METHOD FOR DETERMINING A PROCESS VARIABLE WITH A CLASSIFIER FOR SELECTING A MODEL FOR DETERMINING THE PROCESS VARIABLE

Abstract

The present disclosure relates to a method for determining at least one process variable of a medium, including steps of recording a sensor signal from a field device and determining a selected model from a set of at least two different models by means of a classifier. Each of the models is used for determining the process variable based at least on the sensor signal. The classifier is designed to select the selected model. The method also includes a step of determining the process variable based at least on the selected model and the sensor signal.

Claims

1. A method for determining at least one process variable of a medium, including the following method steps: recording a sensor signal from a field device; determining a selected model from a set of at least two different models using a classifier; wherein each of the models is used for determining the process variable at least on the basis of the sensor signal; and wherein the classifier is designed to select the selected model; and determining the process variable at least on the basis of the selected model and the sensor signal.

2. The method of claim 1, wherein the classifier is designed to learn the selection of the selected model.

3. The method of claim 2, wherein the classifier is trained offline or online.

4. The method of claim 1, wherein the classifier is designed to use at least one influencing variable in the selection of the selected model.

5. The method of claim 4, wherein the influencing variable is the sensor signal or a variable derived from the sensor signal.

6. The method of claim 1, wherein, based on a data record comprising at least one input variable and an output variable associated with the input variable, a mapping is created, wherein the classifier determines the selected model based on the mapping.

7. The method of claim 1, wherein a feature vector is determined, wherein the classifier is designed to select the selected model based on the feature vector.

8. The method of claim 7, wherein a first and a second classifier are used, wherein the first classifier performs a feature extraction or creates a feature vector, wherein the second classifier selects the selected model based on the feature vector.

9. The method of claim 1, further including determining a classification quality with respect to the selection of the selected model.

10. The method of claim 9, further including evaluating the classification quality using a probability with which the classifier selected the selected model.

11. The method of claim 9, further including detecting a change of the classifier from a first to a second selected model.

12. The method of claim 11, further including determining an alternating frequency between the first and the second selected models or a time interval during which the first or the second selected model is used.

13. The method of claim 1, wherein the field device is a field device for determining or monitoring a turbidity, a flow rate, or a fill level of a medium, or for determining a concentration of at least one substance contained in the medium.

14. A computer program for determining at least one process variable of a medium with computer-readable program code which, when executed on a computer, cause the computer to execute the following steps: record a sensor signal from a field device; determine a selected model from a set of at least two different models using a classifier; wherein each of the models is used to determine the process variable based at least on the sensor signal; and wherein the classifier is designed to select the selected model; and determining the process variable at least on the basis of the selected model and the sensor signal.

15. A computer program product stored in a computer readable medium for determining at least one process variable of a medium, comprising: computer code for recording a sensor signal from a field device; computer code for determining a selected model from a set of at least two different models using a classifier; wherein each of the models is used for determining the process variable at least on the basis of the sensor signal; and wherein the classifier is designed to select the selected model; and computer code for determining the process variable at least on the basis of the selected model and the sensor signal.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present disclosure is explained in greater detail with reference to the following figures.

[0030] FIG. 1 (comprising FIGS. 1a and 1b) shows a flowchart for illustration of the method according to the present disclosure;

[0031] FIG. 2 (comprising FIGS. 2a, 2b and 2c) illustrates a possible application in conjunction with the determination of a fill level according to the reflectometric measuring principle;

[0032] FIG. 3 (comprising FIGS. 3a, 3b and 3c) illustrates a possible application in conjunction with the determination of a turbidity of a medium; and

[0033] FIG. 4 (comprising FIGS. 4a, 4b, 4c and 4d) illustrates a possible application in conjunction with the determination of an alcohol content.

DETAILED DESCRIPTION

[0034] In the Figures, identical elements are respectively provided with the same reference characters.

[0035] The method according to the present disclosure is schematically depicted in FIG. 1.

[0036] The method can, for example, be implemented in an electronic system of a field device 1, 4 (shown in later Figures) or in a higher-level unit. The electronic system comprises a memory unit, which is likewise not shown separately, in which different models M.sub.1-M.sub.n for determining the process variable y are stored on the basis of a sensor signal x received from a sensor unit (not shown) of the field device 1, 4.

