Method for compensating sensor

Abstract

The invention relates to a method for compensating a sensor (2), comprising the following steps: establishing a measurement process for compensating the sensor, the measurement process specifying at least a predetermined desired value for at least one process variable (p) and at least one cross-influence variable (T) being reached; recording actual measurements of the at least one process variable (p) and of the at least one cross-impact variable (T) by means of the sensor and the sensor reference at predetermined desired values; interpolating the actual measurements recorded by the sensor into the at least one process variable (p) and the predetermined desired values of the at least one cross-impact variable (T) with the aid of the actual measurements recorded by the sensor reference; predicting a virtual measurement of the sensor; combining the measured values of the actual measured values and the virtual measured values recorded by the sensor to form a family of characteristic lines; and-determining a compensation coefficient of a compensation equation of the sensor based on the family of characteristic lines.

Description

Technical Field

[0001] The present invention relates to a method for compensating a sensor and a sensor for determining a measurement of at least one chemical and / or physical process variable. Background Technology

[0002] Sensors, especially pressure sensors, are used in, for example, process and automation technologies, and are used to monitor and / or determine at least one, for example, chemical and / or physical process variable of a medium.

[0003] The process variables determined by the sensor can be, for example, pressure, fill level, flow rate, temperature, pH value, redox potential, or the conductivity of the medium. Different, possible measurement principles involved in determining these process variables are known in the prior art and therefore will not be explained in further detail here. Sensors used for measuring pressure are particularly exemplified as absolute pressure sensors, relative pressure sensors, or differential pressure sensors.

[0004] Such sensors typically include a sensor unit that is at least partially and / or at least sometimes in contact with the process, and electronics for, for example, signal recording, signal evaluation, and / or signal feeding. The sensor's electronics are typically housed within a housing and additionally include at least one connection element for connecting the electronics to the sensor unit and / or external units. The connection element can be any electrical connection; even wireless connections can be used. The sensor's electronics and sensor unit can be embodied as a separate unit with a separate housing or as a unit sharing a housing.

[0005] Typically, sensor units do not directly determine and / or monitor at least one process variable. Instead, they determine and / or monitor at least one process variable through at least one measured variable, based on which at least one process variable can be calculated electronically. For example, in the case of a pressure sensor, a measuring diaphragm can be applied, the pressure of the process medium acts on the diaphragm, and the diaphragm transmits the pressure to a piezoresistive element with the aid of a pressure-transmitting medium. The effect of this pressure on the measuring diaphragm can be recorded by a piezoresistive resistor. Typically, in this case, the resistors are grouped together as a Wheatstone bridge to generate an output voltage, particularly a diagonal voltage, which ideally is linearly related to the pressure applied to the measuring diaphragm. However, in practice, this dependence is non-linear and also depends on cross-influences. For example, in the case of a pressure sensor, temperature plays a significant role as a cross-influence variable. Ideally, the implementation of a pressure sensor should cover as wide a temperature and pressure range as possible. Especially at the edges of these ranges, cross-influences are greater, and the dependence on the measured variable is more non-linear. However, in order to apply the sensor over a wide range (i.e., over a wide range of pressure and temperature), sensor compensation is required to eliminate the two aforementioned effects (nonlinearity and cross-effects).

[0006] In this scenario, for a pressure sensor, multiple pressure and temperature values ​​are accessed to compensate for nonlinearities and cross-influences in the sensor's output signal, aided by mathematical modeling. In this case, at least one cross-influence variable is set, and at least one measured variable is determined as a function of the cross-influence variable. Examples of cross-influence variables could be, for instance, temperature, as already mentioned. Furthermore, moisture may also have an undesirable cross-influence on the measured variable.

[0007] The compensation process is time-consuming and is associated with high capital costs, high energy requirements, large space requirements in manufacturing, and high sensor lead times in the compensation plant. Summary of the Invention

[0008] Therefore, the object of the present invention is to provide a reduction in the degree of compensation measurement, and thereby a reduction in compensation time.

[0009] According to the present invention, the objective is achieved by the method as defined in claim 1 and the sensor as defined in claim 1.

