An Inverse Gaussian Process Model for Degradation Data

By Xiao Wang, Dihua Xu. Technometrics, 2010

362 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[274] first discussed the application of IG in the degradation process description.”
The Inverse Gaussian Process as a Degradation Model (2014, methodology): “As in Section 3.1 (see also Wang and Xu 2010), the unconditional distribution of Yi for unit i can be derived as fIG(Yi) = (δ̃i) (δ) γ δ γ̃ δ̃i i ni∏ j=1 √√√√ λ2ij 2πy3ij , (5) where δ̃i = ni/2 + δ and γ̃i = γ + ∑ni j=1(yij − μλij )2/ (2μ2yij ).”
Stochastic modelling and analysis of degradation for highly reliable products (2015, methodology): “In the work by Wang and Xu [48], a semiparametric estimation procedure based on the EM algorithm was proposed for the IG process, where the function Λ(t) is estimated nonparametrically.”
Reliability Modeling for Systems with Multiple Degradation Processes Using Inverse Gaussian Process and Copulas (2014, background): “Wang and Xu [4] showed an application in which both Wiener and Gamma processes do not fit the data well.”
Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process (2012, background): “Wang and Xu [23] recently expressed a similar idea.”