[0037] Respectively received from the sensor unit is/are a sensor signal or a plurality of sensor signals x.sub.1-x.sub.i for which the process variable y.sub.1-y.sub.i is respectively to be determined. The various models M.sub.1-M.sub.n are thereby available for determining the process variable y. The classifier K according to the present disclosure then serves to determine and select a selected model (here M.sub.2) from the set of models M.sub.1-M.sub.n. This selection is illustrated in FIG. 1 by the arrows and the two switching elements S.sub.1 and S.sub.2. In the instance of FIG. 1a, the model M.sub.2, by means of which the process variable y.sub.2 is determined at least from the sensor signal x.sub.2, has been selected by the classifier K.

[0038] Optionally, one or more influencing variables can be made available to the classifier K, as indicated by the dashed arrows. In the present instance, these are e.g. the sensor signals x.sub.1-x.sub.i, as well as the further influencing variables x.sub.j and x.sub.k.

[0039] The different models M.sub.1-M.sub.n can respectively be used to determine the process variable y.sub.1-y.sub.i based on the sensor signals x.sub.1-x.sub.i. The models M.sub.1-M.sub.n may, for example, relate to different measurement principles or different configurations in the process, for example different fields of use and/or application. For example, the different models M.sub.1-M.sub.n may also be at least partially mutually exclusive, so that certain models are not applicable to certain circumstances. In the simplest instance, the respectively selected model M.sub.2 remains the same for a predeterminable duration of a specific process. However, it is also conceivable that, during continuous operation, process and/or ambient conditions change in such a way that a change of the selected model M.sub.2 by the classifier K is to be performed continuously, periodically, or selectively. For example, in the instance of FIG. 1b the model M.sub.n, by means of which the process variable y.sub.i is determined at least from the sensor signal x.sub.i, has been selected by the classifier K.

[0040] A possible application of the method according to the present disclosure with regard to the contactless determination of a fill level F of a medium M as process variable y is illustrated by the run-time-based fill level measurement method known per se from the prior art, as illustrated in FIG. 2. Corresponding field devices are produced by the applicant in many different embodiments and are sold under the designations Micropilot, Levelflex, or Prosonic, for example.

[0041] The measuring principle is illustrated schematically in FIG. 2a. A transmission signal S is reflected against a surface O of a medium M located in a container 2, and the received echo signal R is then evaluated with respect to the fill level F of the medium M. The signal evaluation is shown in FIG. 2b. Since, as a rule, different spurious echo signals are superimposed on the fill level-dependent echo signal, the received echo signal R first needs to be appropriately processed further. In order to be able to respectively extract the fill level-dependent echo signal from the reflected echo signal R, a signal transformation 3a is therefore often implemented in frequency space, for example a Fast Fourier Transformation (FFT). What are known as envelopes are subsequently evaluated by means of respective algorithms A provided for this purpose, on the basis of which the fill level F can be determined. (An example of an envelope is shown in FIG. 2c, including amplitude A over time t, and distance d.)

[0042] In order to allow an optimally precise determination of the fill level F, the respectively used algorithms A must be appropriately parameterized 3b for the respective process or the respective application. This parameterization 3b, or the selection and specification of the parameters, often takes place manually according to the prior art. For example, a maximum filling speed and/or emptying speed of the container 2 are specified for the precise tracking of the fill level-dependent echo signal. For precise determination of the fill level F, various data are in turn required regarding the medium M, including the the dielectric constant, and for the surface behavior of the medium M within the container, for example information regarding turbulence or foam formation in the area of the surface O. The parameterization 3b is accordingly highly application-specific and must be selected appropriately for each new application. This is associated with a high cost.

[0043] In relation to the present disclosure, the different envelopes, algorithms A, or even different parameter sets serve as different models M.sub.1-M.sub.n. The classifier K serves for the intelligent selection of the matching model for determining the process variable y=F on the basis of the sensor signals x, which in this instance are provided by the echo signals R. In this respect, it is conceivable that the classifier K selects at least one parameterization 3b for a parameter from a plurality of parameter values based on one or more envelope(s).