[0010] The present invention provides a sensor, particularly a pressure sensor, for compensating for measurements of at least one chemical and / or physical process variable of a medium, particularly pressure, in determining such a medium. The sensor is configured to determine the measured value of at least one process variable of the medium and at least one cross-influence variable affecting the measured variable. The method comprises the following steps: - Establish a measurement procedure for compensating the sensor, wherein the measurement procedure specifies that a predetermined expected value is achieved for at least one process variable and at least one cross-influence variable; - For at least a portion of the established measurement process, at the predetermined expected values ​​of at least one process variable and at least one cross-influence variable, the actual measured values ​​of at least one process variable and at least one cross-influence variable are recorded by a sensor and a sensor reference. - Interpolate the actual measurements recorded by the sensor to a predetermined expected value for at least one process variable and at least one cross-influence variable, with the help of the actual measurements recorded by the sensor reference. - Predict virtual measurements of the sensor with the aid of measurements of at least one other part of the unrecorded actual measurements that are actually recorded by the sensor and interpolated into the established measurement process; - The actual measurements recorded by the sensor and the virtual measurements predicted by the sensor are combined to form a family of feature lines; and - The compensation coefficients of the sensor's compensation equation are determined based on a family of characteristic lines.

[0011] This invention is based on the idea that the main factor in the time consumption of compensation is the time required to access each measurement or measurement point during the compensation period, and that reducing the number of measurements or measurement points to be accessed significantly shortens the compensation time. To this end, this invention provides a method in which a portion of the measured value is virtually determined.

[0012] An advantageous form of the embodiment of the method of the invention provides that, in the case of interpolation of the actual measured values ​​for each record, the interpolation is performed via at least one, preferably multiple, adjacent points, and particularly preferably via all points in the family of feature lines. In particular, the embodiment provides that one or more adjacent points of the family of feature lines through which interpolation is performed are actual recorded measured values ​​of at least one process variable and at least one cross-influence variable.

[0013] Another advantageous form of the method of the present invention provides at least one interpolation polynomial, in particular at least one third-order interpolation polynomial, for interpolation of at least one process variable and / or at least one cross-influence variable.

[0014] Another advantageous form of the method of the invention provides that, with the aid of an earlier determined statistical model, virtual measurements are predicted based on interpolated actual recorded measurements. In particular, the embodiments provide that, in order to determine the model parameters of the statistical model, random samples of sensors with the same construction are used earlier to actually record measurements and / or virtually predict measurements to compensate for the family of feature lines to be used by the sensor.

[0015] Another advantageous form of the method of the present invention provides the application of multilinear, multinomial, or neural network models as statistical models.

[0016] Another advantageous form of the method of the invention provides that the compensation coefficient is available in the sensor, in particular stored or provided in the sensor, to determine the compensated measurement value for the measurement operation.

[0017] Another advantageous form of the method of the present invention provides the standard deviation of actual recorded measurements used to predict virtual measurements or to interpolate to defined expected values.

[0018] The present invention also relates to a sensor, particularly a pressure sensor, for determining at least one chemical and / or physical process variable of a medium, particularly a measurement of pressure, wherein the sensor is implemented to determine the measurement of at least one process variable by means of a compensation equation having corresponding compensation coefficients, particularly compensation coefficients determined by the method according to claim 1. Attached Figure Description

[0019] The invention will now be explained in more detail with reference to the accompanying drawings, which are shown below: Figure 1 This is a schematic diagram of a sensor used in applying the method of the present invention. Figure 2 The present invention is for compensating sensors, such as, for example, Figure 1 The schematic flowchart of the method using the sensor shown and described.

[0020] Figure 3 It is a family of characteristic lines representing actual recorded measurements with process variables and cross-influence variables within a defined expected value. Figure 4 It is a family of feature lines containing interpolated, actually recorded measurements and virtually predicted measurements, and Figure 5 It is a family of feature lines that has both actual recorded measurements and virtual predicted measurements. Detailed Implementation

[0021] Figure 1 A pressure sensor 2 is schematically shown, which determines pressure as a physical process variable of a medium 1. The medium 1 is, for example, arranged in a container 5. The sensor 2 includes a sensor unit 3 and electronic components 4. Figure 1In the example, sensor unit 3 and electronics 4 are arranged in a shared housing. Alternatively, separate housings can be provided for sensor unit 3 and electronics 4, or the two units can be arranged spatially separate from each other. Sensor unit 3 is implemented to record the physical process variable in the form of pressure and the cross-influence variable in the form of temperature of medium 1, and in each case, provides the corresponding (analog or digital) measurement in the form of a signal to electronics 4 for further processing. Electronics 4 is further configured to output a pressure measurement signal compensated for the cross-influence variable of the process variable by a compensation equation having compensation coefficients determined according to a method described in more detail below.

[0022] The method for compensating sensors of the present invention can be used in virtually all types of sensors. To explain the method, an example using a pressure sensor will be used below.

