Recent advances in data acquisition, storing, and recording technology are allowing increasingly detailed samplings of actual customer usage of product. Because of this, laboratories are coming forward to use the actual road data rather than simulated data or data collected from the proving ground. This research work is an attempt to find a representative length of data (short-term data) from the large-distance data (long-term data). For any particular class of vehicle, finding the short-term data, which are representative of long-term data, will avoid the collection of large-distance data in the future.
In this paper, using the Hurst methodology an attempt have been made to find the short-term data that have to be collected, so as to be representative of the long-term data. In this study, data have been collected both on the proving ground as well as on an actual road for a distance of 1700km on a light commercial vehicle. It has been seen from the Hurst exponent that, collecting data for an actual road length of about 200km would give a representation for an actual road length of 1000km.
Rescaled range analysis of service load data
2010-03-01
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Rescaled range analysis of service load data
Online Contents | 2010
|Rescaled range of analysis of service load data
Kraftfahrwesen | 2010
|Reproducibility of Automotive Tests Using Rescaled Range Analysis
SAE Technical Papers | 2017
|Reproducibility of Automotive Tests Using Rescaled Range Analysis
British Library Conference Proceedings | 2017
|