SYMPOSIA PAPER Published: 01 January 2002
STP10850S

Statistical Prediction of the Maximum Inclusion Size in Bearing Steels

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Large and brittle oxide inclusions may initiate fatigue failure in bearing steels. The size of the maximum inclusion in a large volume must be predicted by statistical analysis because only small samples can directly be analysed and there are limitations on non-destructive testing methods. A new method based on the Generalized Pareto distribution (GPD) was recently proposed by the Sheffield group. This allows data on inclusion sizes in small samples to be used to predict the maximum inclusion size in a large volume of steel. The method has advantages over other statistics of extremes methods. The number of sources of failure and the failure rate of practical bearings can be estimated from the predictions of the GPD and the stress distribution in the bearing. Here the GPD method is compared with the Statistics of extreme values (SEV) method developed by Murakami and co-workers. The application of predictions from the GPD in the safe design of bearings will be illustrated.

Author Information

Shi, G
University of Sheffield, Sheffield, United Kingdom TWI Limited, Abington, Cambridge, United Kingdom
Atkinson, HV
University of Sheffield, Sheffield, United Kingdom
Sellars, CM
University of Sheffield, Sheffield, United Kingdom
Anderson, CW
University of Sheffield, Sheffield, United Kingdom
Yates, JR
University of Sheffield, Sheffield, United Kingdom
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Details
Developed by Committee: A01
Pages: 125–137
DOI: 10.1520/STP10850S
ISBN-EB: 978-0-8031-5466-7
ISBN-13: 978-0-8031-2894-1