Variation mode and effect analysis: an application to fatigue life prediction
Journal article, 2009

We present an application of the probabilistic branch of variation mode and effect analysis (VMEA) implemented as a first-order, second-moment reliability method. First order means that the failure function is approximated to be linear around the nominal values with respect to the main influencing variables, while second moment means that only means and variances are taken into account in the statistical procedure. We study the fatigue life of a jet engine component and aim at a safety margin that takes all sources of prediction uncertainties into account. Scatter is defined as random variation due to natural causes, such as non-homogeneous material, geometry variation within tolerances, load variation in usage, and other uncontrolled variations. Other uncertainties are unknown systematic errors, such as model errors in the numerical calculation of fatigue life, statistical errors in estimates of parameters, and unknown usage profile. By treating also systematic errors as random variables, the whole safety margin problem is put into a common framework of second-order statistics. The final estimated prediction variance of the logarithmic life is obtained by summing the variance contributions of all sources of scatter and other uncertainties, and it represents the total uncertainty in the life prediction. Motivated by the central limit theorem, this logarithmic life random variable may be regarded as normally distributed, which gives possibilities to calculate relevant safety margins.

probabilistic VMEA

life prediction safety factor

fatigue life


Pär Johannesson

Thomas Svensson

Leif Samuelsson

Bo Bergman

Chalmers, Technology Management and Economics, Quality Sciences

Jacques de Maré

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Quality and Reliability Engineering International

0748-8017 (ISSN) 1099-1638 (eISSN)

Vol. 25 2 167-179

Subject Categories

Probability Theory and Statistics



More information