METHOD FOR THE AUTOMATED DETERMINATION OF CHARACTERISTIC CURVES AND/OR CHARACTERISTIC MAPS

Abstract

The invention relates to a method for the automated determination of characteristic curves and/or characteristic maps of devices, which comprises the following method steps: acquisition of a measurement data set, execution of an iteration method with the iteration steps calculation of an iteration result from the measurement data set using a neural network, acquisition of a termination parameter, checking the termination parameter and terminating the iteration method if the termination parameter matches a termination criterion, as well as the optical visualization of the iteration result and the measurement data set and repeating the iteration steps.

Claims

1. Method for the automated determination of characteristic curves and/or characteristic maps (100) of devices, which comprises the following method steps: Acquisition of a measurement data set (1) Execution of an iteration method (2) with the iteration steps: Calculation of an iteration result (3) from the measurement data set using a neural network (30) Acquisition of a termination parameter (4) Checking the termination parameter (5) Termination of the iteration method (6) if the termination parameter matches a termination criterion Optical visualization (7) of the iteration result and the measurement data set Repeating the iteration steps (8)

2. Method for the automated determination of characteristic curves and/or characteristic maps (100) of devices according to claim 1, characterized in that the termination parameter is a measure of the deviation from measurement data set to iteration result and/or a user input.

3. Method for automated determination of characteristic curves and/or characteristic maps (100) of devices according to claim 1, characterized in that the termination criterion is a specification for the deviation of measurement data set to iteration result and/or an occurred user input.

4. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the optical visualization (7) is part of the iteration steps.

5. Method for the automated determination of characteristic curves and/or characteristic maps (100) of devices according to claim 4, characterized in that the optical visualization (7) is part of each iteration run (8).

6. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the optical visualization (7) takes place after the calculation of the iteration result (3).

7. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the optical visualization (7) is done in a 3D graphic or a 4D graphic.

8. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the iteration method (2) is performed for a subsection of the measurement data set.

9. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the iteration method (2) is performed for several subsections of the measurement data set.

10. Method for the automated determination of characteristic curves and/or characteristic maps (100) of devices according to claim 9, characterized in that the iteration method (2) accesses the same neural network (30) for each subsection.

11. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that a prognosis and/or prediction about the development of the output parameters of the device is made with the aid of the characteristic curve or the characteristic map (20).

12. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the characteristic curve or map (20) is assigned to a point in time.

13. Method for the automated determination of characteristic curves and/or characteristic maps (100) of devices according to claim 12, characterized in that the characteristic curve and/or the characteristic map (20) and the assigned point in time are stored.

14. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the device is monitored during operation with the aid of the characteristic curve and/or the characteristic map (20).

15. Method for automated determination of characteristic curves and/or maps (100) of devices according to claim 1, characterized in that the change of the characteristic curve and/or the characteristic map (20) of the device is monitored.

Description

[0034] In the following, an embodiment of the present invention is explained in more detail with reference to drawings. Showing:

[0035] FIG. 1: An embodiment example of the method for the automatic determination of characteristic curves and/or characteristic maps

[0036] FIG. 2 a: 4D visualization of the measurement data sets of a two-stage gas compressor, method according to the invention for the automatic determination of characteristic curves and/or characteristic maps after 19 iteration steps

[0037] FIG. 2 b: 4D visualization of the measurement data sets of a two-stage gas compressor, method according to the invention for the automatic determination of characteristic curves and/or characteristic maps after 693 iteration steps

[0038] FIG. 2 c: 4D visualization of the measurement data sets of a two-stage gas compressor, method according to the invention for the automatic determination of characteristic curves and/or characteristic maps after 2097 iteration steps

[0039] FIG. 2 d: 4D visualization of the measurement data sets of a two-stage gas compressor, method according to the invention for the automatic determination of characteristic curves and/or characteristic maps after 3729 iteration steps

[0040] FIG. 2 e: 4D visualization of the measurement data sets of a two-stage gas compressor, method according to the invention for the automatic determination of characteristic curves and/or characteristic maps after 6641 iteration steps

[0041] FIG. 1 shows of an exemplary embodiment of a method according to the invention. The method 100 according to the invention has several method steps: In the first method step, a measurement data set is acquired 1. For this purpose, a sensor system is installed on the device to be monitored. One or more measured values of the sensor system that are acquired at a specific point in time form a measurement data set 1. The sensor system transmits the measurement data set to an evaluation unit. To calculate the characteristic curve or characteristic surface 20, an iteration method is performed and an iteration result is calculated 3. The iteration method 3 itself has four method steps for this purpose: In the first step 3 of the iteration method, an iteration result is calculated based on the acquired measurement data set 1. According to the invention, a neural network 30 is advantageously used for the calculation.

