Method for detecting a blocked effective pressure line of a pressure measuring arrangement

Abstract

The invention relates to a computer-implemented method for detecting a blocked effective pressure line of a pressure measuring arrangement, comprising the following steps: - sensing a pressure measurement signal by means of the pressure measuring arrangement (1); - condensing measured values of the pressure measurement signal into blocks (Bt-1, Bt); - creating in particular acoustic frequency spectra for each block, wherein each frequency spectrum has a plurality of frequency components (c1,t-1; cn,t); - deriving statistical distributions (V1; Vn) such that, for each frequency component, a statistical distribution, into which the corresponding frequency component of a plurality of blocks (Bt-1, Bt) is included, is derived; - determining statistical features (µ; σ) at least from the statistical distributions (V1; Vn); - deciding, from the statistical features (µ; σ) determined from the statistical distributions, whether a blocked effective pressure line exists.

Description

[0001] Method for detecting a blocked differential pressure line of a pressure measuring arrangement

[0002] The invention relates to a computer-implemented method for detecting a blocked differential pressure line of a pressure measuring arrangement, a data processing device with means for carrying out the method, a computer program, a computer-readable medium and a pressure measuring arrangement.

[0003] European patent EP 1 840 549 B1 protects a device for detecting blockages in differential pressure lines, the device comprising two pressure sensors for detecting time series of the static pressure in each differential pressure line and a differential pressure sensor for detecting time series of the difference between the pressures in the two differential pressure lines and three calculation units for calculating the fluctuations of the two static pressures and the differential pressure based on the time series and the sums of squares of the fluctuations; correlation coefficient calculation units for determining the correlation coefficients between the time series of a static pressure and the differential pressure and an evaluation unit which detects blockages in the differential pressure lines based on the correlations and identifies which of the differential pressure lines is or are blocked.

[0004] The German patent application DE 10 2013 110 059 A1 describes a differential pressure measuring arrangement which detects blocked differential pressure lines based on a correlation of a differential pressure signal, which represents a pressure difference between a first medium pressure and a second medium pressure, and a temperature signal, which correlates with a temperature of a differential pressure line.

[0005] Furthermore, there are other approaches for detecting a blocked differential pressure line. In particular, there are approaches that are based on changing the frequency of the pressure signal fluctuations.

[0006] All approaches are effective in themselves, but each has specific weaknesses. Some methods fail to work, or only work to a limited extent, if the underlying process is too dynamic and subject to large fluctuations. Other methods only work within a specific pressure signal range, which dynamic processes regularly leave. Still other methods exhibit weaknesses when long differential pressure lines create resonances that affect the signal power and can sometimes artificially increase it.

[0007] The invention is based on the object of remedying this problem. This object is achieved according to the invention by the method according to claim 1, the data processing device with means for implementing the method according to claim 14, the computer program according to claim 15, the computer-readable medium according to claim 15, and the pressure measuring arrangement according to claim 17.

[0008] The computer-implemented method according to the invention for detecting a blocked differential pressure line of a pressure measuring arrangement comprises the following steps: detecting a pressure measuring signal by means of the pressure measuring arrangement, preferably for a defined period of time;

[0009] Combining, preferably consecutive, measured values ​​of the pressure measurement signal into, preferably overlapping, blocks, each of which preferably has the same length;

[0010] Generating, in particular, acoustic frequency spectra for each block, wherein each frequency spectrum has several frequency components;

[0011] Deriving statistical distributions such that for each frequency component a statistical distribution is derived which includes the corresponding frequency component of several blocks;

[0012] Determining statistical characteristics at least based on statistical distributions;

[0013] Decide whether a blocked differential pressure line is present based on the statistical characteristics determined from the statistical distributions.

[0014] According to the invention, the recording and evaluation of a frequency spectrum of a pressure measurement signal from a pressure measurement arrangement is proposed. The frequency spectrum can preferably be an acoustic frequency spectrum that lies in a range below 20 kHz, in particular below 2.5 kHz, in particular in the range from 0 to 2 kHz, and most particularly in the range from 5 to 200 Hz. The (acoustic) frequency spectrum characterizes the pressure measurement arrangement and essentially represents a fingerprint of the pressure sensor. A change in the pressure measurement arrangement affects the frequency spectrum, in particular the acoustic frequency spectrum. This is utilized according to the invention to detect a blocked differential pressure line based on statistical features derived from statistical distributions.The statistical features are preferably statistical features relating to the process in which the pressure measuring arrangement is arranged to determine the pressure measurement signal.

