SIGNAL PROCESSING METHOD FOR MULTIHEAD SCALES

Abstract

The present invention relates to a signal processing method for weight signals (W) of scales, in particular combination scales (K).

Signal processing is performed using preprocessed discrete values (W(i)) of the weight signal (W), which are supplied to at least one artificial neural network. With the help of this at least one artificial neural network, an estimated value (SW) for the actual weight is determined, for example in a weighing device of a combination scale. This is performed faster than if waiting for the actual weight signal. The estimated values (SW) are forwarded to the combination scale (KW), which uses them to form combinations.

Claims

1. A signal processing method for processing a weight signal of a weighing device, comprising the following steps: a) Repeatedly sampling a weight signal (W) between a first constant value (WK1) and a second constant value (WK2) with a sampling interval (Δt), wherein i discrete values (W(i)) are obtained; b) Preprocessing of the weight signal (W), determination and output of resulting characteristics (RK(i)), c) Providing at least two, at most i resulting characteristics (RK(i)) to respective inputs (E(p)) of at least one artificial neural network (KNN(1)) as input values, processing the input values (RK(i)) in at least one inner layer (H(m,n)), d) Outputting of output values (A(q)) from an output layer (O(q)), wherein an output value (A(1)) is an estimated value (SW) for the second constant value (WK2), wherein input layers (I(i)), the at least one inner layer (H(i)) and output layers (O(i)) each comprise neurons which are connected via weighted connections (w(m,n)) to the neurons of the same or respective preceding and/or succeeding layers.

2. Signal processing method according to claim 1, wherein the resulting characteristic quantities in step b) are selected from maxima (Max(i)) and minima (Min(i)) and associated points in time (t(i)), the areas AR(i) covered by the signal course at specific points in time (t(i)), slopes m(i) or curvatures K(i) at certain points in time (t(i)), as well as temperature, pressure, humidity, time, solar radiation, EMC characteristics, and characteristics of product properties such as density, adhesion properties, degree of moisture.

3. Signal processing method according to claim 1, wherein in step d) a prediction accuracy (PG) is further output as a further output value (A(2)).

4. Signal processing method according to claim 1, further comprising the steps d1) and d2): d1) Checking whether the prediction accuracy (PG) is within a predetermined value range and a predetermined standstill criterion (SSK) has been reached; d2) if the prediction accuracy (PG) is within a predetermined value range, accepting the estimated value (SW); if the prediction accuracy is not within the predetermined value range, processing further weight values until a valid value is reached, or adjusting the stability criterion (SSK) and repeat steps a) to d1).

5. Signal processing method according to claim 1, wherein i artificial neural networks (KNN(i)) are used in series, where i is greater than 1, comprising the following further steps: e) Reading in the input values (RK(i)) as well as output values (A(i−1, q)) of the respective previous artificial neural network (KNN (i−1)) into a further artificial neural network (KNN(i)), wherein one output value (A(i−1, 1)) is the estimated value (SW) for the second constant value (WK2), and a further output value (A(i−1,2)) is the prediction accuracy (PG) of the respective previous artificial neural network (KNN (i−1)); f) Calculating output values (A(i,q)) of the current artificial neural network (KNN (i)), wherein one output value (A(i,1)) is the estimated value (SW1) for the second constant value (WK2), and another output value (A(i,2)) is the prediction accuracy (PG1) of the current artificial neural network (KNN (i)); g) (i−1)-fold repetition of steps e) and f), h) Outputting of the final output values A(q, i), where at least a final estimated value (SW(i)) and a final prediction accuracy (PG(i)) are output.

6. Signal processing method according to claim 5, wherein the decision to use the estimated value (SW) as the second constant value (WK2) is made in dependence on the tendency, for example convergence, divergence or scatter band of the successive estimated values SW(i) or in dependence on the prediction accuracies PG(i) or a combination of both.

7. Signal processing method according to claim 1, wherein the estimated value (SW) of the at least one artificial neural network (KNN(i)) is supplied to a digital filter (DF), which has the current weight signal (W) as input signal and, starting from this value, tracks the temporal course of the weight signal (W) from this moment onwards.

8. Signal processing method according to claim 1, wherein a preprocessing of the weight signal (W) is carried out in step b) by a Fourier analysis of the weight signal (W), wherein at least one interference frequency (SF) as well as its instantaneous amplitudes (AS) as well as its phase positions (PF) are determined, and a counter-phase compensation signal (SK) is determined therefrom, which is added to the weight signal (W) in order to obtain a corrected weight signal (WKORR), wherein preferably a damping (D) is continuously determined from the weight signal (W) in order to correct the amplitudes (AK) of the compensation signal (SK), wherein the resulting characteristics RK(i) are determined from the corrected weight signal (WKORR).

