METHOD FOR PROVIDING A MODEL FOR AT LEAST ONE MACHINE, TRAINING SYSTEM, METHOD FOR SIMULATING AN OPERATION OF A MACHINE, AND SIMULATION SYSTEM

Abstract

In a method for training a model for an electric machine controlled by a control device, a temporal series of measured values that describe an operating variable of the electric machine is received by a training system. An untrained model embodied as an artificial neural network is then trained with the received measured values to produce a trained model. Control variables that describe the control device are determined with the trained model. The training system then receives a temporal series of measured values of a further electric machine that is different from the electric machine and controlled by a further control device. The trained model is then trained further with the computing facility using measured values of the further electric machine to produce a further trained model. The trained model and the further trained model is outputted via a second interface of the training system.

Claims

1.-12. (canceled)

13. A method for training a model for an electric machine controlled by a control device, comprising: receiving a temporal series of measured values that describe an operating variable of the electric machine via a first interface of a training system, training, based on the received measured values, with a computing facility of the training system an untrained model embodied as an artificial neural network to produce a trained model, and determining with the trained model derived control variables that describe the control device, receiving via the first interface of the training system a temporal series of measured values of a further electric machine that is different from the electric machine and controlled by a further control device, further training the trained model with the computing facility using measured values of the further electric machine to produce a further trained model, and outputting the trained model and the further trained model via a second interface of the training system.

14. The method of claim 13, wherein the derived control variables are determined based on control theory data that describe modes of operation of the control device and the further control device.

15. The method of claim 13, wherein the artificial neural network comprises a plurality of concealed layers.

16. The method of claim 13, wherein the measured values describe different operating states of the electric machine.

17. The method of claim 13, wherein the further derived control variables are determined with the further trained model.

18. The method of claim 13, wherein reinforcement learning is used during training.

19. The method of claim 13, wherein the measured values of the electric machine and the measured values of the further electric machine describe different operating states of the electric machine.

20. The method of claim 13, further comprising using as training data during training a target value, to which the operating variable can be regulated by the control device.

21. The method of claim 13, further comprising using the trained model for simulating operation of an electric machine.

22. A training system, comprising: a first interface for receiving a temporal series of measured values of an electric machine and a temporal series of further measured values of a further electric machine which is of a different type than the electric machine, wherein the measured values describe an operating variable of the electric machine, a computing facility configured to train an untrained model using the received measured values and deriving therefrom a trained model and to further train the trained model to form a further trained model using the further measured values, and a second interface for outputting the trained model and the further trained model.

23. A computer program product embodied on a computer-readable storage medium and comprising computer commands which, when loaded into a memory of a training system for training a model for an electric machine controlled by a control device and executed by a processor of the training system, cause the training system to execute a method as set forth in claim 13.

24. A method for simulating operation of a first electric machine and a second electric machine, wherein the operation of the first electric machine is controlled by a first control device and the operation of the second electric machine is controlled by a second control device, wherein a first trained model and a second trained model is trained in each case for the first electric machine and for the second electric machine by receiving a temporal series of measured values that describe an operating variable of the first electric machine via a first interface of a training system, training, based on the received measured values, with a computing facility of the training system an untrained model embodied as an artificial neural network to produce a first trained model which simulates the operation of the first electric machine, and determining with the first trained model derived control variables that describe the control device, receiving via the first interface of the training system a temporal series of measured values of a second electric machine that is different from the first electric machine and controlled by a second control device, further training the first trained model with the computing facility using measured values of the second electric machine to produce a second trained model which simulates the operation of the second electric machine, and outputting the first trained model and the second trained model via a second interface of the training system.

25. The method of claim 24, further comprising emulating operation of the first or second control device by using the first trained model and the second trained model, respectively.

26. A simulation system for simulating operation of an electric machine, in particular a machine tool, comprising: a control device controlling operation of the electric machine, wherein the simulation system is configured to carry out the method as claimed in claim 23.