[0015] An advantageous embodiment of the method according to the invention provides that the method step of determining the statistical features provides that each frequency component of the frequency spectra of the blocks is binarized in such a way that each frequency component is mapped to a value from the set {0; 1} as a function of a distance measure, wherein the distance measure indicates a distance between the respective frequency component and a value, in particular a mean value, which is derived from the statistical distribution in which the respective frequency component has been included.In particular, the embodiment may further provide that the binarized frequency components of the respective block are aggregated to form a random variable for the respective block, wherein a temporal course of the random variable for the blocks is further observed and a further statistical distribution for the random variable is estimated via a mean value and a standard deviation, and wherein at least the mean value and the standard deviation of the further statistical distribution serve as the statistical features.

[0016] A further advantageous embodiment of the method according to the invention provides that the determined statistical features are compared with a baseline in order to decide whether a blocked differential pressure line is present. In particular, the embodiment can provide that the baseline is generated in a learning phase in which the pressure measurement signal is recorded by means of the pressure measurement arrangement over a defined period of time with unblocked differential pressure lines, wherein preferably successive measured values ​​of the recorded pressure measurement signal are combined into preferably overlapping blocks, which preferably each have the same length, and, in particular,acoustic frequency spectra are generated for each block in the learning phase, wherein the frequency components of the frequency spectra of the blocks in the learning phase are binarized, preferably by a Bernoulli transformation, and the binarized frequency components of the respective block are aggregated to form a random variable for the respective block of the learning phase, wherein a temporal course of the random variables for the blocks of the learning phase is observed or recorded and a further statistical distribution for the random variables of the blocks in the learning phase is estimated via a mean value and a standard deviation, and wherein at least the mean value and the standard deviation of the further statistical distribution for the random variables of the blocks in the learning phase serve as at least part of the baseline.

[0017] A further advantageous embodiment of the method according to the invention provides that the comparison of the determined statistical characteristics with the baseline is carried out using a hypothesis test, in particular a z-test. If a test variable of the hypothesis test falls below a predeterminable threshold or lies outside a predeterminable range with an upper and a lower threshold, this is evaluated as a blocked differential pressure line. A further advantageous embodiment of the method according to the invention provides that, before generating the frequency spectra from the pressure measurement signal, idle phases of the process are first filtered out of the pressure measurement signal, and only then are the frequency spectra of the respective blocks determined.In particular, the embodiment can provide that in order to filter out the idle phases of the process, at least one variance is calculated for each of the blocks, and based on the variance, it is decided whether it is a idle phase of the process and / or the variance is compared with a configurable or adjustable threshold value in order to decide whether it is a idle phase of the process, wherein the threshold value is preferably in a range < -70 dB, particularly preferably < -80 dB, very particularly preferably in the range from -80 to -90 dB.

[0018] A further advantageous embodiment of the method according to the invention provides that the frequency spectra are generated by means of a discrete Fourier transformation, in particular a fast Fourier transformation.

[0019] A further advantageous embodiment of the method according to the invention provides that the frequency spectra of the respective blocks are high-pass filtered, so that frequency components below a predeterminable cutoff frequency are essentially filtered out or attenuated. In particular, the embodiment can provide that the predeterminable cutoff frequency is selected such that it lies in the range of a few Hz, preferably less than 5 Hz, particularly preferably in the range of 1-5 Hz.

[0020] The invention further relates to a data processing device with means for carrying out the method according to at least one of the previously described embodiments.

[0021] The invention further relates to a computer program with instructions which, when the program is executed by a computer, cause the computer to carry out the method according to at least one of the previously described embodiments.

[0022] The invention further relates to a computer-readable medium containing instructions which, when executed by a computer, cause the computer to carry out the method according to at least one of the previously described embodiments.

