Advanced production plant optimization with AI
Jayant R. Kalagnanam, Dariusz Piotrowski, et al.
ADIP 2019
This article investigates the modeling of a new type of degradation data: spatio-temporal degradation data collected from a spatial domain over time. Like existing stochastic degradation models, a random field is constructed to describe the spatio-temporal degradation process. We model the degradation process as an additive superposition of two stochastic components: a dynamic spatial degradation generation process and a spatio-temporal propagation process. Some common challenges are addressed, including the spatial heterogeneity of the degradation process, spatial propagation of degradation to neighboring areas, anisotropic and space-time non-separable covariance structures associated with a complex spatio-temporal degradation process, and the computational issues related to parameter estimation and simulation. When spatial dependence is ignored, we show that the proposed spatio-temporal degradation model incorporates some existing purely time-dependent degradation models as its special cases. We also show the connection, under special conditions, between the proposed statistical model and a class of physical-degradation processes given by stochastic partial differential equations. Numerical examples are presented to illustrate modeling approach, parameter estimation, model validation, and applications.
Jayant R. Kalagnanam, Dariusz Piotrowski, et al.
ADIP 2019
Kyongmin Yeo, Martin R. Maxey
Physics of Fluids
Kyongmin Yeo, Małgorzata J. Zimoń, et al.
Physical Review Letters
Arun Iyengar, Jayant R. Kalagnanam, et al.
ICDCS 2019