Reliability Prediction Based on Variation Mode and Effect Analysis
Article first published online: 18 JUN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Quality and Reliability Engineering International
Volume 29, Issue 5, pages 699–708, July 2013
How to Cite
Pavasson, J., Cronholm, K., Strand, H. and Karlberg, M. (2013), Reliability Prediction Based on Variation Mode and Effect Analysis. Qual. Reliab. Engng. Int., 29: 699–708. doi: 10.1002/qre.1420
- Issue published online: 25 JUL 2013
- Article first published online: 18 JUN 2012
- Manuscript Accepted: 12 APR 2012
- Manuscript Revised: 2 JAN 2012
- Manuscript Received: 2 DEC 2010
- reliability engineering;
- clutch shaft;
- engineering design
The possibility of predicting the reliability of hardware for both components and systems is important in engineering design. Today, there are several methods for predicting the reliability of hardware systems and for identifying the causes of failure and failure modes, for example, fault tree analysis and failure mode and effect analysis.
Many failures are caused by variations resulting in a substantial effect on safety or functional requirements. To identify, to assess and to manage unwanted sources of variation, a method called probabilistic variation mode and effect analysis (VMEA) has been developed. With a prescribed reliability, VMEA can be used to derive safety factors in different applications. However, there are few reports on how to derive the reliability based on probabilistic VMEA, especially for transmission clutch shafts.
Hence, the objective of this article was to show how to derive system reliability based on probabilistic VMEA. In particular, wheel loader automatic transmission clutch shaft reliability is investigated to show how different sources of variation affect reliability.
In this article, a new method for predicting system reliability based on probabilistic VMEA is proposed. The method is further verified by a case study on a clutch shaft. It is shown that the reliability of the clutch shaft was close to 1.0 and that the most significant variation contribution was due to mean radius of the friction surface and friction of the disc. Copyright © 2012 John Wiley & Sons, Ltd.