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EP2056179A2 - Procédé de prévention contre les dommages de composants d'un véhicule automobile - Google Patents

Procédé de prévention contre les dommages de composants d'un véhicule automobile Download PDF

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Publication number
EP2056179A2
EP2056179A2 EP08166916A EP08166916A EP2056179A2 EP 2056179 A2 EP2056179 A2 EP 2056179A2 EP 08166916 A EP08166916 A EP 08166916A EP 08166916 A EP08166916 A EP 08166916A EP 2056179 A2 EP2056179 A2 EP 2056179A2
Authority
EP
European Patent Office
Prior art keywords
damage
component
life
path
components
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.)
Withdrawn
Application number
EP08166916A
Other languages
German (de)
English (en)
Other versions
EP2056179A3 (fr
Inventor
Peter SCHÖGGL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
AVL List GmbH
Original Assignee
AVL List GmbH
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by AVL List GmbH filed Critical AVL List GmbH
Publication of EP2056179A2 publication Critical patent/EP2056179A2/fr
Publication of EP2056179A3 publication Critical patent/EP2056179A3/fr
Withdrawn legal-status Critical Current

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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
    • G07C5/006—Indicating maintenance
    • 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

Definitions

  • the invention relates to a method for damage prediction of components of a motor vehicle.
  • a vehicle is known management system, which provides that components are monitored by a sensor system and transmitted in the event of a problem to a central computer.
  • the central computer identifies the problem area and determines the damage of the parts of the system and makes a prediction about the further damage characteristic and lifetime.
  • the user of the vehicle is informed of the result of this evaluation.
  • various information is passed on to the dealer and stored in a database. In this way, recommendations for inspections and service appointments are given to the user prior to the occurrence of serious damage.
  • the disadvantage is that the information is transmitted to a central computer, which causes the further evaluation. As a result, a real-time evaluation of the measured data can not be guaranteed, so that in extreme cases damage may already have occurred before the evaluation takes place by the central computer.
  • Another disadvantage is that no damage database is used and that no calibration with static damage frequency is made.
  • the object of the invention is to avoid these disadvantages and to enable an early and reliable detection of damage and / or a prediction of the remaining service life in the simplest possible way.
  • the damage components of all damage paths and / or damage periods of the component are summed up and with a maximum value stored in a database, wherein it is particularly advantageous if damage to the component is detected when the maximum value is reached.
  • a prognosis can also be made about the remaining service life of a component or an overall system.
  • the remaining service life may be a period of time in the strict sense or a residual distance to the expected failure. It is essential that this residual life expectancy is always to be seen in relation to a defined burden.
  • component x has a remaining life of 7.2 laps.
  • a reliable prediction of the damage during the further damage can be created if the remaining life of the component is estimated from the difference to the maximum value.
  • the remaining service life is determined on the basis of at least one lifetime model stored in a database.
  • the system's claims database is constantly updated so that the latest statistical information can be used in evaluating the damage and predicting the remaining life.
  • the system is thus self-learning.
  • the loading of the component is at least partially obtained on the basis of simulation data. This makes it possible, for example, to make statements about a component which already has a specific load history, it being assumed that this component will still be used in a future race from which assumptions about the expected course are available. At the same time, however, it is also possible to examine a new component exclusively with the aid of simulations in order to predict the expected damage or residual service life.
  • a parameter relevant to the load of the component is calculated on the basis of a predetermined required remaining service life or residual travel distance. If, for example, During a race that lasts more than 30 laps, a simulation shows that under the given conditions the expected remaining life of a component is only 25 laps, appropriate measures can be taken to reduce the risk of failure. For example, the maximum engine speed can be reduced accordingly to increase the remaining life to the required value. In addition to the maximum engine speed can be understood as relevant parameters in the above sense and complex settings, i. For example, an alternative engine map can be activated which is less stressful for the critical component.
  • Relevant components of the vehicle are monitored continuously or discontinuously via an on-board diagnostic sensor device 9.
  • the data of this diagnostic sensor device 9 are supplied to the measuring system 2 and the evaluation software 3. Simultaneously with the state of the components, the actual duration of use, or the wegrange of the component is detected.
  • the data archive 4 are for each component to be observed reference paths, or reference periods that are stored to the occurrence of damage. If a damage is detected for the component, then the evaluation software 3 compares the damage path, or the damage period until the occurrence of this damage for the respective component with a reference path, or the reference time period for this damage in the data archive 4.
  • an acceleration factor is determined, which indicates whether the damage occurred before or after the statistically collected reference path, or reference period.
  • an update of the data archive 4 can be performed. Furthermore, it is possible to carry out an update for failed or faulty data and to send the status of the measurement evaluation to a central computer. However, the data volumes to be transmitted are low due to the evaluations carried out on board the vehicle. It is essential that the method for damage prediction can continue unhindered even if the radio signal 10 fails.
  • the damage components of all damage path or the damage periods of the component are summed and compared with a stored in the database 4 maximum value. Upon reaching this maximum value, damage to the component is detected. On the other hand, from the difference to the maximum value, the remaining life of the component can be estimated. More accurate results can be achieved if the remaining lifetime is determined on the basis of the lifetime model 6 stored in a database 8. It is particularly advantageous if the life models 6 are associated with damage models 7.
  • the engine torque M M and the engine speed n M are used as sensor signals for monitoring the plug-in pump.
  • the pump speed n is determined from the engine speed n M and the Hertz pressure p 0 from the respective injection pressures in the associated engine operating point.
  • the injection pressures can be stored in the basic map in the system.
  • Fig. 2 From the respective residence time and the damage in the various engine operating points results in the current damage to the plug-in pump, which exemplifies in Fig. 2 is shown.
  • the damage S of the plug-in pump per hour is plotted against the engine load L and the engine speed n M ( Fig. 2 ).
  • Fig. 3 the residence time V of the operating points in the engine map over the engine torque M M and the engine speed n M is specified.
  • Fig. 4 shows the normalized damage S n of the plug-in pump per hour and the normalized dwell time V n in the engine map in a diagram. From this, the concrete damage to the plug-in pump is calculated.
  • Fig. 5 the calculated damage S r for the specific case for a distance after the vehicle is parked above the engine torque M M and the engine speed n M is shown.
  • the damage distance is then set in relation to a reference distance.
  • the average load spectrum for the standard user is referred to here as reference collective. This is entered at the beginning of the measurements or during the measurements in the system.
  • the ratio of the distance profile to the reference profile gives the acceleration factor.
  • the method according to the invention allows a real-time identification of stress or damage to the entire vehicle during the test operation. It is possible to make life predictions related to the expected average load spectra. The results of the damage analysis can be incorporated into the damage models, allowing a self-calibration of the system.
  • the process significantly reduces test times, optimizes test runs / cycles for specific components, as well as the overall system, and monitors and records system behavior over time.
  • the damage models tuned to the particular system may be fed to the vehicle control unit for a more accurate prediction of service intervals.
  • An important advantage of the method is that the development time for vehicles can be significantly reduced. Another advantage of the method according to the invention is to minimize the probability of failure of components by appropriate measures.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Testing Of Engines (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Control Of Electric Motors In General (AREA)
EP08166916A 2007-11-02 2008-10-17 Procédé de prévention contre les dommages de composants d'un véhicule automobile Withdrawn EP2056179A3 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
AT0177107A AT504028B1 (de) 2007-11-02 2007-11-02 Verfahren zur schädigungsvorhersage von bauteilen eines kraftfahrzeuges