[0023] The invention further relates to a pressure measuring arrangement, at least comprising: a pressure transducer for detecting at least one media pressure of a medium; an operating and / or evaluation circuit for providing a pressure measurement signal which depends on the at least one media pressure; at least one effective pressure line which is connected to a pressure inlet of the pressure transducer in order to apply the at least one media pressure to the pressure transducer; a detection unit which is preferably designed as part of the operating and / or evaluation circuit and is configured to carry out the method according to one or more of the preceding claims in order to detect whether at least one effective pressure line is blocked.

[0024] The invention is explained in more detail with reference to the following drawings. It shows:

[0025] Fig. 1 : an overall view of an embodiment of a pressure measuring arrangement according to the invention,

[0026] Fig. 2: a sensor module and an electronic module of the pressure measuring arrangement electrically connected to the sensor module in detail,

[0027] Fig. 3: schematic representation of the method according to the invention for detecting a blocked differential pressure line of a pressure measuring arrangement,

[0028] Fig. 4: a block diagram of the method for detecting a clogged differential pressure line of a pressure measuring arrangement during an inference phase,

[0029] Fig. 5: another block diagram of the method for detecting a clogged differential pressure line of a pressure measuring arrangement, showing both the learning and the inference phases,

[0030] Fig. 6: a spectrogram for chunks adjusted for mean and transformed into the frequency domain using a discrete Fourier transform, and

[0031] Fig. 7: a Mahalanobis ellipse with radius 1.5.

[0032] Figure 1 shows a highly simplified and schematic overall representation of a pressure measuring arrangement. Fig. 1 shows a differential pressure measuring arrangement. Although the invention is described below with reference to the differential pressure measuring arrangement, it is not limited to this, but can also be applied to any other pressure measuring arrangement, in particular an absolute and / or relative pressure measuring arrangement. The differential pressure measuring arrangement shown in Fig. 1 comprises a differential pressure measuring transducer 10, which has a sensor module 11, which is arranged between a first, high-pressure side process connection flange 12 and a second, low-pressure side process connection flange 13, and an electronics module 14. Both modules are arranged in a housing of the differential pressure measuring transducer.The electronics module can be connected to a process control system 18 via a two-wire cable 16, whereby the electronics module 14 can communicate and be supplied with power via the two-wire cable. The two-wire cable can be operated, in particular, as a fieldbus according to the Profibus or Foundation Fieldbus standard or according to the HART standard. Such differential pressure transmitters are known per se and are manufactured and marketed by the applicant, for example, under the Deltabar brand.

[0033] The differential pressure measuring arrangement can further comprise a differential pressure transducer 20 for installation in a pipeline 21. The differential pressure transducer comprises an orifice plate 22, a first pressure tap channel 23 on a high-pressure side of the orifice plate 22, and a second pressure tap channel 24 on a low-pressure side of the orifice plate 22. The high-pressure-side process connection flange 12 is connected to the high-pressure-side pressure tap channel 23 via a high-pressure-side differential pressure line 25, and the low-pressure-side process connection flange is connected to the low-pressure-side pressure tap channel 24 via a low-pressure-side differential pressure line 26. The terms "high-pressure side" and "low-pressure side" refer to a pressure difference generated by a flow (from left to right in the drawing), which is proportional to the square of the flow velocity and, for example, is in the order of magnitude of a few tens to hundreds of mbar.The static pressure on which this flow-dependent pressure difference is superimposed can, for example, range from 1 bar to several 100 bar.

[0034] Fig. 2 shows a sensor module 11 and an electronics module 14 electrically connected to the sensor module 11 in detail. For reasons of simplicity, the arrangement of the two modules shown in Figure 2 is arranged side by side and thus differs from that in Figure 1. The sensor module in this case is a piezoresistive pressure sensor element 110 with a measuring diaphragm 112, wherein the pressure sensor element 110 has resistance elements in a bridge circuit in order to convert a pressure-dependent deformation or deflection of the measuring diaphragm into an electrical sensor module signal. Instead of the piezoresistive pressure sensor, a capacitive pressure sensor can also be provided, in which case the measuring diaphragm has an electrode whose capacitance relative to an electrode on a rigid counter-body is a measure of the pressure-dependent deformation or deflection of the measuring diaphragm.Details of electrical transducers are familiar to a person skilled in the field of pressure measurement technology and need not be explained in detail here. The sensor module outputs the sensor module signal, which is dependent on the detected pressure difference, to the electronic module 14. An operating and / or evaluation circuit 160 of the electronic module 14 generates a differential pressure measurement signal representing the pressure difference based on the sensor module signal and can output it, for example, via the two-wire line 16, particularly to the process control system 18. For this purpose, the operating and / or evaluation circuit can, for example, have a microprocessor 162 for processing sensor module signals from the pressure sensor 110 digitized by means of an ADC 164.

[0035] The pressure measuring arrangement further comprises a detection unit 150 configured to execute the computer-implemented method described below. As shown in Fig. 2, the detection unit 150 can be part of the operating and / or evaluation circuit 160 or, alternatively, can be implemented as a separate unit, e.g., in a cloud.

[0036] The computer-implemented method is divided into several method steps: a preprocessor step executed in a preprocessor 152, in which the pressure measurement signal or differential pressure measurement signal is transformed into a frequency spectrum, a feature extractor step executed in a feature extractor 154, in which useful features (hereinafter also referred to as characteristics or features) to be described in more detail are derived from the frequency spectrum, and an optional classifier step executed in a classifier 156, in which a decision is made on the basis of the previously derived features as to whether the differential pressure line 25, 26 is blocked or not. The method steps are executed one after the other. Fig. 3 shows a general block diagram of the computer-implemented method for detecting blocked differential pressure lines 25, 26 of a pressure measuring arrangement 1.

[0037] In the first step a), the pressure measurement signal is first sampled at the highest possible sampling rate in order to obtain successive measured values, preferably digitized measured values, particularly preferably digitized measured values ​​by the ADC 164. The sampling rate is preferably selected such that it is at least a few tens of Hz, preferably at least 50 Hz, particularly preferably at least 100 Hz.

[0038] To prevent idle phases associated with the process, whose pressure values ​​are determined by means of the pressure measuring arrangement 1, from making it difficult to detect a clogged differential pressure line 25, 26, it is necessary to detect and filter out these idle phases as quickly as possible. This can generally be achieved in different ways. A simple subsequent method can provide for the preferably consecutive measured values ​​in the preprocessor step to be divided into preferably overlapping blocks B t-i, Bt (hereinafter also referred to as chunks), preferably of the same length. Chunks B t -i, Bt form inseparable units and all measured values ​​of a chunk B t -i , Bt are processed together. Subsequently, in the preprocessor step, for each chunk B t -i, B t A variance and optionally a mean are calculated for each. The variance is then compared with a configurable or adjustable threshold. If the variance of the chunk BM or B t below the threshold, the chunk BM or B tignored because it is most likely a rest phase in the process. The threshold value depends on the process and can vary from process to process. For example, the threshold value for a level process, in which a level is determined using a pressure measuring arrangement, may be different than for a flow process, in which a flow is determined using a differential pressure measuring arrangement. Typically, however, the threshold value is in a range < -70 dB, preferably < -80 dB, and particularly preferably in the range of -80 to -90 dB.

[0039] The remaining chunks B t -i, B tare then mean-corrected in the preprocessor step and transformed into the frequency domain using a discrete Fourier transformation, e.g. a Fast Fourier Transformation (FFT), in order to obtain the Fourier coefficients required for the subsequent feature extractor step for each chunk BM , B t to obtain. Fig. 6 shows an example of such a spectrogram. After the Fourier transformation, the preprocessor step can optionally provide for the application of a high-pass filter 158, which operates at a predeterminable cutoff frequency in order to filter out the actual pressure value. The predeterminable cutoff frequency can, for example, be selected such that it lies in the range of a few Hz, preferably less than 5 Hz, particularly preferably in the range of 1-5 Hz. The output of the preprocessor after processing consists of the (possibly high-pass filtered) Fourier coefficients of a chunk. This is illustrated in Fig. 3 by method step b).

