The prediction of measurement variability in an automotive application by the use of a coherence formulation

Variability between nominally identical vehicles is an ever-present problem in automotive vehicle design. In this paper, it is shown that it is possible to quantify and, therefore, separate the measurement variability arising from a number of tests on an individual vehicle from the vehicle-to-vehicle variability arising from the manufacturing process with a series of controlled experiments. In this paper, coherence data is used to identify the measurement variability and, thus, to separate these two variability sources. In order to illustrate the methodology, a range of nominally identical automotive vehicles have been tested for NVH (noise, vibration and harshness) variability by exciting the engine mount with an impact hammer and measuring the excitation force and corresponding velocity responses at different points on the vehicle. Normalised standard deviations were calculated for the transfer mobility data, giving variability values of 25.3%, 33.5% and 37.3% for the responses taken at the suspension strut, upper A-pillar and B-pillar, respectively. The measurement variability was determined by taking repeat measurements on a single vehicle, and was found to be 2.9%. The measurement variability predicted by the coherence data on the multi-vehicle tests was compared with the directly taken repeat measurements taken on a single vehicle and these were shown to agree well with one another over the frequency range of interest.

• Record URL:
  https://doi.org/10.1177/0954407017734768
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544070
• Supplemental Notes:
  • © IMechE 2017.
• Authors:
  • Dowsett, Amy
  • O’Boy, Dan
  • Walsh, Stephen
  • Abolfathi, Ali
  • Fisher, Steve
• Publication Date: 2018-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1694-1700
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
  • Volume: 232
  • Issue Number: 12
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4070
  • EISSN: 2041-2991
  • Serial URL: http://pid.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Error analysis; Mathematical prediction; Measurement; Noise; Vibration
• Uncontrolled Terms: Variability
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01715471
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 30 2019 1:01PM