using a 6 km length of rail as an example.
S1, collecting characteristic data related to steel rail overhaul, and preprocessing the collected characteristic data, wherein the processing steps are as follows:
s11, collecting characteristic data related to steel rail overhaul, such as vertical grinding of the steel rail, side grinding of the steel rail, gross weight, historical rail change interval, heavy damage number of 1km, light damage number of 1km and steel rail year;
s12, preprocessing the collected characteristic data, and processing missing values and abnormal values in the data;
and S2, substituting the processed light and heavy injury data of 1km into a formula, and calculating to obtain an important influence factor K of 15.62, wherein K is1＝2.2，K2＝1.5，C＝4，G＝100；
S3, substituting the processed vertical grinding side grinding data into a formula, and calculating to obtain a secondary influence factor S2.624, wherein D is 0.12, and E is 0.2;
s4, substituting the processed steel rail year characteristics into a formula, and calculating to obtain a historical influence factor H which is 0.5;
s5, obtaining the rail change grade evaluation through a formula and calculation, wherein the rail change grade L is 8.62, and calculating the minimum rail change interval L according to the rail change grade L and substituting the rail change grade L into the formulaminThe section Lmin1, wherein LB＝2.6；
S6, length of rail based on 0.05kmThe track-change trend data FL is 8.83 obtained by substituting the mileage N and the track-change level L, as shown in FIG. 3, wherein CY＝0.3，O＝2009；
S7, the track-change trend data FL is substituted into the formula to obtain the recommended track-change interval, as shown in fig. 4, where Hl is 0.05 and Hl is 6.2.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A rail overhaul decision support method based on artificial intelligence is characterized by comprising the following steps:
s1, collecting characteristic data related to steel rail overhaul, and preprocessing the collected characteristic data;
s2, substituting the processed light and heavy damage characteristics into an injury judging formula to obtain a key influence factor F;
s3, substituting the processed vertical grinding side grinding damage characteristics into an appraising formula to obtain a secondary influence factor S;
s4, substituting the processed steel rail year characteristics into a year discrimination formula to obtain a history factor H;
s5, substituting the major influencing factor F, the minor influencing factor S and the historical factor H into a formula to obtain a track change level L, and calculating the minimum track change interval L according to the track change level Lmin；
S6, substituting the track change grade of each section into a formula to calculate track change trend change data FL;
and S7, substituting the track change trend change data of each section into a formula to be added to obtain a track change interval.
2. The artificial intelligence-based rail major repair decision support method according to claim 1, wherein the processing step of S1 is as follows:
s11, collecting characteristic data related to steel rail overhaul, such as vertical grinding of the steel rail, side grinding of the steel rail, gross weight, historical rail change interval, heavy damage number of 1km, light damage number of 1km and steel rail year;
and S12, preprocessing the acquired characteristic data, and processing missing values and abnormal values in the data.
3. The method for supporting rail overhaul decision based on artificial intelligence as claimed in claim 1, wherein the important influence factor F of S2 is the number of serious injuries N of 1kmZAnd substituting the total weight M into a formula, wherein the formula is as follows:
4. The method for supporting rail overhaul decision based on artificial intelligence is characterized in that the secondary influence factors S of S3 are vertical grinding V of the rail and side grinding S of the railiAnd a number of light injuries N of 1kmSSubstituting into a formula to obtain the formula:
5. The method for supporting rail overhaul decision based on artificial intelligence according to claim 1, wherein the historical factor H of S4 is a historical rail change interval HnSubstituting into a formula to obtain the formula:
6. The method for supporting rail overhaul decision based on artificial intelligence according to claim 1, wherein the processing step of S5 is as follows:
s51, calculating by a formula to obtain a rail change grade evaluation, wherein the rail change grade L is obtained by substituting a major influence factor F, a minor influence factor S and a historical factor H into the formula, and the formula is as follows:
s52, calculating the minimum track change interval L according to the track change grade substitution formulamin。
7. The method for supporting rail overhaul decision based on artificial intelligence as claimed in claim 6, wherein the minimum rail change interval L of S52minThe calculation formula is as follows:
8. The support method for steel rail overhaul decision based on artificial intelligence of claim 1, wherein the rail change trend variation data FL of S6 is calculated by substituting the base length of 0.05km for the rail according to mileage N and rail change grade L, wherein the formula is as follows:
9. The artificial intelligence-based steel rail overhaul decision support method according to claim 1, wherein the rail change interval of S7 is obtained by substituting rail change trend change data FL into a formula:
the interval ∑ max (fl (Hl) -Hl,0), Hl being the defined mileage Hl as the standard threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911058756.0A CN111626439B (en) | 2019-11-01 | 2019-11-01 | Rail overhaul decision support method based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911058756.0A CN111626439B (en) | 2019-11-01 | 2019-11-01 | Rail overhaul decision support method based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111626439A true CN111626439A (en) | 2020-09-04 |
| CN111626439B CN111626439B (en) | 2023-12-29 |
Family
ID=72259658
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911058756.0A Active CN111626439B (en) | 2019-11-01 | 2019-11-01 | Rail overhaul decision support method based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111626439B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114834502A (en) * | 2022-05-25 | 2022-08-02 | 东莞灵虎智能科技有限公司 | Method for predicting rail break risk of work service based on artificial intelligence and big data |
| CN114987583A (en) * | 2022-05-18 | 2022-09-02 | 中国铁道科学研究院集团有限公司 | Steel rail overhaul prediction method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5978717A (en) * | 1997-01-17 | 1999-11-02 | Optram, Inc. | Computer system for railway maintenance |
| CN103714228A (en) * | 2012-09-29 | 2014-04-09 | 国际商业机器公司 | Method and device for determining rail maintenance sections |
| CN105109517A (en) * | 2015-08-13 | 2015-12-02 | 中国神华能源股份有限公司 | Rail-flaw analyzing method and rail-flaw detecting car |
| CN108009742A (en) * | 2017-12-15 | 2018-05-08 | 北京交通大学 | A kind of method and system of definite railroad track health status |
| CN110363403A (en) * | 2019-06-27 | 2019-10-22 | 中国铁道科学研究院集团有限公司 | Railway track damage forecast method and device |
-
2019
- 2019-11-01 CN CN201911058756.0A patent/CN111626439B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5978717A (en) * | 1997-01-17 | 1999-11-02 | Optram, Inc. | Computer system for railway maintenance |
| CN103714228A (en) * | 2012-09-29 | 2014-04-09 | 国际商业机器公司 | Method and device for determining rail maintenance sections |
| CN105109517A (en) * | 2015-08-13 | 2015-12-02 | 中国神华能源股份有限公司 | Rail-flaw analyzing method and rail-flaw detecting car |
| CN108009742A (en) * | 2017-12-15 | 2018-05-08 | 北京交通大学 | A kind of method and system of definite railroad track health status |
| CN110363403A (en) * | 2019-06-27 | 2019-10-22 | 中国铁道科学研究院集团有限公司 | Railway track damage forecast method and device |
Non-Patent Citations (1)
| Title |
|---|
| 魏世斌等: "重载铁路线路大修经济决策的研究", 《铁道学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114987583A (en) * | 2022-05-18 | 2022-09-02 | 中国铁道科学研究院集团有限公司 | Steel rail overhaul prediction method and device, electronic equipment and storage medium |
| CN114834502A (en) * | 2022-05-25 | 2022-08-02 | 东莞灵虎智能科技有限公司 | Method for predicting rail break risk of work service based on artificial intelligence and big data |
| CN114834502B (en) * | 2022-05-25 | 2024-03-19 | 东莞灵虎智能科技有限公司 | Task rail break risk prediction method based on artificial intelligence and big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111626439B (en) | 2023-12-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111626439A (en) | Steel rail overhaul decision support method based on artificial intelligence | |
| CN112201038A (en) | Road network risk assessment method based on risk of bad driving behavior of single vehicle | |
| CN112364297A (en) | Method for evaluating service state of steel rail of ordinary speed line | |
| CN113822387B (en) | Road surface damage condition index prediction method, system, equipment and medium | |
| CN109635440B (en) | Overhead transmission line icing flashover tripping probability calculation method | |
| CN104240030A (en) | Track traffic network dynamic security risk evaluation method | |
| Wybo | Track circuit reliability assessment for preventing railway accidents | |
| Andrade et al. | Assessing the potential cost savings of introducing the maintenance option of ‘Economic Tyre Turning’in Great Britain railway wheelsets | |
| CN116050853A (en) | Wind control method and system for net cargo freight bill | |
| CN110503209B (en) | Steel rail analysis early warning model construction method and system based on big data | |
| CN111986480A (en) | Method, system and storage medium for evaluating influence of urban road traffic incident | |
| CN108376293B (en) | ZJ17 cigarette equipment maintenance intelligent prediction method based on fuzzy mathematics improved analytic hierarchy process | |
| CN116644890A (en) | Method for calculating overhaul index of high-speed railway steel rail | |
| Girsch et al. | Managing rail life to match performance and cut costs | |
| CN116481839A (en) | Wheel state evaluation method based on objective weighting method | |
| CN115081784A (en) | Fault prediction and health management system for locomotive wheels | |
| CN109604709A (en) | A kind of Continuous Hot Dip Galvanizing Line end trimming shears state of wear judgment method and device | |
| CN114331139A (en) | Campus risk monitoring and early warning system based on block chain network | |
| CN113255825A (en) | Track bed defect identification method and device | |
| Liu et al. | Benefit-cost analysis of heavy haul railway track upgrade for safety and efficiency | |
| CN112330516A (en) | Method and device for generating road surface maintenance plan | |
| CN113435675B (en) | Road grading method based on mobile dangerous chemical transportation and application thereof | |
| CN113487144B (en) | Safety risk early warning method and system for railway group on-road operation | |
| Patlasov et al. | Development of methods to increase the efficiency of railway maintenance | |
| CN115860730B (en) | Railway track constructor safety control system based on internet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |