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 system behavior of a technical device using a monitoring algorithm which is supplied with input data and output data of the technical device in a learning phase. In a subsequent prediction phase, the monitoring algorithm is only supplied with the input data, and output data are calculated. In a preprocessing phase, the input data supplied to the monitoring algorithm are aligned with data of a reference signal.

Claims

1. A method for determining an inadmissible deviation of a system behavior of a technical device from a standard value range using a monitoring algorithm comprising: in a learning phase, supplying the monitoring algorithm with input data and output data of the technical device; in a prediction phase, which follows the learning phase, supplying the monitoring algorithm only with the input data of the technical device; computing, in the monitoring algorithm, output comparison data; ascertaining the inadmissible deviation of the technical device when, based on a difference from the output comparison data, the output data of the technical device lies outside the standard value range; and in a preprocessing step, normalizing the input data supplied to the monitoring algorithm to data of a reference signal.

2. The method as claimed in claim 1, wherein in the preprocessing step, a number of items of the input data supplied to the monitoring algorithm is harmonized with a number of items of the data of the reference signal.

3. The method as claimed in claim 1 wherein in the preprocessing step, when a number of items of the input data and a number of items of the data of the reference signal are equal, but the input data is skewed with respect to the data of the reference signal, then the input data is mapped onto the data of the reference signal.

4. The method as claimed in claim 1, wherein: in the preprocessing step, the normalization of the input data supplied to the monitoring algorithm, which input data is in time-discrete form, takes place in three sub-steps, in a first sub-step, time-normalization of the input data in a viewed time window onto the reference signal is performed, in a second sub-step, the input data for time segments of the time window is transformed into a frequency domain, and in a third sub-step, frequency segments of the input data, which frequency segments are associated with different time segments, are combined according to the time-normalization of the first sub-step.

5. The method as claimed in claim 4, wherein the time-normalization of the input data onto the reference signal, which is performed in the first sub-step, is carried out using dynamic time warping.

6. The method as claimed in claim 4 wherein the transformation of the input data for the viewed time window into the frequency domain, which is performed in the second sub-step, is carried out using a short-time Fourier transform.

7. The method as claimed in claim 4, wherein the output data of the technical device is transformed into the frequency domain and compared in the frequency domain with the output comparison data computed in the monitoring algorithm.

8. The method as claimed in claim 4, wherein the output comparison data computed in the monitoring algorithm is transformed into a time domain and compared in the time domain with the output data of the technical device.

9. The method as claimed in claim 1, wherein the reference signal is formed from a plurality of preceding items of the input data.

10. The method as claimed in claim 1, wherein the reference signal corresponds to a defined driving maneuver of a vehicle.

11. The method as claimed in claim 1, wherein the monitoring algorithm is embodied as a neural network.

12. The method as claimed in claim 1, wherein a control unit in a vehicle is configured to perform the method.

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

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

Description

[0022] Further advantages and expedient embodiments can be found in the further claims, the description of the figures, and the drawings, in which:

[0023] FIG. 1 is a block diagram containing a symbolic depiction of an ESP module which is supplied with input data, produces output data and is connected in parallel with a neural network;

[0024] FIG. 2 shows graphs of the variation over time of an input signal and a reference signal;

[0025] FIG. 3 is a diagram of the input signal transformed into the frequency domain in matrix form;

[0026] FIG. 4 shows the input signal transformed into the frequency domain including time-normalization according to FIG. 2.

[0027] The block diagram of 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 having input data and output data and having a parallel-connected neural network 4. The ESP module 1 used by way of example as the technical device comprises an ESP pump for producing a desired modulated braking pressure in the braking system, and a control unit for controlling the ESP pump. Input data 2, for instance an input current for the electrically operable ESP pump of the ESP module 1, is supplied to the ESP module 1, which ESP module 1 produces output data 3, for instance a hydraulic braking pressure, in response to the input data 2.

[0028] Connected in parallel with the technical device 1 is a neural network 4, which forms a monitoring algorithm. The neural network 4 is trained in a learning phase to the system behavior of the technical device 1, for which purpose the neural network 4 is supplied in the learning phase with both the input data 2 and the output data 3 of the technical device 1. In FIG. 1, the dashed arrow from the output data 3 to the neural network 4 corresponds to the learning phase of the neural network, in which phase the neural network is also supplied with the output data 3 in addition to the input data 2.