[0023] Figure 2 A schematic process flow diagram of the compensation method of the present invention is shown.

[0024] In the first method step, a measurement process is established to compensate sensor 2. In this respect, Figures 3 to 5 The measurement graph is illustrated by way of example, which includes the process variable p to be recorded by sensor 2 and the cross-influence variable T, also recorded by sensor 2. In the case of the measurement graph 6 shown here and the method described below, everything is two-dimensional. The two-dimensional measurement graph 6 here includes the process variable pressure (p) on the x-axis and the cross-influence variable temperature (T) on the y-axis. However, the invention is not limited to the two-dimensional measurement graph 6, but can instead be used for n-dimensional measurement graphs or can be transferred to n-dimensional measurement graphs, in the case of which more than one process variable and / or one cross-influence variable can be considered. For example, moisture can be included as another cross-influence variable, making the measurement graph 6 three-dimensional in this case.

[0025] In the second method step following the first method step, actual measured values ​​are recorded for the process variable p and the cross-influence variable T via a sensor and a sensor reference. In this case, the recorded actual measured values ​​are the values ​​measured by the sensor 2 to be compensated and by the sensor reference. In this case, the actual measured values ​​are recorded within the measurement graph 6 at predetermined expected values. The expected values ​​may be located at the intersection of the vertical (cross-influence variable = temperature) and horizontal (process variable = pressure) lines, such as... Figures 3 to 5 As shown. To record the actual measured value at the predetermined expected value, sensor 2 and sensor reference are exposed to the corresponding test conditions, namely, the corresponding pressure and the corresponding temperature. The actual measured value can be, for example, an analog or digital raw measurement of the sensor unit.

[0026] However, according to the present invention, the actual measured values ​​of the sensor are not recorded at all predetermined desired values ​​or intersections within the family of feature lines 6, but only at a portion of them. For example, in Figure 3 Of the feature line family shown, only 15 actual measurements were recorded by the sensor and sensor reference at different desired values. The actual recorded measurements are... Figures 3 to 5 It is represented by a continuous circle of lines.

[0027] In the third method step below, such as... Figure 3 As shown, with the aid of actual measurements recorded by the sensor reference, the actual measurements of the process variable p and the cross-influence variable T recorded at predetermined expected values ​​of sensor 2 are interpolated into their predetermined expected values. In this case, the interpolation preferably occurs at least through multiple adjacent points of the actual recorded measurements (intersections or expected values ​​adjacent to each other in the horizontal and / or vertical directions in the measurement diagram at their expected values ​​or intersections). Particularly preferably, the interpolation occurs through all expected values. In the interpolation of actual measurements, for example, an interpolation polynomial can be used; in the current case, for the process variable pressure p and the cross-influence variable temperature T, therefore, in each case, a 2D interpolation polynomial can be used. Through interpolation, the measured value is calculated at the defined expected value, i.e., the actual recorded measured value is correspondingly corrected so that the interpolated (calculated) value exists at the defined expected value. As a result, the conversion from actual measured values ​​to ideal measurement conditions is achieved. This is in Figure 3 The values ​​are indicated by arrows at various measurement points. In the method of the present invention, the interpolated measurement values ​​thus obtained are stored for further use.

[0028] Then, in the fourth method step, such as Figure 4 As shown, virtual measurements are predicted for the process variable p and the cross-influence variable T by the sensor. In this case, values ​​interpolated from the actual measurements recorded by the sensor to predetermined expected values ​​are considered. Additionally, the standard deviation of the interpolated values ​​to the predetermined expected values ​​can also be used to predict the virtual measurements. Based on these values ​​(interpolated values ​​and / or their standard deviations), virtual measurements are predicted for at least a portion of the measurement graph where no actual measurements are recorded. Preferably, virtual measurements are predicted for all expected values ​​where no actual measurements are recorded by the sensor. The virtual predicted measurements are... Figure 4 and Figure 5The diagram is indicated by a dashed circle. To predict virtual measurements, a previously determined statistical model can be applied. This model can be, for example, a multilinear model, a multinomial model, or a neural network. The use of multilinear or multinomial models offers the advantage that model parameters can be determined very easily. To construct the model, random samples from sensor 2 of the same construction can be used, allowing two actual measurements and a predicted virtual measurement to be recorded within a defined time period. In model construction and parameter measurement, both the actual measurements recorded by different sensors and the virtual predicted measurements are interpolated to predetermined expected values. In this case, interpolation can be performed as described above. In this case, it is advantageous, but not absolutely necessary, that all actual measurements recorded by the sensors are used to predict the virtual measurement. As the number of model parameters increases, the number of random samples (i.e., the number of sensors used to record the actual measurements for model construction) also increases.

