A probabilistic estimation approach for the failure forecast method using Bayesian inference

Abstract

Positive-feedback mechanisms such as fatigue induce a self-accelerating behavior, captured by models displaying infinite limit-state asymptotics, collectively known as the failure forecast method (FFM). This paper presents a Bayesian model parameter estimation approach to the fully nonlinear FFM implementation and compares the results to the classic linear regression formulation, including a regression uncertainty model. This process is demonstrated in a cyclic loading fatigue crack propagation application, both on a synthetic data set and on a full fatigue experiment. A novel "switch point" parameter is included in the Bayesian formulation to account for nonstationary changes in the growth parameter.

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

“…The posterior predictive distribution measures the possible unobserved values distribution on observed values conditional in Bayesian statistics [44][45][46][47][48].…”