A Robustness Approach to Reliability
Journal article, 2013

Reliability of products is here regarded with respect to failure avoidance rather than probability of failure. To avoid failures, we emphasize variation and suggest some powerful tools for handling failures due to variation. Thus, instead of technical calculation of probabilities from data that usually are too weak for correct results, we emphasize the statistical thinking that puts the designers focus on the critical product functions. Making the design insensitive to unavoidable variation is called robust design and is handled by (i) identification and classification of variation, (ii) design of experiments to find robust solutions, and (iii) statistically based estimations of proper safety margins. Extensions of the classical failure mode and effect analysis (FMEA) are presented. The first extension consists of identifying failure modes caused by variation in the traditional bottom–up FMEA analysis. The second variation mode and effect analysis (VMEA) is a top–down analysis, taking the product characteristics as a starting point and analyzing how sensitive these characteristics are to variation. In cases when there is sufficient detailed information of potential failure causes, the VMEA can be applied in its most advanced mode, the probabilistic VMEA. Variation is then measured as statistical standard deviations, and sensitivities are measured as partial derivatives. This method gives the opportunity to dimension tolerances and safety margins to avoid failures caused by both unavoidable variation and lack of knowledge regarding failure processes.

safety factors

uncertainty

reliability prediction

variation

P-diagram

Author

Pär Johannesson

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Bo Bergman

Chalmers, Technology Management and Economics, Quality Sciences

Thomas Svensson

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Martin Arvidsson

Chalmers, Technology Management and Economics, Quality Sciences

Åke Lönnqvist

Chalmers, Technology Management and Economics, Quality Sciences

Stefano Barone

Chalmers, Technology Management and Economics, Quality Sciences

Jacques de Maré

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Quality and Reliability Engineering International

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

Vol. 29 1 17-32

Subject Categories

Other Mechanical Engineering

Probability Theory and Statistics

Roots

Basic sciences

DOI

10.1002/qre.1294

More information

Created

10/8/2017