Stationary non-Gaussian random vibration control: A review

A translation model for non-stationary, non-Gaussian random processes

A model for simulation of non-stationary, non-Gaussian processes based on non-linear translation of Gaussian random vectors is presented. This method is a generalization of traditional translation processes that includes the capability of simulating samples with spatially or temporally varying marginal probability density functions. A formal development of the properties of the resulting proces...

Simulation of Sample Paths of Non Gaussian Stationary Random Fields

Mathematical justifications are given for a simulation technique of multivariate nonGaussian random processes and fields based on Rosenblatt’s transformation of Gaussian processes. Different types of convergences are given for the approaching sequence. Moreover an original numerical method is proposed in order to solve the functional equation yielding the underlying Gaussian process autocorrela...

A Method for Anomaly Detection for Non-stationary Vibration Signatures

Vibration signatures contain information regarding the health status of the machine components. One approach to assess the health of the components is to search systematically for a list of specific failure patterns, based on the physical specifications of the known components (e.g. the physical specifications of the bearings, the gearwheels or the shafts). It is possible to do so, since the ma...

A Non–stationary Non-gaussian Hedonic Spatial Model for House Selling Prices a Non–stationary Non-gaussian Hedonic Spatial Model for House Selling Prices

This work proposes a hedonic random field model to describe house selling prices over the period 2000– 2005 in Cedar Falls, Iowa. This real estate market presents two distinctive features that are not described by commonly used stationary Gaussian random field models: (a) the city encompasses within its limits a hog lot which acts as an externality, negatively affecting the selling price of nea...

Autoregressive Gaussian Random Vectors of First Order