Accelerated Degradation Reliability Modeling Based on Multiple Uncertainty Wiener Processes

Abstract

Numerous scholars' studies usually adopt the Wiener process to establish industrial robot performance degradation models and randomize the drift coefficients to characterize the differences in individual degradation rates, but they do not take into account the influence of measurement errors on the accuracy of the models. Aiming at the above problems, combining the Wiener process and the accelerated degradation model, the accelerated degradation reliability model of industrial robots based on the multi-uncertainty Wiener process is established, and by adding the factor of measurement error, the model is able to more accurately characterize the influence of external factors on the degradation process of industrial robots. Bayesian theory, Markov chain Monte Carlo method and Bootstrap method are used to estimate the unknown parameters in the model. Finally, the accuracy of the proposed parameter estimation method and model is verified by simulating the industrial robot performance degradation data with Monte Carlo method.