9. Signal processing method according to claim 7, wherein the interference frequencies are determined by a further sensor (SE), separately from the actual measured quantity.

10. Signal processing method according to claim 1, wherein the difference of an estimated value (SW) and the weight signal (W) is determined, a correction factor (KORR) over time is determined therefrom, and this is subtracted from the weight signal (W) in subsequent measurements to obtain a corrected weight signal (WKORR).

11. Signal processing method according to claim 10, wherein the correction factor (KORR) is calculated from a fixed magnitude curve of weight signals independent of the measurement process.

12. Signal processing method according to claim 1, wherein different variants of the signal processing methods are calculated in parallel and their results are merged into an average value of the estimated value (SW) for the second constant value (WK2) by a suitable averaging method.

13. A signal processing method according to claim 4, wherein two successive weight values (W(i), W(i−1)) are compared for the standstill criterion (SSK) for the weight signal (W), and if their difference is smaller than a predetermined value (DIFF), a counter (Z) is incremented, and if their difference is greater than a predetermined value (DIFF), a counter (Z) is incrementally decreased, and if the counter (Z) reaches a specific preset requirement (ZV), the standstill criterion (SSK) is considered to be fulfilled, the counting up of the counter (Z) preferably taking place with a predetermined increment (IN) and the counting down taking place with a predetermined decrement (DE).

14. A signal processing method according to claim 4, wherein two successive weight values (W(i−1), W(i)) are compared for the standstill criterion (SSK) for the weight signal (W), and if their difference is smaller than a predetermined value (DIFF), a counter (Z) is incremented, and if their difference is greater than a predetermined value (DIFF), the counter (Z) is set to zero, and if the counter (Z) reaches a certain preset (ZV), the standstill criterion (SSK) is considered to be fulfilled.

15. Combination scale (KW), comprising: a distribution plate (1) and several dosing chutes (15), each of which is provided with a drive (11), as well as at least one weighing device (13) and optionally storage container(s) (12), each of which is/are arranged below the end of a dosing chutes (15), and a collection chute (14) adapted to receive products from the weighing devices (13), wherein the weighing means (13) are adapted to record a weight signal (W) over time (t), wherein the combination scale (KW) further comprises a control device (20) adapted to perform, for at least one weighing device (13), a signal processing method according to claim 1 for determining estimated values (SW) for the weight signal (W) of the weighing device(s) (13), determining combinations therefrom and controlling the discharge of product from the weighing device(s) (13).

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0082] In the following, selected embodiments of the present invention are explained in more detail with reference to the figures described below.

[0083] FIG. 1 shows the sampling of a measurement signal (for example the weight signal of a weighing device of a combination scale) plotted against time.

[0084] FIGS. 2 a, 2b and 2c show the preprocessing of the measurement signal according to a first embodiment of the present invention.

[0085] FIG. 2 a) shows that extreme values (i.e. local minima and maxima) are determined, and FIG. 2 b) shows that covered areas are determined up to local minima and maxima, and FIG. 2c) shows the pre-processing of the measurement signal by calculating slopes and curvatures at specified times.

[0086] FIG. 3 shows the structure of an artificial neural network according to a first embodiment of the present invention.

[0087] FIG. 4 shows value acquisition using the artificial neural network according to a first embodiment of the present invention.

[0088] FIG. 5 shows a sequence of multiple artificial neural networks and processing of a measured value according to a second embodiment of the present invention.

[0089] FIGS. 6a and 6b show a third embodiment of the present invention, in which case the switch to a digital filter is made after data processing by an artificial neural network.

[0090] FIGS. 7a, 7b, 7c, 7d and 7e show data processing or measured value processing according to a fourth embodiment of the present invention, here an average value is recorded over entire periods.

[0091] FIGS. 8a, 8b, 8c and 8d show a fifth embodiment of the present invention in which interference frequencies are eliminated by a compensation signal in phase opposition.

[0092] FIGS. 9a, 9b and 9c show a sixth embodiment of the present invention, it is shown how a corresponding detection error is corrected here.

