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Proving Ground Optimization and Damage Correlation with Customer Usage

Journal Article
2011-01-0484
ISSN: 1946-3979, e-ISSN: 1946-3987
Published April 12, 2011 by SAE International in United States
Proving Ground Optimization and Damage Correlation with Customer Usage
Sector:
Citation: Halfpenny, A. and Pompetzki, M., "Proving Ground Optimization and Damage Correlation with Customer Usage," SAE Int. J. Mater. Manuf. 4(1):620-631, 2011, https://doi.org/10.4271/2011-01-0484.
Language: English

Abstract:

Proving grounds are an extremely efficient means of qualifying the durability of vehicle components. They accelerate damage accumulation rates so failures are detectable in a very short period of time. It is important that proving ground damage is correlated with target customer usage. It is also important to determine the most efficient use of the proving ground in order to meet project targets and minimize overall development costs. This paper describes the latest techniques for proving ground correlation and optimization.
Acceleration, strain, wheel force and other types of data are collected on a vehicle as it traverses different proving ground surfaces. Comparable data are also collected from instrumented ‘customer’ vehicles. The objective of the analysis is to determine which mixture of proving ground surfaces offers the best representation of customer usage while minimizing the total test time.
Prior to the optimization stage, time series data must be characterized and reduced to an efficient format. Techniques such as rainflow cycle counting and level crossing are discussed and recommendations are made on the merits of each approach. A new technique based on a damage-weighted frequency spectrum is introduced. This offers significant improvements in damage-based optimization when used with acceleration measurements.
The proving ground schedule will tend to be more severely damaging in some components than others. The paper discusses the choice of measurement location in optimizing overall vehicle damage. It also describes how to establish which components are under-tested (i.e. not expected to fail) and which are over-tested (i.e. fail early) in the proving ground schedule.