EP3774487A1 - Verfahren und vorrichtung zur diagnose und überwachung von fahrzeugen, fahrzeugkomponenten und fahrwegen - Google Patents
Verfahren und vorrichtung zur diagnose und überwachung von fahrzeugen, fahrzeugkomponenten und fahrwegenInfo
- Publication number
- EP3774487A1 EP3774487A1 EP19727863.3A EP19727863A EP3774487A1 EP 3774487 A1 EP3774487 A1 EP 3774487A1 EP 19727863 A EP19727863 A EP 19727863A EP 3774487 A1 EP3774487 A1 EP 3774487A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- signals
- characteristic value
- vehicle
- characteristic
- probability
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000012544 monitoring process Methods 0.000 title claims abstract description 8
- 238000003745 diagnosis Methods 0.000 title abstract description 3
- 238000013179 statistical model Methods 0.000 claims abstract description 41
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000005259 measurement Methods 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 26
- 230000005540 biological transmission Effects 0.000 claims description 11
- 238000012706 support-vector machine Methods 0.000 claims description 7
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000001514 detection method Methods 0.000 abstract description 4
- 230000001133 acceleration Effects 0.000 description 22
- 238000000926 separation method Methods 0.000 description 10
- 230000007547 defect Effects 0.000 description 9
- 230000006399 behavior Effects 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 8
- 230000000875 corresponding effect Effects 0.000 description 4
- 230000002349 favourable effect Effects 0.000 description 4
- 230000002950 deficient Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002123 temporal effect Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000012854 evaluation process Methods 0.000 description 2
- 238000013213 extrapolation Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0081—On-board diagnosis or maintenance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
- B61L27/53—Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
- B61L27/57—Trackside diagnosis or maintenance, e.g. software upgrades for vehicles or trains, e.g. trackside supervision of train conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
Definitions
- the invention relates to a method for diagnosing and monitoring vehicles, vehicle components, routes and track components, in particular for rail vehicles and infrastructures of rail vehicles, wherein by means of at least one first sensor measurements are performed and by means of at least one arithmetic unit
- Another important factor is the effective and efficient maintenance and servicing of vehicles and infrastructures.
- Rail vehicle detected and supplied to characterize a driving behavior of the rail vehicle, the operating parameters of a monitoring hierarchy.
- the invention is therefore based on the object, a developed over the prior art, especially
- this object is achieved with a method of the type mentioned, in which at least one arithmetic unit is supplied with at least measured first signals,
- At least one first parameter is formed from the at least first signals
- the at least one first characteristic value or at least one first characteristic value combination is classified by means of at least one first statistical model or a prediction is carried out on the basis of the at least first characteristic value or the at least first characteristic value combination, and wherein at least one technical first state indicator with respect to at least one first vehicle component or at least one infrastructure component of at least one
- Classification result or from at least one
- Characteristics is carried out by means of statistical models. This means that e.g. erroneous or error-free states or a faulty or error-free behavior of a vehicle component (for example a damper) or a faulty or error-free behavior
- Track component (e.g., a track) no longer needs to be detected on the basis of rigid limits, but that, for example, limit values or threshold curves etc. depending on operating conditions of a vehicle or infrastructure, a failure behavior of a vehicle or infrastructure component o.ä. are customizable.
- Condition indicators may be e.g. out
- At least first statistical model is formed by means of a machine learning method.
- the at least first statistical model is formed on the basis of an adjustment calculation.
- a favorable solution is achieved if the at least first status indicator has a first probability value for entry by the at least one
- Prediction result is assigned to be indexed technical condition.
- Violations of these limits or these threshold curves etc. can be made. These probabilities may be formed, for example, based on frequencies of said violations.
- Characteristics one to n3 state indicators are formed with one to n4 probability values, each of the one to n3 state indicators one of the one to n4
- Probability values are assigned and from the one to n4 probability values a combination status indicator is formed with an associated combination probability value.
- the combination status indicator can be formed, for example, by means of a probabilistic graphical model.
- Condition indication by means of the second state indication (and vice versa) plausibility and hedged.
- Diagnosis and / or monitoring device of a vehicle can be used.
- warning events, status or error outputs, etc. can take place.
- These can be used, for example, as visual and / or audible warnings and / or status information or monitoring and / or diagnostic information is output in a cab of the vehicle.
- the invention is based on
- FIG. 1 A flowchart for an exemplary embodiment
- FIG. 2 A result diagram with by means of a
- Fig. 3 A rail vehicle with sensors, a
- Embodiment variant of a method according to the invention, partially computer-implemented method is in a computing unit 10, which in a car body 21 one in Fig. 3rd
- arranged rail vehicle is arranged
- a first sensor 1 which measures accelerations of the chassis frame 22, i. is designed as an acceleration sensor. Furthermore, a sensor 2 designed as a temperature sensor is connected to a damper 24 of the chassis 23 and is as
- Strain gauge formed third sensor 3 on a spring 28 of the chassis 23 is arranged.