[0029] Then, in such as Figure 5 In the fifth method step shown, the actually recorded measurements and the virtually predicted measurements are combined to form a complete family of feature lines 6. In this case, the actually recorded measurements are preferably used, so those measurements that were not interpolated by the sensor are excluded, because otherwise unnecessary interpolation errors would be included in the compensation.

[0030] In the sixth method step below, compensation coefficients for the compensation equation used in the measurement operation of sensor 2 are determined based on the complete family of characteristic lines 6. With the compensation equation having the determined compensation coefficients, the process variable p is made independent of the cross-influence variable T in the measurement operation of sensor 2. Further, linearization of the sensor signal of the process variable can be performed via the compensation equation. These compensation coefficients are available in sensor 2 to be accessible during the measurement operation. This may occur, for example, because these are stored in the sensor, especially in the sensor's electronics.

[0031] List of reference markers

[0032] 1. Medium

[0033] 2. Sensors, especially pressure sensors

[0034] 3 Sensor Units

[0035] 4 Electronic devices

[0036] 5 containers

[0037] 6. Characteristic Line Family

[0038] p Process variables, especially pressure

[0039] T cross-influence variables, especially temperature

Claims

1. A method for compensating for a sensor (2), particularly a pressure sensor, for determining at least one chemical and / or physical process variable (p), particularly a pressure, of a medium (1), wherein, The sensor is configured to determine the measured value of at least one process variable of the medium (1) and at least one cross-influence variable (T) affecting the measured variable, wherein the method includes the following steps: - Establish a measurement procedure for compensating the sensor, wherein the measurement procedure specifies that a predetermined expected value is achieved for at least the at least one process variable and the at least one cross-influence variable; - For at least a portion of the established measurement process, at the predetermined expected value of the at least one process variable and the at least one cross-influence variable, the actual measured value of the at least one process variable and the at least one cross-influence variable is recorded by the sensor and the sensor reference. - The actual measurement values ​​recorded by the sensor are interpolated into the predetermined expected values ​​of the at least one process variable and the at least one cross-influence variable with the help of the actual measurement values ​​recorded by the sensor reference; - Predict virtual measurement values ​​of the sensor with the aid of actual measurement values ​​that are at least recorded by the sensor and interpolated into at least one other portion of the unrecorded actual measurement values ​​of the established measurement process; - The actual measurements recorded by the sensor and the virtual measurements predicted by the sensor are combined to form a family of feature lines; and - The compensation coefficients of the compensation equation of the sensor are determined based on the family of feature lines.

2. The method according to claim 1, wherein, In the interpolation of each recorded actual measurement value, the interpolation is performed via at least one, preferably multiple, adjacent points, and more preferably via all points in the family of feature lines.

3. The method according to the preceding claim, wherein, One or more adjacent points of the family of feature lines through which interpolation is performed are actual recorded measurements of the at least one process variable (p) and the at least one cross-influence variable (T).

4. The method according to one or more of the preceding claims, wherein, At least one interpolation polynomial, especially at least one third-order interpolation polynomial, is used for interpolation of the at least one process variable (p) and / or the at least one cross-influence variable (T).

5. The method according to one or more of the preceding claims, wherein, With the help of an earlier established statistical model, the virtual measurement value is predicted based on the interpolated actual recorded measurement value.

6. The method according to the preceding claim, wherein, To determine the model parameters of the statistical model, earlier random samples of sensors with the same construction were used to actually record measurements and / or virtually predict measurements to compensate for the family of feature lines to be used by the sensor.

7. The method according to any one of the preceding two claims, wherein, Apply multilinear, multinomial, or neural network models as statistical models.

8. The method according to one or more of the preceding claims, wherein, The compensation coefficient is available in the sensor, in particular stored or provided in the sensor, to determine the compensated measurement value for the measurement operation.

9. The method according to one or more of the preceding claims, wherein, A further step used to predict the virtual measurement is the standard deviation of the actual recorded measurement interpolated to the defined expected value.

10. A sensor, particularly a pressure sensor, for determining a measurement of at least one chemical and / or physical process variable (p), particularly pressure, of a medium (1), wherein, The sensor is implemented to determine the measured value of the at least one process variable by means of a compensation equation having corresponding compensation coefficients, in particular having compensation coefficients determined according to the method of claim 1.

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