DocumentCode :
778942
Title :
A discretizing approach for stress/strength analysis
Author :
English, John R. ; Sargent, Tom ; Landers, Thomas L.
Author_Institution :
Dept. of Ind. Eng., Arkansas Univ., Fayetteville, AR, USA
Volume :
45
Issue :
1
fYear :
1996
fDate :
3/1/1996 12:00:00 AM
Firstpage :
84
Lastpage :
89
Abstract :
This paper implements and evaluates a discretizing approach for estimating the reliability of systems for which complex functions define strength or stress and where the derivation of reliability exceed analytic techniques. The discretizing approach predicts system reliability with reasonably high accuracy. Specifically, there is little difference in the accuracy of predictions for three engineering problems when compared to simulation results. The reliability predictions are near the 95% confidence intervals of the simulation results and are best in the high reliability and low reliability regions. The small errors observed are attributed to the estimation errors of the discretizing approach. The mid-range reliability values (e.g. 50% reliability) are not generally of interest in engineering applications, and even for these value, the errors are small. There is little improvement in increasing the number of points in the pmf from 3 to 6. Due to this small difference, 3 discretizing points are recommended for reliability predictions when computational ease is of concern and limited to 4 points when more accurate reliability predictions are required. This paper models three systems and evaluates the robustness (departures from assumed distributions) of the discretizing approach. The discretizing approach is not too sensitive to departures from the assumed distribution of the underlying random variables regions are accurately estimated
Keywords :
mechanical strength; random processes; reliability theory; stress analysis; Gaussian case; confidence intervals; discretizing approach; high reliability; low reliability; near Gaussian case; reliability estimation; robustness evaluation; stress/strength analysis; system reliability prediction; underlying random variables regions; Engine cylinders; Interference; Marine vehicles; Reliability engineering; Reliability theory; State estimation; Statistical distributions; Stress; System testing; Torque;
fLanguage :
English
Journal_Title :
Reliability, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9529
Type :
jour
DOI :
10.1109/24.488921
Filename :
488921
Link To Document :
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