engleski

Data Modelling in Risk Assessment

There are two sources of information used in the Bayesian approach to risk modelling: measurement data and a priori beliefs of an experienced measurer about the possible distribution of parameters that describes the measurement data. With each measurement, there is a problem of compliance of the measurement results with the manufacturer's specifications. When the measurement result is outside the tolerance interval and the actual value within the tolerance interval, we are talking about the producer's risk. If the measurement result is within the tolerance interval, and the actual value is outside that interval, we are talking about consumer's risk. The consumer's risk can be reduced by setting acceptance interval. The upper acceptance limit of roundness deviation for the inner bearing ring has been evaluated in this paper. The assessment was carried out for consumer risks of 0.1 % to 0.5 %. The measurement data are assumed to belong to a normal distribution. Since the roundness deviation of the inner bearing ring is always a positive value, the following prior distributions were selected: gamma distribution, beta distribution, one-parameter Rayleigh distribution, and two uniform distributions on different intervals.

Consumer's risk, producer's risk, tolerance interval, acceptance interval, guard band

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