[0040] In the feature extractor step following the preprocessor step, the features required to detect a clogged differential pressure line 25, 26 are determined. For this purpose, a variant is proposed that is essentially based on the application of classical statistics and is shown in Figs. 4 and 5.

[0041] This variant is divided into a learning phase (process steps a) - e) in Fig. 3), in which features typical for the process are first recognized, and an inference phase in which the spectra are analyzed based on the learned patterns during the actual operation of the pressure measuring arrangement.

[0042] The aim of the learning phase is to estimate covariance matrices of all frequency components CI,M ; c n ,t and, based on this, the determination of the distribution of a random variable. The learning process, in turn, is divided into several phases and can be described in detail as follows:

[0043] In a first phase of the learning process (“Vert. Estimator (Component)” in Fig. 5), the statistical distributions i- n of the individual Fourier coefficients (i.e. the respective value of the individual frequency components CI,M; c n ,t) over different blocks B t -i, B t The individual frequency components Ci, t -i; c n ,t of a block B t -i, Bt are each treated separately. The real and imaginary parts of the frequency component Ci, t -i ; c n ,t are initially considered as separate random variables, which allows them to be interpreted as a bivariate probability distribution for each frequency. Alternatively, it is sufficient to consider the amplitudes of the individual Fourier coefficients. For the description of each of these statistical distributions Vi-V nTheir covariance matrices and means are continuously estimated. This process continues until the estimates are sufficiently stable. In other words, it is first checked whether the differences between the current estimates of the covariance matrices and means at time t are sufficiently small compared to those at time t - 1. The error can be sufficiently small, for example, if the mean square error sum of the covariance matrices and means at time t differs by less than, for example, 10' compared to those at time t-1. 5differ from each other. Once the estimates exhibit sufficiently small errors, a distance metric (more precisely: the Mahalanobis distance) is induced over each distribution using the preferably inverted covariance matrices. This distance metric, in simple terms, indicates the distance of a point (in standard deviations) from the center of the distribution. The first phase is illustrated in Fig. 3 by process step c).

[0044] In the next phase (process step d) in Fig. 3) of the learning process, binarization is carried out in such a way that a radius R, especially in standard deviations, is defined that describes a circle on these statistical distributions, where the radius R is based on a distance metric, in particular the Mahalanobis distance. Now, it is determined for continuous Fourier coefficients whether they lie inside or outside the circle. This creates a Bernoulli process for each frequency component with an underlying Bernoulli distribution whose parameter specifies the probability that a point ends up outside the ellipse. Fig. 3 shows schematically such Mahalanobis ellipses ME and Fig. 7 shows such a Mahalanobis ellipse ME with radius 1.5.

[0045] In a subsequent phase (process step e) in Fig. 3) of the learning process, an aggregation is carried out in such a way that for each Fourier sample, ie the Fourier coefficients of each block or chunk at a time (in Fig. 3 e.g. at time t and Li), of the preprocessor all Bernoulli samples, ie each (binary) decision whether the Fourier coefficient lies inside or outside the circle, for each frequency component to a random variable w t -i; w t be aggregated. This can be done, for example, by calculating the arithmetic mean. The course of these mean values ​​is observed over time, and their statistical distribution VL is estimated using the mean and standard deviation p; o. These two quantities, together with the covariance matrices and mean values ​​of the (individual) frequency components, serve as a baseline for the inference phase.

[0046] The learning phase is preferably carried out for a defined period of time. After the learning phase has been completed and the baseline has been determined or established, the inference phase (process steps a), b), d), and f) in Fig. 3) can then be carried out, preferably during the actual measurement operation, in which a flow rate is determined using the differential pressure measuring system.

[0047] For this purpose, the Fourier coefficients are first binarized, similar to the baseline learning phase. Steps a), b), and d) are performed as described above in the learning phase. Furthermore, the inverted covariance matrices and mean values ​​from the learning phase are available for calculating the Mahalanobis distances.

