journal article·2021-04

Load assumption process for durability design using new data sources and data analytics

Michael Burger, Klaus Dreßler, Michael Speckert

Publisher page

Abstract

Abstract Durability engineering for vehicles is about relating the real operational loading and fatigue environment to the actual capability and strength capacity of the product and its parts. The statistical modelling of the usage loading distribution has always been a big challenge; the latter is necessary to derive statistically validated design targets. In the past, dedicated durability measurement campaigns were the only available data source to obtain durability loads. Nowadays, this has changed due to the increasing availability of new complementary data sources. We show how such data like field monitoring data and geographic data, along with the usage of new data analytics and simulation methods can be used to derive customer and usage related durability loads and targets.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Infrastructure Maintenance and Monitoring · Transport Systems and Technology · Structural Health Monitoring Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (35)

Show source-provided references
1. Johannesson · 2013
2. Dressler K, Speckert M, Müller R, Weber C. Customer loads correlation in truck engineering. In: Proceedings of FISITA world automotive congress (F200812198), München; 2008.
3. Customer usage profiles, strength requirements and test schedules in truck engineering
4. Speckert M, Dressler K, Lübke M. T. Halfmann, Automatisierte und um GEO-Daten angereicherte Auswertung von Messdaten zur Herleitung von Beanspruchungs-Verteilungen. In: DVM-Bericht 143, DVM; 2016. p. 165–80.
5. The Virtual Measurement Campaign (VMC) concept - A methodology for geo-referenced description and evaluation of environmental conditions for vehicle loads and energy efficiency
6. Dressler K, Speckert M. Simulation der Nutzungsvariabilität für Betriebsfestigkeit und Energieeffizienz unter Verwendung geo-referenzierter Daten, in: VDI-Bericht 2224, VDI; 2014. p. 439–50.
7. Die virtuelle Messkampagne (VMC) - ein geo-referenziertes System für die Fahrzeugauslegung hinsichtlich Beanspruchung
8. Burger M, Speckert M, Dressler K. Nutzungsspezifische Vorhersage von Beanspruchung und Energieverbrauch. In: VDI-Bericht 2333, VDI; 2018. p. 781–93.10.51202/9783181023334-781 ↗
9. Ableitung von feldrelevanten Lastkollektiven mittels stochastischer Verkehrssimulation
10. Model-Based Identification Of Road Profiles and Road Roughness Indicators Using Vehicle Measurements
11. Burger M, Dressler K, Speckert M. New Data Sources and Data Analytics Methods for Durability Engineering. In: Proceedings of the fourth international conference on material and component performance under variable amplitude loading (VAL 4), March 30 - April 3, 2020 in Darmstadt, Germany; 2020.
12. Determining Customer-Correlated Loads by Using the Monte Carlo Method
13. Ableitung von einsatzspezifischen Auslegungslasten der Betriebsfestigkeit - Ermittlung globaler Marktanforderungen am Beispiel des südamerikanischen Marktes
14. Beanspruchungskollektive für Nutzfahrzeuge in Gesamteuropa - Ein innovativer Extrapolationsansatz10.3139/120.110247 ↗
15. Streit A, Dressler K, Speckert M, Lichter J, Zenner T, Bach P. Anwendung statistischer Methoden zur Erstellung von Nutzungsprofilen für die Auslegung von Mobilbaggern. In: VDI-Bericht 2065, VDI; 2009, pp. 65–77.
16. Determination of test scenarios for durability verification of tractors under consideration of their usage variability?
17. Montgomery · 2003
18. Modelling of road profiles using roughness indicators10.1504/IJVD.2014.066068 ↗
19. Weyh T, Speckert M, Opalinski A, Wagner M. Planung einer Messkampagne durch Osteuropa mittels der Fraunhofer Software VMC (”Virtual Measurement Campaign”), in: VDI-Bericht 2247, VDI; 2015. p. 207–18.
20. Planning and conducting a measurement campaign and derivation of customer-specific load distributions for light duty trucks in China
21. Biedinger C, Weyh T, Opalinski A, Wagner M. Simulation of customer-specific vehicle usage. In: Proceedings of commercial vehicle technology 2016, Shaker; 2016.
22. Modellierung des Verschleißverhaltens von Baumaschinen, Master’s thesis
23. Bishop · 2006
24. Murphy · 2012
25. Hastie · 2009
26. The world’s user-generated road map is more than 80% complete10.1371/journal.pone.0180698 ↗
27. Gillespie T, Sayers M, Segel L. Calibration of response-type road roughness measuring systems, NCHRP Report 228.
28. Gillespie T, Sayers M, Queiroz C. The international road roughness experiment: Establishing correlation and calibration standard for measurement, Technical Report No. 45, The World Bank.
29. ISO (1995-09-01) Mechanical vibration, Road surface profiles, Reporting of measured data, International Organization for Standardization (ISO), ISO 8608:1995(E).
30. Calculating Invariant Loads for System Simulation in Vehicle Engineering
31. Burger M. Optimal Control of Dynamical Systems: Calculating Input Data for Multibody System Simulation. Dissertation. TU Kaiserslautern, Verlag Dr. Hut; 2011.
32. Calculating road input data for vehicle simulation10.1007/s11044-013-9380-9 ↗
33. Estimation of road profile variability from measured vehicle responses10.1080/00423114.2016.1145243 ↗
34. The use of vehicle acceleration measurements to estimate road roughness10.1080/00423110701485050 ↗
35. Dijkstra B. Iterative learning control, PhD thesis, TU Delft; 2004.