Bayesian analysis with consideration of data uncertainty in a specific scenario
Abstract In this paper data uncertainty in a specific scenario is modeled with Berkson and Classical errors. With consideration of uncertainty three methods within Bayesian framework are presented to update the failure probability with Beta-Binomial model. It shows that the three methods have their posteriors in the same form of weighted Beta distributions, but the weights are different for each method. Approximation to the mixed posteriors has been proposed and demonstrated by computation results. Moreover, comparison and illustration of the three methods are made based on case study and analytical analysis, which suggest that the LO method with Classical error model be more appropriate in similar applications.
According to the principle and algorithm of BP neural network,the structure of vibrating sieve can be determined.The BP neural network model for fault diagnosing is obtained by taking the characteristic variables of operating state of vibrating sieve as the input of neural network and using the Matlab neural network toolbox for the network training.The experimental result shows that the method can accurately diagnose the fault of vibrating sieve.