- the second sensor 2 measures damper temperatures, the third sensor 3 deformations of the spring 28th
- the first sensor 1, the second sensor 2 and the third sensor 3 continuously perform measurements.
- the first sensor 1, the second sensor 2, the third sensor 3 and the arithmetic unit 10 are supplied with electricity via a power supply device, not shown.
- the arithmetic unit 10 which is connected to the first sensor 1, the second sensor 2 and the third sensor 3, there is a current signal processing 11 and evaluation of continuously detected first signals 4 of the first sensor 1, second signals 5 of the second sensor 2 and third signals 6 of the third sensor 3.
- the signal processing 11 comprises a storage of the first signals 4, the second signals 5 and the third signals 6 and their preparation for the evaluation processes.
- the first signals 4 and the first reference signals are acceleration signals of the
- Chassis frame 22 the second signals 5 and the second reference signals to temperature signals of the damper 24, the third signals 6 and the third
- Signals 6 third characteristic values formed (characteristic value determination 12). Temporally before the first characteristic values, the second characteristic values and the third characteristic values become from the first
- Reference parameters 9 are statistical
- Time periods are the characteristics associated with the reference characteristics, i. the first characteristic values are on the same path and time segments as the first reference parameters 7
- the second characteristics on the same way and Time segments such as the second reference characteristic values 8 and the third characteristic values correspond to the same path and time segments as the third reference characteristic values 9.
- the first characteristic values, the second characteristic values and the third characteristic values are inserted into a first statistical model 15, a second statistical model 16 and into further statistical models which are implemented in the arithmetic unit 10 and are continuously classified and predicted by means of these statistical models (Classification 13 and
- the first statistical model 15 and the second statistical model 16 are formed by means of a method of machine learning, the so-called Support Vector Machine.
- Embodiment of a method according to the invention used in a linear binary variant to classify characteristic values or characteristic value combinations.
- the first statistical model 15 will be out of the first
- the first reference characteristics 7, the second reference characteristics 8 and the third reference characteristics 9 thus function as learning data for the first statistical model 15 and the second statistical model 16.
- Dampers 24 may vary depending on the
- the first statistical model 15 is based on
- Reference characteristic combinations ie pairings from first Reference characteristics 7 and second reference characteristics 8, which in the second acceleration interval and the second
- State area 25 the second state area 26 and the separation line 27, which are shown diagrammatically in FIG. 2, can be localized via vectors.
- the separation line 27 is formed from the reference value combinations in a manner such that normal distances from the
- Separation line 27 to the reference characteristic combinations of the first state area 25 are mathematically negative and normal distances from the separation line 27 to the
- Reference characteristic combinations of the second state region 26 are mathematically positive. A corresponding
- inventive method is additionally provided, statistical models based only on a
- Reference characteristic value category (e.g., based on the second reference characteristics 8 and their temporal behavior, etc.), i. do not use reference characteristic combinations. This can also damper states
- the second statistical model 16 is formed according to the scheme described above, which is also used to form the first statistical model 15. However, the formation of the second statistical model 16 will be
- Chassis frame 22 in combination with deformations
- inventive method is additionally provided, the classification 13 not only on the basis of
- Characteristic combinations but also on the basis of individual characteristics (for example, the second characteristic values and their temporal behavior) to perform.
- the second characteristic values are used in that statistical model which is formed by means of the second reference characteristic values 8 without the use of reference characteristic value combinations.
- the prediction 14 is based on characteristic values.
- linear regression functions are provided or implemented in the arithmetic unit 10. This
- Regression functions are formed from the first characteristics, the second characteristics, and the third characteristics by means of educational rules known in the art and used to perform interpolations or extrapolations based on the characteristics.
- regressions of the second characteristic values and of the first characteristic value combinations are dependent on the
- Driving speed performed to a functional relationship between the second characteristics and the first characteristic combinations on the one hand and the driving speed on the other hand to be able to form.
- maximum travel speeds can be determined, up to which the damper 24 can be operated without premature excessive wear, etc. occurs.
- this exemplary embodiment of a method according to the invention involves allocations of characteristic values or
- Prediction results are e.g. Forecasts of medium
- a first state indicator is formed by a number of first characteristic combinations associated with the second state region 26 being set to a total number of first
- Characteristic combinations i. is set in proportion. From this, a first probability value is formed, which gives a statement about how
- the status indicator contains information "damper defective" with an associated probability for this damper defect.
