APPARATUS AND AUTOMATED METHOD FOR EVALUATING SENSOR MEASURED VALUES, AND USE OF THE APPARATUS

Abstract

The invention specifies an apparatus for evaluating sensor measured values (1.1), having: —a sensor (1), wherein a model function that is suitable for a least squares regression and definable by a parameter vector is provided for evaluating the sensor measured values (1.1) of the sensor (1), wherein at least one parameter of the parameter vector forms a sensor output signal (3), and —a computing and evaluation unit (2) that has a neural network (2.1), which estimates the parameter vector on the basis of actually ascertained sensor measured values (1.1), and a least squares regression module (2.2), wherein the neural network (2.1) is trained with parameter vectors and the associated sensor measured values, and that is set up: .sup.∘—to use the trained neural network (2.1) to ascertain at least one parameter estimate vector for sensor measured values (1.1) measured using the sensor (1) as an input variable for the least squares regression module (2.2), .sup.∘—if a convergence criterion is satisfied for the performance of the least squares regression, to terminate the least squares regression and .sup.∘—to output the at least one parameter of the most recently ascertained parameter vector as sensor output signal (3). An associated automated method for evaluating sensor measured values and a use of the apparatus are likewise specified.

Claims

1. A device for evaluating sensor measured values the device comprising: a sensor configured to provide sensor measured values, wherein a model function suitable for a least squares regression and configured to be defined by a parameter vector is provided for an evaluation of the sensor measured values of the sensor, wherein at least one parameter of the parameter vector forms a sensor output signal; and an evaluation unit including a neural network that is configured to estimate the parameter vector based on actually ascertained sensor measured values and a least squares regression module, wherein the neural network is trained with parameter vectors and associated sensor measured values, the neural network further configured to ascertain at least one parameter estimated vector as input quantity for a least squares regression of the least squares regression module for sensor measured values measured by the sensor by way of the trained neural network, to terminate the least squares regression when a convergence criterion is met when carrying out the least squares regression, and to output the at least one parameter of a last ascertained parameter vector from the least squares regression with the smallest square error as sensor output signal; wherein the evaluation unit includes an assessment module connected downstream of the least squares regression module, the assessment module configured to ascertain a success status of the evaluation from a residual of the least squares regression, information about a termination status of the least squares regression, and at least one further item of information about the least squares regression, the assessment module configured to output the success status, the information, and the at least one further item of information as a further sensor output signal, wherein the success status may be successful or unsuccessful.

2. (canceled)

3. The device of claim 1, wherein the assessment module is configured to ascertain the success status based further on the at least one parameter of the last ascertained parameter vector, the sensor measured values, or the at least on parameter of the last ascertained parameter vector and the sensor measured values.

4. The device of claim 1, wherein the assessment module is configured to ascertain quality information about the evaluation from the residual of the least squares regression, information about the termination status of the least squares regression and at least one further item of information about the least squares regression and to output the quality information, the information about the termination status, and the at least one further item as the further sensor output signal.

5. The device of claim 4, wherein the quality information is a Euclidean norm of the residual or a dimensionless-normalized Euclidean norm of the residual.

6. The device of claim 4, wherein the assessment module is configured to set the success status to “successful” when the quality information remains below a predefined quality threshold.

7. The device of claim 1, wherein the sensor is configured to provide sensor measured values for an evaluation of a chromatogram in gas chromatography.

8. The device of claim 1, wherein the sensor is configured to provide sensor measured values for spectral evaluation in spectroscopy.

9. The device of claim 1, wherein the sensor is configured to provide sensor measured values for a spectral evaluation of timeseries.

10. The device of claim 1, wherein the sensor is configured to provide sensor measured values for an analysis of audio data.

11. The device of claim 1, wherein the sensor is configured to provide sensor measured values for a recognition of objects in image data.

12. An automated method for evaluating sensor measured values, the method comprising: providing a model function configured for a least squares regression and configured to be defined by a parameter vector for an evaluation of the sensor measured values, wherein a sensor output signal is formed by at least one parameter of the parameter vector; providing a neural network configured to estimate the parameter vector based on actually ascertained sensor measured values and a least squares regression module, wherein the neural network is trained with parameter vectors and associated sensor measured values; ascertaining at least one parameter estimated vector as input quantity for a least squares regression of the least squares regression module for measured sensor values by the trained neural network, wherein the least squares regression is terminated when a convergence criterion is met when carrying out the least squares regression and the at least one parameter of the last ascertained parameter vector from the least squares regression with the smallest square error is output as sensor output signal; and ascertaining and outputting a success status of the evaluation from a residual of the least squares regression, information about a termination status of the least squares regression, and at least one further item of information about the least squares regression, wherein the success status may be successful or unsuccessful.

