Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment

Abstract

The investigation of the reliability of long-life equipment is typically hindered by the lack of experimental data, which makes accurate assessments challenging. To address this problem, a bootstrap method based on the improved RBF (radial basis function) neural network is proposed. This method utilizes the exponential function to modify the conventional empirical distribution function and fit right-tailed data. In addition, it employs the RBF radial basis neural network to obtain the distribution characteristics of the original samples and then constructs the neighborhood function to generate the input network. The expanded sample is used to estimate the scale and shape parameters of the Weibull distribution and obtain the estimated value of the MTBF (mean time between failures). The bias correction method is then used to obtain the interval estimate for the MTBF. Subsequently, a simulation experiment is conducted based on the failure data of a CNC (computer numerical control) machine tool to verify the effect of this method. The results show that the accuracy of the MTBF point estimation and interval estimation obtained using the proposed method is superior to those of the original and conventional bootstrap methods, which is of major significance to engineering applications.