[0029] After completion of the learning phase, the neural network 4 can be used in a prediction phase in order to ascertain in good time a deterioration in the system behavior of the technical device 1. For this purpose, in the prediction phase, the input data 2 of the technical device 1 is supplied as the input to the neural network 4, and the neural network 4 then produces output comparison data on the basis of its trained behavior (output from the neural network 4 represented by a continuous line). The output comparison data from the neural network 4 can be compared with the output data 3 of the technical device 1. If the difference between the output comparison data of the neural network 4 and the output data 3 of the technical device 1 lies outside a defined standard value range then there exists an inadmissibly large deterioration in the system behavior of the technical device 1, from which can be inferred a shortened service life or partial failure of the technical device 1. In response, measures can be taken such as, for instance, producing a warning signal or reducing the range of functions of the technical device 1.

[0030] The neural network 4 can be implemented and run in the control unit of the technical device 1. It is also possible, however, to have the neural network 4 running in a further control unit that is embodied separately from the control unit of the technical device 1.

[0031] FIGS. 2 to 4 show a preprocessing step, which is performed before each learning-phase step and before each prediction-phase step, and in which the input data supplied to the monitoring algorithm is normalized to the data of a reference signal.

[0032] FIG. 2 shows two graphs, one above the other, containing the time-dependent variation of a reference signal R (bottom graph) and of a signal containing measured input data M (top graph). The input data M corresponds to the input data 2 in FIG. 1. The reference signal R has a series of time points a, b, c, d and e. The signal containing the input data M comprises a series of time points 1 to 6 at which the values of the input data are measured. The reference signal R can be obtained, for example, from a multiplicity of preceding items of real input data of the technical device or of another technical device of identical design.

[0033] Although the signal curves R and M exhibit the same fundamental curve, they are not identical. In order to normalize the measured signal of the input data M, which contains the measured time points 1 to 6 numbering six in total, to the reference signal R, which contains a total of five time points a to e, dynamic time warping is performed in a first sub-step. This involves taking into consideration optimization aspects to find the most cost-effective path from the start to the end of the two signal curves R and M. This results in the association, represented by the dashed line, between the time points in the signal curves R and M having the association patterns 1a, 2b, 3c, 4c, 5d and 6e. The measured values in the signal curve M at the time points 3 and 4 are both associated with the time point c in the reference signal R.

[0034] FIG. 3 shows a schematic diagram of the input data M in the frequency domain. Here, in a second sub-step, the input data M is transformed into the frequency domain by means of a short-time Fourier transform STFT, which is achieved by performing a fast Fourier transform at each time point t=1 to t=6. This procedure has the advantage that the time information is retained even when transforming into the frequency domain. In the matrix shown in FIG. 3, each of the columns represents a vector transformed into the frequency domain and associated with one of the time points t=1 to 6.

[0035] FIG. 4 shows the third and last sub-step of the preprocessing of the input data, in which sub-step the matrix of the input data M from FIG. 3 is combined in accordance with the time-normalization in the first sub-step shown in FIG. 2. As a result, also in the frequency domain, as is shown in FIG. 4, the frequency segments that are associated with the time points 3 and 4 are combined to form a shared frequency segment. This results in a reduction in the frequency segments from six to five. The frequency segments 3 and 4 are combined, for example, by averaging the information in the respective vectors associated with the time points 3 and 4.

[0036] After completion of the preprocessing, the normalized input data M in the frequency domain can be supplied in the prediction phase to the monitoring algorithm implemented as the neural network, whereupon the neural network determines output comparison data in the frequency domain, which can be compared with associated output data of the technical device in the frequency domain. In the event of an inadmissible deviation indicating a deterioration in the system behavior of the technical device, an alarm signal can be produced, for example.

[0037] As an alternative to this procedure, it is also possible to transform from the frequency domain to the time domain the output comparison data computed in the neural network, and to compare this data with the output data of the technical device in the time domain. Again in this case, in the event of an inadmissibly high deviation indicating a deterioration in the system behavior, it is possible to produce an alarm signal or to take other measures, for instance degrading the functionality of the technical device or activating alternative technical devices.