[0093] FIG. 10 shows a section of a multihead scale with a connected signal processing system according to one of the embodiments of the present invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

[0094] FIG. 1 shows a typical measurement signal over time t—for example, the weight signal W of a weighing cell of a multihead scale. Here, discrete values W(i) are read out at specific times. Time point 0 describes the time at which a product to be weighed is inserted into the weighing cell of the multihead scale. The first constant value WK1 was the zero value, since no weight was displayed in the weighing cell before the product was dropped.

[0095] The weight signal W increases steadily and reaches a maximum W(4) (also due to the impact pulse), while it then decreases again over time, the weight signal then being subject to some fluctuations with local minima and maxima. In FIG. 1, fifteen measured values of the weight W(1)-W(15) are shown. The first measurement point W(1) is at 0 as mentioned, then it drastically increases before reaching a first local maximum at W(4), then it continues to decrease until it reaches a next local minimum at W(6), and then maxima alternate with corresponding minima. In the first region, i.e., between W(4) and W(7), the distance between two adjacent extrema, in this case maxima, is relatively large, while it decreases in the further course: For example, the distance between the two maxima near W(13) and W(15) is correspondingly smaller.

[0096] The measured value for the weight signal W moves with increasing time t towards a second constant value WK2.

[0097] In the example of FIG. 1, the sampling is therefore carried out on the basis of specified time steps Δt. Regardless of how the signal behaves, a new measured value W(i) is always recorded after a certain time span Δt, i.e. the measured signal is sampled accordingly. The corresponding inverse of the time difference Δt is the data rate DR (not shown in FIG. 1) here. The higher the data rate DR (the lower the time difference Δt), the more accurately the weight signal W is reproduced accordingly.

[0098] FIG. 2 generally shows a pre-processing of the measured signal values and determination of significant parameters.

[0099] FIG. 2 a) shows that the local maxima MAX(1), MAX(2), MAX(3), MAX(4), MAX(5) and MAX(6), i.e. discrete points, are recorded respectively independent of the time span Δt, and correspondingly the local minima MIN(1), MIN(2), MIN(3), MIN(4), MIN(5) and MIN(6). Local maxima and local minima alternate—the distance between two adjacent maxima or minima becomes smaller with time.

[0100] FIG. 2 b) shows another example for a preprocessing of the data of the measurement signal. Here, the areas AR1 and AR2 covered by the signal course are recorded at the first local maximum MAX(1) as well as the first local minimum MIN(1) respectively.

[0101] FIG. 2 c) shows a third example of preprocessing. Here, at fixed times t1, t2, t3 . . . the slopes m(i) or curvatures K(i) are recorded. Slopes m(i) result from the quotient of the difference of two weight signals W(i+1) and W(i) by the difference of both times t.sub.i+1 and t.sub.i. Curvatures K(i) result from the difference of two slopes m(i+1) and m(i) divided by the difference of both times t.sub.i+1 and t.sub.i.

[0102] Preprocessed values, as shown in FIGS. 2 a)-2 c), can better reflect characteristic features of the signal compared to merely sampled discrete values W(i) as depicted in FIG. 1.

[0103] FIG. 3 shows the structure and layout of the artificial neural network used for the processing of measured values and signals according to the first embodiment of the present invention. In the example of FIG. 3, an input layer I with multiple nodes I(1), l(2) . . . I(p) is shown. In the input layer l, p input signals are read in, for example the characteristics which are taken from a pre-processing of the measurement signal, for example p maxima/minima of the measurement signal according to FIG. 2a. The hidden layers H consist of a different number of neurons of the hidden layers H(m,n). In the present example, two columns and four rows of neurons of the hidden layer H(m,n) are shown, but dots indicate that this number can be extended accordingly.

[0104] Furthermore, multiple neurons of the output layer O with multiple nodes I(1), . . . . I(q) are shown.

[0105] The output values of the output layer O are A(1) . . . A(q).

[0106] These can be, for example, the early estimated value SW of the second constant value WK2 and a prediction accuracy PG, but also other quantities characteristic for the system.

[0107] The artificial neural network can therefore consist of a different number of neurons I(p), H(m,n) as well as O(q) in each layer, these are each connected with weighted connections w(m,n) with the corresponding neurons of the same or previous/subsequent layer. Such a weighted connection is exemplarily shown between a neuron of the input layer I(1) as well as a neuron of the hidden layer H(1,1) by an arrow, the weighted connection is named w(m,n) here. The weighted connections w(m,n) are constantly recalculated during the training phase of the artificial neural network and adjusted accordingly.