- Kennwertkombination a second state indicator relating to a spring defect with an associated second
- the combination probability value P K is formed by means of a probabilistic graphical model based on a method of machine learning.
- State indicator and the second state indicator indicates determined.
- the combination probability value P K describes a probability of an actual occurrence of a failed condition of the damper 24 under a condition where an error is indicated by the first condition indicator and the second condition indicator.
- the combination condition indicator will likely indicate a failure of the damper 24.
- the first parameter P DF , the second parameter P F and the third parameter P D are continuously adapted via operating observations.
- the second parameter P F is increased when increased over a defined period of time Frequency of a damper defect is observed.
- the first state indicator is related to a first vehicle component (the damper 24) and the second state indicator relates to a second vehicle component (the spring 28).
- the first state indicator and the second state indicator point to the first one
- Generate signals e.g., temperature signals and oil pressure signals, etc.
- the method according to the invention also becomes a third one
- Kennwertkombinationen from continuously measured accelerations of the chassis frame 22 and measured speeds are assigned to the state areas, creating a Classification 13 of characteristic combinations takes place. From a frequency distribution corresponding
- the third condition indicator is determined.
- Data formed in the method step of the indicator combination 18 are used in a diagnostic and monitoring device implemented in the arithmetic unit 10 and continuously evaluated there. Furthermore, these data are shown for further evaluation via a shown in Fig. 3
- Data transmission device 19 of the rail vehicle also continuously to a maintenance level, i. to one
- FIG. 2 shows a diagram with a first state area 25, a second state area 26 and a separation line 27 of a first statistical model 15, which are also described in connection with FIG. 1, are formed on the basis of a support vector machine method and via which technical conditions of a damper 24 shown in Fig. 3 are evaluable.
- Accelerations are plotted on an x-axis of the diagram, and damper temperatures on a y-axis.
- the first state region 25 is a first function value 29 and a second function value 30 first
- the second state area 26 is a third function value 31 and a fourth function value 32 first
- the first function value 29, the second function value 30, the third function value 31 and the fourth function value 32 are formed from first characteristic combinations.
- the first Characteristic combinations are in turn made up of first characteristic values relating to accelerations of one shown in FIG.
- the first function value 29 and the second function value 30 have negative y 'coordinates
- the third function value 31 and the fourth function value 32 have positive y' coordinates.
- the first function value 29 and the second function value 30 are assigned to a favorable damper state, the third function value 31 and the fourth function value 32 to an unfavorable state of the damper 24.
- a first condition indicator is formed, optionally indicating a damper defect.
- Function values which are formed from second characteristic combinations, associated with corresponding state areas of a second statistical model 16 described in connection with FIG. 1.
- the second characteristic combinations are, as mentioned in connection with FIG. 1, formed from the first characteristic values as well as from third characteristic values with respect to a deformation of a spring 28 shown in FIG. 3.
- a second state indicator is formed from an accumulation of unfavorable spring states based on an assignment of acceleration-spring deformation function values to state regions, which optionally indicates a spring defect (eg more than three times a day when such spring states occur).
- an assignment of the second characteristic values to state regions of a further statistical model is also present Base of a support vector machine method provided.
- Steps of second reference parameters 8 described in connection with FIG. 1 are formed as a temperature-time relationship.
- Second characteristic values which branch with increasing time increasing temperatures, which lie above a defined period of time above a temperature-time separating straight line formed especially for this further statistical model, indicate a damper defect or excessive damper wear.
- a rail vehicle with a car body 21 and a chassis 23 is shown.
- the chassis 23 has a damper 24, which is a primary damper, and a spring 28, which serves as a primary spring
- a first sensor 1 for measuring accelerations of
- Chassis frame 22 i. an acceleration sensor
- a second sensor 2 which is designed as a temperature sensor, with the damper 24th
- the spring 28 has a third sensor 3, which is designed as a strain gauge.
- the first sensor 1, the second sensor 2 and the third sensor 3 are provided with a computing unit 10 in the car body 21
- a roof area of the rail vehicle is a
- Data transmission device 19 which is designed as a radio device and signal and current conducting with the
- the Arithmetic unit 10 is connected, provided.
- the data transmission device 19 also has a Global Positioning System unit
- the arithmetic unit 10 is supplied with power via a vehicle electrical system (not shown) of the rail vehicle and in turn supplies the first sensor 1, the second sensor 2, the third sensor 3 and the data transmission device 19 with electricity.
- a computer program product is installed, by means of which process steps of the method according to the invention according to FIG. 1, i. a
- Monitoring device of the rail vehicle acts, certain status indicators are warning events
- Rail vehicle to a display unit in a likewise not shown cab of the rail vehicle and transmitted there as warnings or status information (for example, to display a damper defect or a residual life of the damper 24).
