METHOD FOR DETERMINING THE SERVICE LIFE OF A SWITCHING DEVICE

Abstract

A method for determining the service life of a switching device, comprising the steps of: a) providing a neural network having at least two input variables and an output variable; b) determining at least a current variable, which represents a current flowing through the switching device, and a switching device state variable, which represents a sticking or jammed or fused switching device; c) inputting at least the current variable and the switching device state variable as input variables into the neural network; d) determining a remaining service life of the switching device by means of the neural network.

A method for training a neural network for determining the service life of a switching device, a corresponding device for determining the service life, a corresponding computer program, and a machine-readable storage medium with the computer program.

Claims

1. A method for determining the service life of a switching device (32), the method comprising the steps of: a) providing a neural network having at least two input variables and an output variable; b) determining at least a current variable, which represents a current flowing through the switching device (32), and a switching device state variable, which represents a sticking or jammed or fused switching device (32); c) inputting at least the current variable and the switching device state variable as input variables into the neural network; and d) determining a remaining service life of the switching device (32) by means of the neural network.

2. The method according to claim 1, wherein the current variable is a continuous variable, and the switching device state variable is a discrete variable.

3. The method according to claim 1, wherein the neural network is trained by means of monitored learning.

4. The method according to claim 1, wherein at least the method steps i) and iii) are carried out in a cloud-based device.

5. A method for training a neural network for determining the service life of a switching device (32), the method comprising the steps of: i) providing data sets, comprising at least a current variable, which represents a current flowing through the switching device, and a switching device state variable, which represents a sticking or jammed or fused switching device (32), and an associated service life variable, which represents a service life of the switching device (32); ii) providing a neural network having at least two input variables and an output variable; iii) inputting at least the current variable and the switching device state variable as input variables into the neural network; iv) comparing the output variable of the neural network with the corresponding service life variable; and v) adjusting parameters of the neural network as a function of the comparison.

6. A device for determining the service life of a switching device (32), comprising an electronic control unit (31) configured to a) provide a neural network having at least two input variables and an output variable; b) determine at least a current variable, which represents a current flowing through the switching device (32), and a switching device state variable, which represents a sticking or jammed or fused switching device (32); c) input at least the current variable and the switching device state variable as input variables into the neural network; and d) determine a remaining service life of the switching device (32) by means of the neural network.

7. A computer-readable storage medium containing instructions that when executed by a c computer cause the computer to a) provide a neural network having at least two input variables and an output variable; b) determine at least a current variable, which represents a current flowing through the switching device (32), and a switching device state variable, which represents a sticking or jammed or fused switching device (32); c) input at least the current variable and the switching device state variable as input variables into the neural network; and d) determine a remaining service life of the switching device (32) by means of the neural network.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Advantageous embodiments of the invention are shown in the figures and are explained in more detail in the subsequent description.

[0024] Shown are:

[0025] FIG. 1 a flowchart of a method according to the invention for determining service life according to one embodiment;

[0026] FIG. 2 a flowchart of a method according to the invention for training a neural network according to one embodiment; and

[0027] FIG. 3 a schematic diagram of a device according to the invention for determining service life.

[0028] The same reference signs refer in all figures to the same device components or the same method steps.

DETAILED DESCRIPTION

[0029] FIG. 1 shows a flowchart of a method according to the invention for determining service life according to one embodiment.

[0030] In a first step S11, a neural network with at least two input variables and an output variable is provided. For this purpose, a recurrent or feedback neural network in particular is suitable, since it can easily handle sequential input variables of different lengths.

[0031] In a second step S12, at least a current variable is determined, wherein the current variable represents an electrical current flowing through the switching device. Furthermore, in the second step S12, a switching device state variable is determined, which represents a sticking or jammed or fused switching device.

[0032] In a third step S13, the current variable and the switching device variable are transferred as input variables to the neural network. Depending upon the quantity of available data, the corresponding input variables can grow in size over time. In order to reflect the development over time, if required, a corresponding time stamp can also be stored. If the corresponding variables are always determined at the same time interval, it may be possible to dispense with this.

[0033] In a fourth step S14, a remaining service life of the switching device is then determined by means of the neural network. A warning can thus be output, for example, if the remaining service life falls below a predefined limit value.

[0034] FIG. 2 shows a flowchart of a method according to the invention for training a neural network according to one embodiment.

[0035] In a first step S21, data sets are provided which comprise at least a current variable, a switching device state variable, and an associated service life variable of a switching device. In this case, the current variable represents an electrical current flowing through the switching device, the switching device state variable represents a state of the switching device as stuck, jammed, or fused, and the service life variable represents a remaining service life of the switching device, wherein the definition of the remaining service life can be determined differently depending upon the application.

[0036] In a second step S22, a neural network with at least two input variables and an output variable is provided. This typically still has a standard parameterization, which does not yet reflect the findings from the training data.

[0037] In a third step S23, at least the current variable and the switching device state variable are input as input variables into the neural network. Accordingly, the neural network supplies an output variable.

[0038] In a fourth step S24, the output variable of the neural network is compared with the corresponding service life variable of the data sets. Typically, the corresponding variables are not the same, and the neural network must be adapted to reflect the reality more accurately.

[0039] In a fifth step S25, parameters of the neural network are therefore adapted as a function of the above comparison. Thus, the remaining service life of the switching device can be determined precisely by means of the adapted neural network.

[0040] FIG. 3 shows a schematic diagram of a device 30 according to the invention for determining the service life of a switching device 32. The device 30 comprises an electronic computing unit 31, which is configured to carry out a method according to the invention. The current variable and the switching device state variable can be determined from a database, not shown here, which can also be included in the device 30. The corresponding data can be transferred, e.g., by means of a network connection, from the switching device 32 to this database, where they are accordingly available. For example, they can also be used for training a neural network.