[0048] Subsequently (shown by process step f) in Fig. 3), the binary variables of the distributions are aggregated again into a single sample. From the samples generated in this way, another statistical distribution Vi (for the inference phase) is also determined so that it can be compared with the baseline. For this purpose, a window or a window function can be used which contains the last N samples, from which a sample mean can be calculated. The distance between the current distribution Vi and the learned distribution VL (baseline) is determined using a distance measure. This can be done, for example, using a hypothesis test, in particular a z-test, which determines how different the sample distribution is from the statistical distribution VL of the baseline and thus the distance between the current distribution and the learned statistical distribution (baseline). The test can be one-sided or two-sided.

[0049] If this test variable falls below a threshold value in a one-sided test (e.g. in a fill level measurement) or if the test variable lies outside a range consisting of two threshold values, an upper and a lower threshold value, in a two-sided test (e.g. in a flow measurement), unnatural process behavior is detected and this is evaluated or output as a blocked differential pressure line 25, 26. In principle, the evaluation or output in the feature extractor step that a blocked differential pressure line 25, 26 is present can be sufficient to detect a blocked differential pressure line 25, 26. However, incorrect results can arise in this case. For this reason, it can optionally be provided that in the classifier step following the feature extractor step, additional smoothing of the diagnostic signal originating from the feature extractor is carried out using a mean value filter.The mean filter aggregates the last M statements resulting from the feature extractor step and decides, depending on the threshold value, whether a blocked differential pressure line actually exists or not.

[0050] List of reference symbols

[0051] 1 pressure measuring arrangement

[0052] 10 pressure transducers

[0053] 11 Sensor module

[0054] 12, 13 Process connection flanges

[0055] 14 Electronic module

[0056] 16 two-wire cable

[0057] 18 Process control system

[0058] 20 differential pressure sensors

[0059] 21 Pipeline

[0060] 22 aperture

[0061] 23, 24 pressure tap channels

[0062] 25, 26 Differential pressure lines

[0063] 110 Pressure sensor element

[0064] 112 measuring membrane

[0065] 140 Pressure transmission medium

[0066] 150 detection units

[0067] 152 Preprocessor

[0068] 154 Feature Extractor

[0069] 156 Classifier

[0070] 158 high-pass filters

[0071] 160 Operating and / or evaluation circuit

[0072] 162 microprocessor

[0073] 164 ADC

[0074] Vl-Vn Statistical Distributions

[0075] VL Further statistical distribution or distribution from the learning phase

[0076] VI Further statistical distribution or distribution from the inference phase p; o Statistical characteristics, especially mean and standard deviation

[0077] Bt-i, Bt blocks or chunks

[0078] C1 ,t-1 ; C n ,t frequency components of the frequency spectra of the blocks

[0079] R radius for the Mahalanobis ellipse

[0080] ME Mahalanobis ellipse wt-i; w t Random variable

Claims

Patent claims 1 . A computer-implemented method for detecting a clogged differential pressure line of a pressure measuring arrangement comprising the following steps: Detecting a pressure measurement signal by means of the pressure measurement arrangement (1), preferably for a defined period of time; Combining, preferably consecutive, measured values ​​of the pressure measurement signal into, preferably overlapping, blocks (B t -i, Bt), each of which preferably has the same length; Generating, in particular, acoustic frequency spectra for each block, where each frequency spectrum contains several frequency components (ci, t -i; c n ,t); Deriving statistical distributions (V1 ; V n ) such that for each frequency component a statistical distribution into which the corresponding frequency component of several blocks (B t-i, Bt); determining statistical characteristics (p; o) at least based on the statistical distributions (V1 ; V n ); Decide on the basis of the statistical characteristics (p; o) determined from the statistical distributions whether a blocked differential pressure line (25, 26) is present.

2. The method according to claim 1, wherein the method step of determining the statistical features (p; o) provides that each frequency component of the frequency spectra of the blocks is binarized in such a way that each frequency component is mapped to a value from the set {0; 1} as a function of a distance measure, wherein the distance measure indicates a distance between the respective frequency component and a value, in particular a mean value, which value is derived from the statistical distribution in which the respective frequency component has entered.

3. Method according to the preceding claim, wherein the binarized frequency components of the respective block are combined to form a random variable (w t -i; w t ) are aggregated for the respective block, and furthermore a temporal course of the random variables (w t -i; w t ) is observed for the blocks and a further statistical distribution (Vi) is estimated for the random variables via a mean and a standard deviation (p; o), and wherein at least the mean and the standard deviation (p; o) of the further statistical distribution serve as the statistical characteristics.