- Connection with this exemplary embodiment of a device according to the invention is a maintenance status, transmitted for further evaluation via a vehicle fleet.
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- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Vehicle Body Suspensions (AREA)
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AT504072018 | 2018-05-16 | ||
PCT/EP2019/062498 WO2019219756A1 (de) | 2018-05-16 | 2019-05-15 | Verfahren und vorrichtung zur diagnose und überwachung von fahrzeugen, fahrzeugkomponenten und fahrwegen |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3774487A1 true EP3774487A1 (de) | 2021-02-17 |
Family
ID=66685577
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP19727863.3A Pending EP3774487A1 (de) | 2018-05-16 | 2019-05-15 | Verfahren und vorrichtung zur diagnose und überwachung von fahrzeugen, fahrzeugkomponenten und fahrwegen |
Country Status (5)
Country | Link |
---|---|
US (1) | US20210217256A1 (zh) |
EP (1) | EP3774487A1 (zh) |
CN (1) | CN112469613A (zh) |
RU (1) | RU2763414C1 (zh) |
WO (1) | WO2019219756A1 (zh) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IT202000005194A1 (it) * | 2020-03-11 | 2021-09-11 | Faiveley Transport Italia Spa | Sistema di monitoraggio per almeno una pluralità di dispositivi omogenei di almeno un veicolo ferroviario |
JP2023528011A (ja) * | 2020-05-29 | 2023-07-03 | コヌクス ゲーエムベーハー | 自動リアルタイムデータ生成 |
AT524207B1 (de) | 2020-12-11 | 2022-04-15 | Siemens Mobility Austria Gmbh | Fahrwerk für ein Schienenfahrzeug |
AT525305A1 (de) | 2021-08-04 | 2023-02-15 | Siemens Mobility Austria Gmbh | Sensoranordnung und Fahrwerk |
AT526374A1 (de) | 2022-07-29 | 2024-02-15 | Siemens Mobility Austria Gmbh | Synchronisierung einer Zeiterfassungsanordnung |
Family Cites Families (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CA2211815A1 (en) * | 1997-07-29 | 1999-01-29 | Craig Luker | Method and apparatus for determining vehicle brake effectiveness |
ES2240542T3 (es) | 2000-12-22 | 2005-10-16 | Db Fernverkehr Ag | Procedimiento y dispositivo para supervisar el comportamiento de marcha de vehiculos sobre carriles. |
JP4298433B2 (ja) * | 2003-08-20 | 2009-07-22 | 株式会社日立製作所 | 鉄道車両の異常検知装置 |
CN103229148B (zh) * | 2010-09-28 | 2016-08-31 | 西门子公司 | 运输车辆的适应性远程维护 |
US20140200951A1 (en) * | 2013-01-11 | 2014-07-17 | International Business Machines Corporation | Scalable rule logicalization for asset health prediction |
CN112949715A (zh) * | 2013-12-31 | 2021-06-11 | 北京泰乐德信息技术有限公司 | 一种基于svm的轨道交通故障诊断方法 |
JP6420972B2 (ja) * | 2014-06-13 | 2018-11-07 | 公益財団法人鉄道総合技術研究所 | 列車制御システム設計用シミュレータ |
US20160155098A1 (en) * | 2014-12-01 | 2016-06-02 | Uptake, LLC | Historical Health Metrics |
JP6588814B2 (ja) * | 2015-12-17 | 2019-10-09 | 株式会社東芝 | 異常診断装置及び方法 |
US10861320B2 (en) * | 2016-08-22 | 2020-12-08 | Rapidsos, Inc. | Predictive analytics for emergency detection and response management |
CN106976468A (zh) * | 2017-03-09 | 2017-07-25 | 南京理工大学 | 一种基于dwt和c‑svm的道岔故障诊断方法 |
-
2019
- 2019-05-15 RU RU2020141186A patent/RU2763414C1/ru active
- 2019-05-15 US US17/054,866 patent/US20210217256A1/en active Pending
- 2019-05-15 CN CN201980047483.1A patent/CN112469613A/zh active Pending
- 2019-05-15 EP EP19727863.3A patent/EP3774487A1/de active Pending
- 2019-05-15 WO PCT/EP2019/062498 patent/WO2019219756A1/de unknown
Also Published As
Publication number | Publication date |
---|---|
RU2763414C1 (ru) | 2021-12-29 |
US20210217256A1 (en) | 2021-07-15 |
WO2019219756A1 (de) | 2019-11-21 |
CN112469613A (zh) | 2021-03-09 |
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