Forecasting of machine outages have been actively pursued in the manufacturing industries to ensure that maintenance is carried out only when required. In this paper, we propose a precognitive maintenance framework based on mixed timeand condition-based models to predict both machine degradation stage and wear. The decision-making framework is based on stage classification using Support Vector ...

The identification of dynamic processes can be performed by means of different classes of models relying on different stochastic environments to describe the misfit between the model and process observations. This paper introduces a new class of models by considering additive error terms on the observations of the input and output of ARARX models and proposes a three–step identification procedu...

This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and outpu...

We analyse the relationship between a mobile robot’s perception, motion and position (i.e. we address the problem of sensor-based self-localisation), by constructing transparent, linear polynomial models, using an ARMAX process which expresses the interdependency between motion, perception and location. Our models show that in sensor-based localisation there is a clear and measurable trade-off ...

We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems. The impulse responses are modeled as Gaussian processes whose autocovariances encode the BIBO stability constraint, as defined by the recently introduced “Stable Spline kernel”. Sparse solutions are obtained by placing exponential hyperpriors on the scale factors of such kernels. Numerical exp...

estimation of agricultural sector demand and supply and identification of its determinants could lead to more efficient policies and planning of this section. in this study, aggregate demand and supply of agriculture sector for years of 1959-2007 by using of nonlinear restricted armax model are estimated. result indicates that aggregate agriculture demand is inelastic in short and long run. tho...

• Traditional approaches, including Box–Jenkins autoregressive integrated moving average (ARIMA) model, autoregressive and moving average with exogenous variables (ARMAX) model, seasonal autoregressive integrated moving average (SARIMA) model, exponential smoothing models [including Holt–Winters model (HW) and seasonal Holt and Winters’ linear exponential smoothing (SHW)], state space/Kalman fi...