System state prediction

1. A method for predicting a future state of a physical system, the method comprising:

providing a state space model of behaviour of the physical system, the state space model including covariances for state transition and measurement errors;

providing a data-based regression model for prediction of state variables of the physical system;

observing a state vector comprising state variables of the physical system;

determining a prediction vector of state variables based on the state vector, using the data-based regression model;

combining an output of the state space model with an output of the data-based regression model through a Bayesian filter implemented with a Kalman filter to produce a joint state prediction, wherein the combining further includes replacing at least one state vector of the Kalman filter with the prediction vector determined using the data-based regression model; and

outputting a signal to activate a countermeasure within the physical system to prevent a pending critical situation of the physical system.

2. The method according to claim 1 , wherein the Bayesian filter is an Extended Kalman filter.

3. The method according to claim 1 , wherein the data-based regression model comprises a trained Recurrent Neural Network.

4. The method according to claim 3 , wherein the physical system is time continuous, the Recurrent Neural Network is interpolated between discrete time steps and the Bayesian filter comprises a Continuous Kalman Filter.

5. An apparatus comprising:

an interface for observing a state vector comprising state variables in a physical system;

a processing means, adapted to carry out the method according to claim 1 .

6. The method according to claim 1 , further comprising: outputting a signal that identifies a pending critical situation of the physical system.

7. The method according to claim 1 , further comprising: protecting a motor of the physical system based on the future state of the physical system.

8. The method according to claim 1 , further comprising:

receiving measurements from one or more sensors of the physical system;

providing a state variable forecast based on the measurements;

determining that the state variable forecast indicates a pending critical situation of the physical system by comparing the state variable forecast to a predetermined threshold; and

implementing a countermeasure within the technical system to prevent an occurrence of the pending critical situation.

9. The method according to claim 1 , further comprising:

splitting an available historical data of the physical system into a training data set and a validation data set;

training the data-based regression model using the training data set; and

applying the data-based regression model to the validation data set for predicting system outputs to given inputs.

10. The method according to claim 1 , wherein the at least one state vector of the Kalman filter relates to a time step.