27. A computer program product embodied on a computer-readable storage medium and comprising computer commands which, when loaded into a memory of a simulation system for simulating operation of an electric machine controlled by a control device and executed by a processor of the simulation system, cause the simulation system to execute the method as set forth in claim 23.

Description

[0035] The invention is now explained in more detail on the basis of preferred exemplary embodiments and with reference to the appended drawings, in which:

[0036] FIG. 1 shows a schematic flow chart of a method for providing a model for a machine, wherein operation of the machine is controlled by means of a control device; and

[0037] FIG. 2 shows a schematic flow chart of a method for determining a model for a machine according to a further embodiment.

[0038] In the figures, the same or functionally identical elements are provided with the same reference characters.

[0039] FIG. 1 shows a schematic flow chart of a method for providing a model M.sub.A for a machine A. Machine A can be a machine tool. In particular, machine A is a CNC machine. In this regard operation of the machine A is controlled by means of a control device 2. This control device 2 can comprise a number of controllers or control circuits.

[0040] The model M.sub.A for the machine A can be determined by means of a training system 1. This training system 1 can be formed by a corresponding computer. The training system 1 here has a corresponding computing facility 6. An untrained model is fed to the training system 1 by way of an interface 3. The untrained model is an artificial neural network, in particular what is known as a deep neural network. A temporal series of measured values X.sub.A is fed to the training system 1 by way of an interface 4. These values X.sub.A describe operating variables of machine A. For instance, the measured values X.sub.A can describe a rotational speed, a direction of rotation, a torque, an electrical voltage, an electrical current or suchlike. Control of the machine A by means of the control device 2 is carried out on the basis of control variables θ.sub.A. These control variables θ.sub.A are not known, however.

[0041] In order to train the untrained model M.sub.U, control theory data can moreover be fed hereto. This control theory data describes in particular properties of known control devices 2. For instance, the control theory data can describe different transmission functions, which are used by control devices 2. While the model is being trained, the properties of the control device 2 or the control algorithm of the control device 2 can be approximated or determined with the aid of the temporal course of the measured values X.sub.A or the operating variables and the known control theory data.

[0042] The trained model M.sub.A can then be output by the training system 1 by way of an interface 5. Derived control variables θ′.sub.A can then be provided on the basis of the trained model M.sub.A. The derived control variables θ′.sub.A describe the properties of the control device 2, which have been determined during the training. The trained model M.sub.A therefore describes the operation of machine A, which is controlled by means of the control device 2.

[0043] FIG. 2 shows a schematic flow chart for determining a model M.sub.A,B according to a further embodiment. As explained previously in conjunction with FIG. 1, the untrained model M.sub.U is firstly also trained here on the basis of the measured values XA of machine A. The trained model M.sub.A can be provided after this training. In this case, what is known as transfer learning is additionally carried out. Here the trained model M.sub.A is fed to the training system 1 in a subsequent step.

[0044] Furthermore, measured values X.sub.B are determined by a further machine B and fed to the training system 1. Here operation of the further machine B is controlled by means of a further control device 2′. The machines A and B can originate from the same manufacturer, for instance, and be different or similar types of machines. The trained model M.sub.A can then be extended on the basis of the measured values X.sub.B of the further machine B and the extended trained model M.sub.A,B can be output by means of the training system 1. This extended trained model M.sub.A,B can in turn be used to determine derived control variables θ′.sub.A′, θ′.sub.B, which have been determined on the basis of the control devices 2, 2′ of the machines A, B.

[0045] The trained model M.sub.A or the extended trained model M.sub.A,B can be used to simulate the operation of the machine A, B. To this end, the model M.sub.A, M.sub.A,B can be run on a corresponding simulation system 7 or computer. This is shown schematically in FIG. 1. Therefore, operation of the machine A, B, which is controlled by the control device 2, 2′, can therefore be simulated or reproduced. It is therefore possible to obtain information relating to the typically unknown functionality of the complex control device 2, 2′. Overall this allows operation of the machine A, B to be optimized and errors to be avoided.