13. (canceled)

14. The method of claim 12, wherein, the at least one parameter of the last ascertained parameter vector, the sensor measured values, or the last ascertained parameter vector and the sensor measured values are used to ascertain the success status.

15. The method of claim 12, wherein quality information about the evaluation is ascertained from information about the termination status of the least squares regression and at least one further item of information about the least squares regression and output as a further sensor output signal.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG. 1 depicts a block diagram of a device for evaluating sensor measured values according to an embodiment.

[0039] FIG. 2 depicts a flowchart of a method for evaluating sensor measured values according to an embodiment.

DETAILED DESCRIPTION

[0040] FIG. 1 depicts a block diagram of a device for evaluating sensor measured values 1.1. A sensor 1 generates sensor measured values 1.1, that serve as input signal for a computing and evaluation unit 2. To evaluate the sensor measured values 1.1 of the sensor 1, provision is made for a model function suitable for a least squares regression, which model function is able to be defined by a parameter vector. At least one parameter of the parameter vector forms a sensor output signal 3. The sensor output signal 3 and further sensor output signals are output and displayed on a display unit 4.

[0041] The computing and evaluation unit 2, for example a computer, includes a neural network 2.1 that estimates the parameter vector on the basis of actually ascertained sensor measured values 1.1 and a least squares regression module 2.2. The neural network 2.1 has been trained with parameter vectors and the associated sensor measured values 1.1. The computing and evaluation unit 2 is configured to ascertain at least one parameter estimated vector as input quantity for the least squares regression module 2.2 for sensor values 1.1 measured by the sensor 1 by way of the trained neural network 2.1. The least squares regression is terminated when a convergence criterion is met when carrying out the least squares regression and the one or more parameters of the last ascertained parameter vector are output as sensor output signal 3.

[0042] The convergence criterion may for example be falling below a predefined threshold value of the residual sum of squares of the least squares regression, a predefinable maximum number of iterations or a predefinable maximum time.

[0043] The computing and evaluation unit 2 also includes an assessment module 2.3 that is connected downstream of the least squares regression module 2.2. Inputs for the assessment module 2.3 are for example the residual of the last model evaluation of the least squares regressions in the least squares regression module, information about the termination status of the least squares regressions and at least one further item of information about the least squares regressions. From the inputs, the assessment module 2.3 ascertains a success status of the evaluation and outputs this as further sensor output signal 3. The success status may be “successful” or “unsuccessful”. When ascertaining the success status, in addition, at least one parameter of the last ascertained parameter vector of the least squares regressions and/or the sensor measured values 1.1 may additionally be taken into consideration.

[0044] The assessment module 2.3 may also be configured to ascertain quality information about the evaluation from the residual of the least squares regressions, information about the termination status of the least squares regression and at least one further item of information about the least squares regressions and to output it as further sensor output signal 3. The quality information (also able to be called “quality of sensing”) is a continuous, non-negative scalar variable. The quality information may be for example the Euclidean norm of the residual or the dimensionless-normalized Euclidean norm of the residual of the selected least squares regression.

[0045] The assessment module may also be configured to set the success status to “successful” when the quality information remains below a predefined quality threshold.

[0046] One variant of the abovementioned ascertainment of the quality information is that of restricting the area of the formation of the Euclidean norm. If for example it is known that the relevant information is located in a certain area in the measurement vector, then this may be selected in a targeted manner and the deviation between model and measurement may be examined only in this area. The area containing the relevant information may be output from the last model evaluation as “auxiliary quantity”.

[0047] One further variant is that of applying weight factors (->vector) to the residual prior to forming the Euclidean norm. A “soft” selection thus takes place, in contrast to the “hard” masking (previous case). The weight factors are likewise output with the last evaluation of the model function as auxiliary quantities.

[0048] A further variant makes provision to link the least squares regression algorithm to the termination status, for example using a heuristic policy that additionally assesses certain termination status events negatively.