Publications (2)

Publication Number Publication Date
EP2056179A2 true EP2056179A2 (fr) 2009-05-06
EP2056179A3 EP2056179A3 (fr) 2010-05-26

Family

ID=39032430

Family Applications (1)

Application Number Title Priority Date Filing Date
EP08166916A Withdrawn EP2056179A3 (fr) 2007-11-02 2008-10-17 Procédé de prévention contre les dommages de composants d'un véhicule automobile

Country Status (5)

Country Link
US (1) US20090118897A1 (fr)
EP (1) EP2056179A3 (fr)
JP (1) JP2009115796A (fr)
CN (1) CN101424590B (fr)
AT (1) AT504028B1 (fr)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102010054531A1 (de) 2010-12-15 2012-06-21 Volkswagen Ag Verfahren zur Bestimmung eines Zustands eines Verbrennungsmotors eines Fahrzeugs mit weiterem Motor sowie entsprechende Vorrichtung und Fahrzeug
GB2491045A (en) * 2011-05-20 2012-11-21 Romax Technology Ltd Determining damage to, or remaining useful life of, rotating machinery eg drive trains, gearboxes and generators of wind and water turbines
WO2016005086A1 (fr) * 2014-07-11 2016-01-14 Robert Bosch Gmbh Dispositif et procédé servant à déterminer des profils de charge de véhicules automobiles
WO2017192998A1 (fr) * 2016-05-06 2017-11-09 General Electric Company Commande d'opérations d'aéronef et attribution de composants de moteur d'aéronef
EP3361450A1 (fr) * 2017-02-10 2018-08-15 Hitachi, Ltd. Prévention de défaillance de composant de véhicule
WO2018162198A1 (fr) * 2017-03-08 2018-09-13 Siemens Aktiengesellschaft Procédé et système de détermination d'une durée de vie attendue d'un moyen de fonctionnement électrique
US10318904B2 (en) 2016-05-06 2019-06-11 General Electric Company Computing system to control the use of physical state attainment of assets to meet temporal performance criteria
WO2020074115A1 (fr) * 2018-10-10 2020-04-16 Deutz Aktiengesellschaft Procédé pour la détection et la prédiction de l'encrassement d'un refroidisseur ecr dans un moteur diesel à combustion interne