4. Method according to one or more of the preceding claims, wherein the determined statistical characteristics (p; o) are compared with a baseline in order to decide whether a blocked differential pressure line is present.

5. Method according to the preceding claim, wherein the baseline is generated in a learning phase in which the pressure measurement signal is detected by means of the pressure measurement arrangement (1) over a defined period of time with non-blocked effective pressure lines, wherein preferably successive measured values ​​of the detected pressure measurement signal are assigned to, preferably overlapping blocks (B t -i, Bt), which preferably each have the same length, are combined and, in particular, acoustic frequency spectra for each block (B t -i, Bt) are generated in the learning phase, where the frequency components (ci,ti ; c n ,t) of the frequency spectra of the blocks in the learning phase, preferably binarized by a Bernoulli transformation and the binarized frequency components of the respective block (B t -i , Bt) to a random variable (w t -i; w t ) for the respective block (B t -i , B t) of the learning phase are aggregated, whereby a temporal course of the random variables (w t -i; w t ) for the blocks of the learning phase and a further statistical distribution (Vi_) for the random variables (w t -i; w t ) of the blocks in the learning phase is estimated via a mean and a standard deviation (p; o) and wherein at least the mean and the standard deviation (p; o) of the further statistical distribution (Vi_) for the random variables of the blocks in the learning phase serve as at least part of the baseline.

6. Method according to at least one of the two preceding claims, wherein the comparison of the determined statistical characteristics (p; o) with the baseline provides for the determination of a distance measure and in the event that the distance measure falls below a predeterminable threshold value or lies outside a predeterminable range with an upper and a lower threshold value, this is evaluated as a blocked differential pressure line.

7. Method according to the preceding claim, wherein the comparison of the determined statistical characteristics (p; o) with the baseline and the determination of a distance measure are carried out by means of a hypothesis test, in particular a z-test.

8. Method according to one or more of the preceding claims, wherein, before generating the frequency spectra from the pressure measurement signal, rest phases of the process are first filtered out of the pressure measurement signal, and only then are the frequency spectra of the respective blocks (B t-i, Bt) is determined.

9. Method according to the preceding claim, wherein for filtering out the idle phases of the process for the blocks (B t -i , Bt) at least one variance is calculated, and based on the variance it is decided whether it is a rest phase of the process.

10. Method according to the preceding claim, wherein the variance is compared with a configurable or adjustable threshold value to decide whether it is a rest phase of the process, wherein the threshold value is preferably in a range < -70 dB, particularly preferably < -80 dB, most preferably in the range from -80 to -90 dB.

11. Method according to one or more of the preceding claims, wherein the frequency spectra are generated by means of a discrete Fourier transformation, in particular a fast Fourier transformation.

12. Method according to one or more of the preceding claims, wherein the frequency spectra of the respective blocks (B t -i, Bt) are high-pass filtered so that frequency components below a predefined cutoff frequency are essentially filtered out or attenuated.

13. Method according to the preceding claim, wherein the predeterminable cut-off frequency is selected such that it lies in the range of a few Hz, preferably less than 5 Hz, particularly preferably in the range of 1-5 Hz.

14. Data processing device with means for carrying out the method according to at least one of the preceding claims.

15. A computer program comprising instructions which, when executed by a computer, cause the computer to perform the method according to at least one of claims 1 to 13.

16. A computer-readable medium containing instructions which, when executed by a computer, cause the computer to perform the method of at least one of claims 1 to 13.

17. Pressure measuring arrangement, at least comprising: a pressure transducer (10) for detecting at least one media pressure of a medium; an operating and / or evaluation circuit (14) for providing a pressure measurement signal which depends on the at least one media pressure; at least one effective pressure line (25) which is connected to a pressure inlet (12) of the pressure transducer (10) in order to apply the at least one media pressure to the pressure transducer (10); a detection unit (150), which is preferably designed as part of the operating and / or evaluation circuit and is configured to carry out the method according to one or more of the preceding claims in order to detect whether the at least one effective pressure line (25, 26) is blocked.

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