Method for Determining an Inadmissible Deviation of the System Behavior of a Technical Device from a Standard Value Range

Abstract

A method determines an inadmissible deviation of a technical device using an artificial neural network which is supplied with input data and output data of the technical device in a learning phase. In a subsequent prediction phase, the neural network is only supplied with the input data, and comparative output data are calculated in the neural network and are compared to the output data of the technical device.

Claims

1. A method for determining an impermissible deviation of a system behavior of a technical device from a normal value range using an artificial neural network comprising: supplying the neural network with input data and output data of the technical device in a learning phase; in a prediction phase following the learning phase (i) feeding only the input data of the technical device to the neural network, and (ii) calculating output reference data in the neural network; and identifying the impermissible deviation when the output data of the technical device is outside the normal value range based on a difference with respect to the output reference data calculated by the neural network.

2. The method as claimed in claim 1, wherein: the neural network is divided into a base network and a head network, in a first section of the learning phase both the base network and the head network are trained on a first technical device, and in a second section of the learning phase only the head network is trained on a second technical device which is identical to the first technical device.

3. The method as claimed in claim 2, wherein in the prediction phase both the base network and the head network are used to determine an inadmissible deviation of the second technical device.

4. The method as claimed in claim 2, wherein a number of neurons of the head network is smaller than a number of neurons of the base network by at least a factor of five or ten.

5. The method as claimed in claim 2, wherein an output of the base network is used as an input for the head network.

6. The method as claimed in claim 2, wherein measured values of the second technical device are fed to the head network as an input.

7. The method as claimed in claim 2, wherein information about a type or a class of the input data is fed to the head network as an input.

8. The method as claimed in claim 2, wherein the neural network comprises a plurality of the base networks to which different input data are fed.

9. The method as claimed in claim 8, wherein an output of each of the base networks is fed to a common head network.

10. The method as claimed in claim 2, wherein the input data is subjected to pre-processing before the calculating takes place in the neural network.

11. The method as claimed in claim 1, wherein a control unit in a vehicle is configured to carry out the method.

12. The method as claimed in claim 1, wherein a computer program product includes program code configured to carry out the method.

13. The method as claimed in claim 12, wherein a non-transitory machine-readable storage medium is configured to store the computer program product.

Description

[0025] Further advantages and expedient designs can be derived from the other claims, the description of the figures and the drawings. In the drawings:

[0026] FIG. 1 shows a block diagram with a symbolic illustration of a first ESP module to which input data is fed and which produces output data, with a parallel connected neural network consisting of a base network and a head network, which is in the first section of a learning phase,

[0027] FIG. 2 shows the block diagram according to FIG. 1, but with a second ESP module and the neural network in a second section of the learning phase,

[0028] FIG. 3 shows the block diagram according to FIG. 2 with the second ESP module and the neural network in a prediction phase,

[0029] FIG. 4 shows the base network and the head network of the neural network in a detailed representation.

[0030] In the figures, equivalent components are labelled with the same reference signs.

[0031] The block diagram according to FIG. 1 shows a schematic diagram of a technical device 1 in the form of an ESP module for a braking system in a vehicle with input and output data and with a parallel connected neural network 4. The ESP module 1, which by way of example is used as the technical device, comprises an ESP pump for generating a desired, modulated brake pressure in the braking system, and a control unit for controlling the ESP pump. The ESP module 1 is supplied with input data 2, for example an input current for the electrically operated ESP pump of the ESP module 1, wherein in response to the input data 2 the ESP module 1 produces output data 3, for example a hydraulic brake pressure.

[0032] Connected in parallel with the technical device 1 is a neural network 4, which is trained in a learning phase to the system behavior of the technical device 1, for which the input data 2 and the output data 3 of the technical device 1 are fed to the neural network 4 during the learning phase.

[0033] The neural network 4 is divided into a base network and a head network, which each have a plurality of layers and interact. The output of the base network 6 represents the input of the head network 7. The base network 6 is significantly larger than the head network 7; the number of neurons of the base network is preferably at least a factor of five or at least a factor of ten greater than the number of neurons of the head network 7.

[0034] FIG. 1 shows a first section of the learning phase, in which both the base network 6 and the head network 7 are trained on the system behavior of the technical device 1. For this purpose, both the input data 2 and the output data of the technical device 1 are fed to the base network 6 as input and connections are created in the base network 6 and the head network 7.

[0035] The first section of the learning phase according to FIG. 1 can be carried out during a development phase of the technical device 1. After the completion of the first section of the learning phase, the training for the base network 6 is terminated.

[0036] FIG. 2 shows a second section of the learning phase for the neural network 4, wherein this second section of the learning phase is carried out on a second technical device 5, which is technically identical to the first technical device 1. This second section of the learning phase relates only to the head network 7 of the neural network 4, while the base network 6 is no longer trained in the second section of the learning phase. Due to the lesser computational effort involved, this design enables the second section of the learning phase to be carried out on a correspondingly less powerful control unit, in particular directly at the installation site of the second technical device 5. In the case of an ESP module 5, the second section of the learning phase can be carried out in the control unit of the ESP module.

[0037] Also in the second section of the learning phase, the input data 2 and the output data 3 of the second technical device 5 are fed to the neural network 4 as input, but exclusively to the head network 7 of the neural network.

[0038] FIG. 3 shows the second technical device 5 in a prediction phase of the neural network 4. The learning phase has been completed, the head network 7 has been sufficiently trained for the specific application with the second technical device 5. In the prediction phase according to FIG. 3, the neural network 4 is supplied with the input data 2 of the second technical device 5 as input, wherein in the neural network with the base network 6 and the head network 7, on the basis of the learned behavior, output reference data are generated which are compared with the output data 3 of the second technical device 5. If the deviation is so great that the output data 3 of the technical device 5 is outside a given standard value range, an impermissibly large deterioration of the system behavior of the technical device 5 exists, from which a shortened service life or an imminent partial failure or complete failure of the technical device 5 can be concluded. Measures can then be taken, such as the generation of a warning signal, or a reduction in the functional scope of the technical device 5.

[0039] FIG. 4 shows the detailed structure of the neural network with the base network 6 and the head network 7. The base network 6 comprises a plurality of individual base networks or sub-base networks 6a, 6b and 6c, to which measured values in the time or frequency domain of a highly dynamic measuring channel are fed as input data. In the learning phase, these are input and output data of the technical device and in the prediction phase, input data of the technical device.

[0040] On the output side, the generated data in the sub-base networks 6a, 6b and 6c are fed to the head network 7 as input, in which further connections are created in the learning phase and a prediction is made about the system behavior of the technical device under consideration in the prediction phase. In the second section of the learning phase, the output data of the second technical device can be fed directly to the head network 7 as input, as can also be seen in FIG. 2.

[0041] In the prediction phase, supplementary information can be fed to the head network 7 as additional input, for example information about the type or class of the input data, or static measured values or mean values of low-dynamic measured values.