[0049] The described device may be used, inter alia, for an evaluation of a chromatogram in gas chromatography. Good initial parameter starting values for the least squares regression are very important here, since, due to the large number of peaks, there are a large number of local minima in the LS regression task and only one of these is the correct global optimum, e.g., the convergence of a typical LS regression, for example its algorithm, is not robust. The described device may be used for a spectral evaluation in high-resolution spectroscopy, for example spectroscopy based on tunable lasers. In this case too, good initial parameter starting values for the LS regression are very important, since, due to the spectral fingerprint, there are a large number of local minima and only one of these is the correct global optimum, e.g., the convergence of a typical LS regression algorithm is not robust. The described device may be used for a spectral evaluation of timeseries, such as for example of measured voltage/current, ultrasonic vibrations or the like, to check the state or establish the state of technical devices or apparatuses. Resonances (that is to say peaks) are often contained in spectral data of timeseries of physical signals. These often follow specific patterns, such as individual peaks may contain harmonics. Since there may be multiple base resonances, the resulting spectrum may appear highly complex. If a generic model is to be adapted, then the base resonance frequencies first have to be known. Identifying these is a challenging problem that is exacerbated by noise that is present and any other interfering signals. If it is necessary to adapt a model in which the parameters that influence the base resonant frequencies are adapted, an initial estimate thereof is essential for a successful LS regression. One example is the state monitoring of the current of a motor of unknown size and speed. The described device may be used for an analysis of audio data, such as speech. The AI of the neural network here may perform speech recognition and determine further parameters for speech synthesis. The physical model here is a suitable speech synthesis module. It should be able to be parameterized, such that the speech spoken by the speaker is able to be reproduced sufficiently accurately by way of the further parameters. The verification step is performed by comparing the measurement with the synthesis signal. The described device may be used for an identification of objects in image data. Good models (CAD or the like) of the objects of interest are often present in industrial applications. The scenery may be simulated with a suitable variation of (interfering) backgrounds. Parameters such as position of the object or of the objects are parameters of the computing model, along with orientation in space. The AI of the neural network is trained to estimate at least these parameters. The LS regression is then used to improve the estimate, and the verification is then performed. An LS regression that operates on image data is no different in principle from a (one-dimensional) non-linear regression. The model is compared point for point with the measurement (the recorded image) and then the mean squared error is formed.

[0050] In the case of image data analysis, other test criteria may be used that are more expedient, for example weighting of the model and measurement before the squared deviation is calculated. A weight function may then for example weight the areas more where the payload signal includes a high amplitude, or where the desired “information” is contained. This may be used to suppress interference outside the area of interest and thus to reduce the rejection rate.

[0051] FIG. 2 depicts a flowchart of an automated, for example computer-implemented, method for evaluating sensor measured values. In the first step 101, a model function suitable for an LS regression and able to be defined by a parameter vector is provided for an evaluation of the sensor measured values. A sensor output signal is formed by at least one parameter of the parameter vector. In the second step 102, a neural network that estimates the parameter vector on the basis of actually ascertained sensor measured values and a least squares regression module are provided. The neural network has been trained with parameter vectors and the associated sensor measured values in a previous step 100.

[0052] In the third step 103, at least one parameter estimated vector is ascertained as input quantity for the least squares regression module for measured sensor values by way of the trained neural network. In the following fourth step 104, the LS regression is carried out and the least squares regression is terminated when a convergence criterion is met when carrying out the least squares regression. In a fifth step 105, the at least one parameter of the last ascertained parameter vector is then output as sensor output signal.

[0053] In a sixth step 106, a success status of the evaluation is ascertained from the residual of the least squares regression, information about the termination status of the least squares regression and at least one further item of information about the least squares regression and, in the seventh step 107, is output as further sensor output signal. The success status may be “successful” or “unsuccessful”.

[0054] In order to ascertain the success status in the sixth step 106, at least one parameter of the last ascertained parameter vector and/or the sensor measured values may additionally be taken into consideration.

[0055] In the eighth step 108, quality information about the evaluation is ascertained from information about the termination status of the least squares regression and at least one further item of information about the least squares regression and, in the ninth step 109, is output as further sensor output signal.

[0056] It is to be understood that the elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present invention. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent, and that such new combinations are to be understood as forming a part of the present specification.

[0057] While the present invention has been described above by reference to various embodiments, it may be understood that many changes and modifications may be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.