[0044] Another example of an application of the method according to the present disclosure relates to a turbidity sensor 4, likewise known from the prior art, for determining a turbidity of a medium M, as illustrated in FIG. 3. Such sensors 4 can additionally be used for determining the substance concentration of an undissolved solid CF, for example for determining the substance concentration of various sludges, such as in sewage treatment plants. For example, what are known as thin slurries, activated sludges, surplus activated sludges, or also digested sludges are known in this context. For each type of sludge, a separate suitable model is provided by means of which the substance concentration of the solid y=CF can be determined on the basis of a sensor signal x of the turbidity sensor 4.

[0045] Turbidity sensors are also produced by the applicant in various embodiments and are sold under the name Turbimax, for example. A turbidity sensor 4 based on the measurement principle of scattered light measurement is shown in FIG. 3a. Starting from the light source 5, transmitted light 6 (solid line) is radiated across a window 7 that is transparent to the transmitted light 6 into a measuring chamber 8 containing the medium M. There, the transmitted light 6 is scattered at a scattering point P at a measurement angle , or is converted into received light 9 (dashed line). The received light 9 in turn passes across a window 10, which is transparent to the received light 9, via a diaphragm 11, to a detector 12 and is a measure of the turbidity of the medium M.

[0046] In the instance of the quadruple-beam alternating light method, as illustrated in FIGS. 3b and 3c the sensor 4 has two light sources 5a, 5b and four detectors 12a-12d for redundant detection of the received light 9 or scattered light. Two of the detectors 12c and 12d may serve for detecting 90 scattered light; the other two 12a and 12b may serve for detecting 135 scattered light. FIG. 3b thereby shows a schematic front view of the sensor 4, and FIG. 3c shows a side view.

[0047] Before starting up a sensor 4 for determining the solid concentration CF of a sludge in a specific application, the appropriate model M.sub.1-M.sub.n must respectively be selected manually. In the event that the type of sludge changes over the course of time, the model M.sub.1-M.sub.n used for determining the substance concentration CF must correspondingly also be changed. If the necessity of a model change in continuous operation is not detected promptly, which often occurs, a faulty determination of the substance concentration of the sludge occurs at least intermittently.

[0048] By means of the present disclosure, a classifier K can now be used for determining a respective appropriate selected model M.sub.2, M.sub.n. The classifier K accordingly serves in principle for the intelligent recognition of the sludge type at least on the basis of the sensor signals x of the turbidity sensor 4, for example of the signals x received by means of the detector 12. Depending on the type of sludge, the classifier K selects the selected model M.sub.2, M.sub.n suitable for determining the concentration.

[0049] On the one hand, sensor signals x.sub.1-x.sub.i of the turbidity sensor 4 can serve as possible influencing variables. However, other influencing variables x.sub.j, x.sub.k can also additionally or alternatively be provided, for example those which reflect spectral characteristics of the medium M, for example an absorption, reflection, transmission, or a scattering at one or more different wavelengths.

[0050] Yet another possible application of the present disclosure relates to the measurement of the alcohol content C.sub.A in a medium M in the form of an aqueous solution, as illustrated in FIG. 4. The difficulty in determining the alcohol content is often that it is not known in advance what type of alcohol is involved in the respective instance, for example methanol, ethanol, or isopropanol (2-propanol). FIG. 4a-4c show characteristic curves for the different alcohols methanol (a), ethanol (b), and isopropanol (c), which indicate the density p as a function of the alcohol concentration C.sub.A. The course of the characteristic curves is distinctly different for the respective alcohols. Accordingly, the accuracy in the concentration determination depends on knowing the respective alcohol present in the aqueous solution.

[0051] For example, in order to determine which alcohol is respectively involved, the density p and the refractive index n.sub.D of the aqueous solution may be determined. Using these two variables, which alcohol is involved can be unambiguously determined, as can be seen from FIG. 4d. The refractive index n.sub.D as a function of the density p for a particular alcohol respectively shows a characteristic curve, and is independent of the concentration of the alcohol C.sub.A in the aqueous solution.

[0052] In relation to the present disclosure, the classifier K can, for example, be provided with the refractive index n.sub.D and the density p of the aqueous solution as influencing variables x.sub.j, x.sub.k. The classifier K is then designed to determine the respective alcohol present and to select a characteristic curve (the selected model M.sub.2, M.sub.n). The alcohol content C.sub.A of the aqueous solution can then be determined on the basis of the characteristic curve and the density .