[0042] It has been found that the calculation by a neural network 30 provides useful results much more frequently than a calculation of the characteristic curve or characteristic surface 20 by means of previously known methods. In particular, if the neural network 30 has been taught, e.g., in that characteristic curves or maps 20 of a plurality of similar devices have already been determined by means of the neural network 30. Another advantage is that even if the characteristic curve or map 20 is unknown, it can be evaluated quickly and with sufficient accuracy, e.g. if a characteristic curve or map 20 is being determined for a device for the first time.

[0043] In the second method step for calculating the iteration result 3, a termination parameter is acquired 4. According to the invention, the termination parameter can be set in two ways, namely by the MSE of the characteristic curve or map 20 or manually by a user.

[0044] In the next method step of the method according to the invention for the automated determination of characteristic curves and/or maps 20, an optical visualization 7 of the iteration result takes place together with the measurement data set.

[0045] After that, the termination parameter is checked 5. The calculation of the iteration result 3 is terminated 6 if the termination parameter matches an termination criterion. A termination criterion can be e.g. a low MSE of the characteristic curve or the characteristic map 20, or an evaluation of the characteristic curve or the characteristic map 20 by a user.

[0046] In the last method step of the method according to the invention, the iteration steps of the iteration method 3 are repeated 8 if the termination parameter does not match a termination criterion, i.e. if the MSE of the determined characteristic curve or map 20 is too large and/or a user considers the quality of the characteristic curve or map 20 to be insufficient by optical validation.

[0047] Thus, if the MSE of the determined characteristic curve or map 20 is small enough (e.g., less than 0.005) or does not show significant changes after a plurality of iteration methods 3 executed in succession, the iteration method 3 is terminated. But even with low MSE, the determined characteristic curve or map 20 can be incorrect. By displaying the characteristic curve or the characteristic map 20 in a coordinate system together with the measurement data records, the user receives a visualization of the characteristic curve or the characteristic map 20 at each iteration step of the iteration method 3 and can thus visually check the quality of the characteristic curve or the characteristic map 20. By means of this advantageous optical visualization 7, a user can thus very quickly, reliably and intuitively recognize both the quality of the characteristic curve or map 20 and the learning success of the neural network 30.

[0048] An embodiment of the method 100 according to the invention is shown in FIGS. 2 a) to 2 e). As an example, an underground gas storage facility is used as the device to be monitored. These storage facilities serve to balance out imbalances between supply or production and demand or consumption and thus to increase security of supply. Since the gas in the underground storage facility usually has a higher pressure than the long-distance gas pipeline, the gas is compressed for injection by means of one or—as in this exemplary embodiment—by means of two gas compressors.

[0049] A 3D representation was selected for visualization, in which a color and/or brightness coding 13 was assigned to the measuring points 10 displayed in the 3D representation to represent the 4th dimension. The surfaces 11.1, 11.2, 11.3 between the coordinate axes are shown as projection surfaces, on each of which the measuring points 10 are projected as projected points 12.1, 12.2, 12.3. Furthermore, the determined map 20 is drawn.

[0050] The method 100 according to the invention has several method steps: In the first method step, a measurement data set is acquired 1. For this purpose, a sensor system is installed on the device to be monitored. One or more measured values of the sensor system that are acquired at a specific point in time form a measurement data set 1. The sensor system transmits the measurement data set to an evaluation unit. To calculate the characteristic curve or characteristic surface 20, an iteration method is performed and an iteration result is calculated 3. The iteration method 3 itself has four method steps for this purpose: In the first step 3 of the iteration method, an iteration result is calculated based on the acquired measurement data set 1. According to the invention, a neural network 30 is advantageously used for the calculation.

[0051] In the second method step for calculating the iteration result 3, a termination parameter is acquired 4. According to the invention, the termination parameter can be set in two ways, namely by the MSE of the characteristic curve or map 20 or manually by a user.

[0052] In the next method step of the method according to the invention for the automated determination of characteristic curves and/or maps 20, an optical visualization 7 of the iteration result takes place together with the measurement data set.

[0053] After that, the termination parameter is checked 5. The calculation of the iteration result 3 is terminated 6 if the termination parameter matches an termination criterion. A termination criterion can be e.g. a low MSE of the characteristic curve or the characteristic map 20, or an evaluation of the characteristic curve or the characteristic map 20 by a user.

