A generic Bayesian approach for model-based prognostics with Laplace approximation

This paper presents a generic Bayesian framework using Laplace approximation for model-based remaining useful life prognosis. The developed generic Bayesian prognosis approach models and updates remaining useful life distributions by incorporating timely evolving sensory data using a general Bayesian inference mechanism and employs an efficient Bayesian updating approach using an Laplace approach (LA) method. The developed Bayesian prognosis approach eliminates the dependency of evolutionary updating process on a selection of distribution types for the parameters for a given system degradation model. Furthermore, with the developed LA method, the Bayesian updating process can be carried out efficiently which makes the proposed approach possible for real-time prognosis applications. The proposed Bayesian prognosis methodology is generally applicable for different degradation models without prior distribution constraints as faced by conjugate or semi-conjugate Bayesian inference models. Two practical prognosis applications are employed in this study to demonstrate the efficacy of the proposed Bayesian prognosis methodology.