System state prediction

Patent metadata

Field	Value
Publication number	US-11347996-B2
Application number	US-201816106153-A
Country	US
Kind code	B2
Filing date	Aug 21, 2018
Priority date	Aug 8, 2017
Publication date	May 31, 2022
Grant date	May 31, 2022

Abstract

A method which includes steps of providing a state space model of behaviour of a physical system, the 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 regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

First claim

The invention claimed is:

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.

Assignees

• Siemens Ag

Inventors

• Allmaras Moritz
• Obst Birgit

Classifications

• G06N3/042

  Knowledge-based neural networks; Logical representations of neural networks · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G05B13/027 Primary

  using neural networks only · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title