[0054] In the last method step of the method according to the invention, the iteration steps of the iteration method 3 are repeated 8 if the termination parameter does not match a termination criterion, i.e. if the MSE of the determined characteristic curve or map 20 is too large and/or a user considers the quality of the characteristic curve or map 20 to be insufficient by optical validation.

[0055] In this embodiment example, the sensor system provides measurement data sets on the volume flow of the gas fed into the storage facility (parameter 1, x-axis) and on the compression ratio of the natural gas in the storage facility (parameter 2, y-axis). These two parameters are entered into the neural network 30 in the input layer. The parameter efficiency (parameter 3, z-axis), which is particularly relevant for a user of the system, as well as the speed of the two gas compressors (parameter 4, gray scale) are the output and are output via the output layer of the neural network 30. Accordingly, the neural network 30 has 2 input neurons as well as 2 output neurons. The neural network 30 was trained using training data from various subsurface gas storage facilities. In the present example, two input neurons and two output neurons are used in each case. Between the input and output neurons there are another 5 hidden layers with 100 neurons each.

[0056] FIG. 2 shows the 4-dimensional optical visualization 7 of the measurement data sets together with the characteristic map 20 determined from them. The optical visualization 7 can be stored at different times in each case and thus assigned to the point in time. In this embodiment example, the optical visualization 7 as part of the iteration steps of the iteration method 2 is performed after each calculation of the iteration result 3. This illustrates the quality of the determined characteristic map 20 after the specified number of iteration steps (FIG. 2 a-e) and can be visually validated by a user. The MSE of the determined characteristic map 20 is also given.

[0057] After 19 iteration steps (FIG. 2 a), the characteristic map 20 still exhibits a very large MSE of 0.094. The characteristic map 20 still essentially has the shape of an inclined plane. It is visually apparent to a user alone that the calculated characteristic map 20 does not at this point in time approximately reflect the behavior of the gas injection into the underground gas storage facility.

[0058] After 693 iteration steps (FIG. 2 b), the MSE of the characteristic map 20 is still 0.007. The formation of two relative maxima can already be seen. The two relative maxima correspond to a serial and a parallel operation of the two gas compressors. Here, becomes apparent the advantage of the method 100 according to the invention of optically visualizing 7 the characteristic map 20. Despite the low MSE of the characteristic map 20 of 0.007, it is visually apparent that the behavior of the subsurface gas storage facility cannot yet be predicted correctly when compared with FIG. 2 c, in which characteristic map 20 has a larger error of 0.013.

[0059] After 2097 iteration steps (FIG. 2 c), the MSE of the characteristic map 20 has increased to 0.013, yet both relative maxima are much more pronounced visually. After 3729 iteration steps (FIG. 2 d), the MSE of the characteristic map 20 is still 0.0034, the significantly improved characteristic map 20 already shows the two relative maxima with a visually recognizable difference in efficiency. After 6641 iteration steps (FIG. 2 e), the MSE of the characteristic map is 0.0045, slightly increased compared to 3729 iteration steps (FIG. 2 d). However, the two relative maxima still show clearly visually distinguishable efficiencies: The maximum of the efficiency for the parallel mode is significantly lower than the maximum of the efficiency for a serial mode of the gas compressors. This also corresponds to the empirical values.

[0060] The characteristic maps 20 of both driving modes were not calculated separately in this embodiment example. Nevertheless, the method 100 according to the invention provides a well-validated characteristic map 20. The method 100 according to the invention also allows the characteristic map 20 to be determined even from one or more subsections of the measurement data records, by simply using only the measurement data records that are of interest to the user to determine the characteristic map and hiding the measurement data records that are not of interest. The method 100 according to the invention is very efficient; the characteristic map 20 shown in FIG. 2 e required a computing time of only 2 minutes.

LIST OF REFERENCE SIGNS

[0061] 100 Method for the automatic determination of characteristic curves and/or characteristic maps [0062] 1 Acquisition of a measurement data set [0063] 2 Execution of the iteration method [0064] 3 Calculation of an iteration result [0065] 4 Acquisition of the termination parameter [0066] 5 Checking the termination parameter [0067] 6 Termination of the iteration method [0068] 7 Optical visualization [0069] 8 Repetition of the iteration method [0070] 10 Measuring point of a measurement dataset [0071] 11.1, 11.2, 11.3 Surfaces between the coordinate axes [0072] 12.1, 12.2, 12.3 Projected measuring points [0073] 13 Color/brightness coding [0074] 20 Characteristic map [0075] 30 Neural network