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DE102008049754A1 (de) 2008-09-30 2010-04-08 Continental Automotive Gmbh Verfahren und Vorrichtung zur Verschleißdiagnose eines Kraftfahrzeugs
FR2996192B1 (fr) * 2012-10-02 2015-05-01 Eurodrive Services And Distrib N V Procede de determination de l'etat d'usure d'une piece et d'information d'un client
CN105122029A (zh) * 2013-04-22 2015-12-02 沃尔沃卡车集团 监视车辆系统的健康状态的方法
DE102013211543A1 (de) * 2013-06-19 2014-12-24 Robert Bosch Gmbh Verfahren zum alterungs- und energieeffizienten Betrieb insbesondere eines Kraftfahrzeugs
DE102015120107A1 (de) * 2015-11-19 2017-05-24 Technische Universität Darmstadt Verfahren zur Auslegung und Dimensionierung eines Neuteils eines Kraftfahrzeugs
CN107292394A (zh) * 2016-04-11 2017-10-24 富泰华工业（深圳）有限公司 车辆损伤定价系统及方法
DE102017106919A1 (de) * 2017-03-30 2018-10-04 Technische Universität Darmstadt Verfahren zur Bestimmung einer Schädigungsmaßunsicherheit eines Kraftfahrzeugs
US20190378349A1 (en) * 2018-06-07 2019-12-12 GM Global Technology Operations LLC Vehicle remaining useful life prediction
US11087566B2 (en) * 2019-04-16 2021-08-10 Verizon Patent And Licensing Inc. Determining vehicle service timeframes based on vehicle data
US11669760B2 (en) * 2019-12-17 2023-06-06 Palo Alto Research Center Incorporated Weight assignment for fusion of prognostic estimators
JP7392615B2 (ja) * 2020-09-18 2023-12-06 トヨタ自動車株式会社 ベルト余寿命診断装置
CN113624523B (zh) * 2021-06-21 2024-06-25 广州市科叶环保科技有限公司 一种水泵寿命可靠性加速测试方法
CN114622974B (zh) * 2022-05-16 2022-08-23 山东新凌志检测技术有限公司 一种机动车尾气智能检测诊断系统及方法

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Cited By (16)

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Publication number Priority date Publication date Assignee Title
WO2012079716A1 (fr) 2010-12-15 2012-06-21 Volkswagen Aktiengesellschaft Procédé de détermination de l'état d'un moteur à combustion interne d'un véhicule équipé d'un moteur électrique additionnel, ainsi que dispositif correspondant et véhicule
DE102010054531A1 (de) 2010-12-15 2012-06-21 Volkswagen Ag Verfahren zur Bestimmung eines Zustands eines Verbrennungsmotors eines Fahrzeugs mit weiterem Motor sowie entsprechende Vorrichtung und Fahrzeug
US10466138B2 (en) 2011-05-20 2019-11-05 Andy Poon Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
GB2491045A (en) * 2011-05-20 2012-11-21 Romax Technology Ltd Determining damage to, or remaining useful life of, rotating machinery eg drive trains, gearboxes and generators of wind and water turbines
GB2491045B (en) * 2011-05-20 2013-10-30 Romax Technology Ltd Determining damage and remaining useful life of rotating machinery including drive trains, gearboxes, and generators
US10527520B2 (en) 2011-05-20 2020-01-07 Insight Analytics Solutions Holdings Limited Operating wind motors and determining their remaining useful life
WO2016005086A1 (fr) * 2014-07-11 2016-01-14 Robert Bosch Gmbh Dispositif et procédé servant à déterminer des profils de charge de véhicules automobiles
US10692310B2 (en) 2014-07-11 2020-06-23 Robert Bosch Gmbh Device and method for determining load profiles of motor vehicles
US10318904B2 (en) 2016-05-06 2019-06-11 General Electric Company Computing system to control the use of physical state attainment of assets to meet temporal performance criteria
US10318903B2 (en) 2016-05-06 2019-06-11 General Electric Company Constrained cash computing system to optimally schedule aircraft repair capacity with closed loop dynamic physical state and asset utilization attainment control
US10417614B2 (en) 2016-05-06 2019-09-17 General Electric Company Controlling aircraft operations and aircraft engine components assignment
WO2017192998A1 (fr) * 2016-05-06 2017-11-09 General Electric Company Commande d'opérations d'aéronef et attribution de composants de moteur d'aéronef
US10424132B2 (en) 2017-02-10 2019-09-24 Hitachi, Ltd. Vehicle component failure prevention
EP3361450A1 (fr) * 2017-02-10 2018-08-15 Hitachi, Ltd. Prévention de défaillance de composant de véhicule
WO2018162198A1 (fr) * 2017-03-08 2018-09-13 Siemens Aktiengesellschaft Procédé et système de détermination d'une durée de vie attendue d'un moyen de fonctionnement électrique
WO2020074115A1 (fr) * 2018-10-10 2020-04-16 Deutz Aktiengesellschaft Procédé pour la détection et la prédiction de l'encrassement d'un refroidisseur ecr dans un moteur diesel à combustion interne

Also Published As

Publication number Publication date
EP2056179A3 (fr) 2010-05-26
CN101424590A (zh) 2009-05-06
US20090118897A1 (en) 2009-05-07
AT504028B1 (de) 2009-03-15
JP2009115796A (ja) 2009-05-28
AT504028A2 (de) 2008-02-15
AT504028A3 (de) 2008-10-15
CN101424590B (zh) 2013-08-14

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