Reducing cyclic testing time for components of automotive suspension system utilising the wavelet transform and the Fuzzy C-Means

T. E. Putra, Shahrum Abdullah, D. Schramm, Mohd. Zaki Nuawi, T. Bruckmann

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This study aims to introduce a novel method for accelerating fatigue tests. Strain signals measured at automotive suspension components were extracted based on the Morlet wavelet producing damaging segments. Furthermore, the segments were clustered using the Fuzzy C-Means to remove the segments having lower energy. The process was able to shorten the strain signals up to 41.4% and it was able to retain at least 90% of the fatigue damage. It reduced the testing time by more than 33%, with equivalent fatigue life. Indirectly, the use of modified strain signals could reduce device operating costs.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalMechanical Systems and Signal Processing
Volume90
DOIs
Publication statusPublished - 1 Jun 2017

Fingerprint

Wavelet transforms
Testing
Suspensions (components)
Fatigue of materials
Fatigue damage
Operating costs

Keywords

  • Clustering
  • Extraction
  • Fatigue
  • Strain

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

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