[0108] FIG. 4 shows the value acquisition using a single-stage artificial neural network KNN according to the first embodiment of the present invention. Here it is shown how p values E(1) . . . E(p) are fed to the artificial neural network, and in the present example two values are output, namely the prediction accuracy PG and an estimated value SW for the second constant value WK2. The estimated value SW should indicate a sufficiently accurate stable value. The expected confidence into the accuracy of the estimated value SW is indicated by the prediction accuracy PG.

[0109] FIG. 5 shows a second embodiment of the present invention, here the value acquisition takes place within a multi-stage artificial neural network KNN. Here, several artificial neural networks KNN1, KNN2, KNN3 . . . KNNi are connected in series. As input values i input values E(1) . . . E(p) are again used accordingly. These are read out into the first artificial neural network KNN1. This is now a computation at a first measured value W1, in this case the first local maximum. The first artificial neural network KNN1 provides a first prediction accuracy PG1 as well as a first estimation value SW1 to the second artificial neural network KNN2. Also the input values E1 . . . E(p) are delivered to the second artificial neural network KNN2. At a next measuring point W2 a second calculation is performed. This takes not only the input values E(1) . . . E(p), but also the prediction accuracy PG1 of the first artificial neural network KNN1 as well as its estimated value SW1 into account. The second artificial neural network KNN2 thus outputs a second prediction accuracy PG2 and a second estimated value SW2. These are passed on to the third artificial neural network KNN3, again together with the input values E(1) . . . E(p). The third artificial neural network KNN3 calculates a third prediction accuracy PG3 as well as a third estimated value SW3. The prediction accuracy is continuously getting better, this is also shown by the smaller scatter from SW1 to SW3. The third artificial neural network KNN3 performs the calculation at a third measurement point W3. This is repeated up to an i-th artificial neural network KNNi until finally a final estimate SWi as well as a final prediction accuracy PGi is output. The expected confidence into the accuracy of the estimate PG(i−1) of the previous artificial neural network KNN(i−1) can be better trained using the estimate of SW(i−1) of the previous stage, thus the accuracy of the prediction can be better reflected. Methodologically, the estimation of each estimation value and prediction accuracy is performed as follows: After the first artificial neural network KNN1, both the estimated value SW1 and the prediction accuracy PG1 are in the first estimation range S1∈(S1m, S1M) and PG1 ∈(PG1m, PG1M).

[0110] After the estimated value SW1 is available, the time for the execution of the next stage of the second artificial neural network for the determination of the next relevant measured value W2 can be determined, whereby the calculation of the second stage of the artificial neural networks can for example take place when the next measured value W2 arrives at the value W=(W(1)+SW(1))/2, or when the area covered by the course of the signal adopts the value A2=k×A1 with k∈(1,n). There can also be other criteria which are independent or even dependent on the course of the signal.

[0111] When the current measured value has reached the point W2, the second artificial neural network KNN2 calculates a second estimated value SW2 and a second prediction accuracy PG2. Here, both values are in a respective second estimation range S2 ∈(S2.sub.m, S2.sub.M) and PG2 ∈(PG2.sub.m, PG2.sub.M). Mostly, the estimation range is now already narrower (S2⊂S1). After the second estimated value SW2 is available, the next relevant measured value W3 can be determined.

[0112] After that, any number of further stages can follow according to the same principle. The possible ranges of the estimated values SW should thereby become narrower and narrower, the estimated values SW more and more accurate and the prediction accuracies PG better and better.

[0113] A decision to use any estimated value SWi as the expected constant value WK2 can be made using a wide variety of procedures, for example by considering the tendency (is there convergence or divergence of the successive estimated values SWi, SWi−1, or does the scatter band narrow accordingly), or the individual prediction accuracies PGi, PGi−1 . . . can be considered. Also a combination of both procedures can be done.

[0114] FIG. 5 shows the unsigned prediction accuracies PGF and the signed prediction accuracies PGV. Both variants improve with time—the unsigned prediction accuracies PGF approach the value of one, and the signed prediction accuracies PGV approach the value of zero.

[0115] FIG. 6 shows a third embodiment of the present invention, using signal processing by a method according to the first or second embodiment, i.e. filtering with one or more artificial neural networks. These output an estimated value SW for the second constant value WK2. This estimated value is set as the output value for a digital filter DF, which has the weight signal W as input signal and tracks the temporal course of the measurement signal. For this purpose, the internal intermediate parameters of the digital filter DF are calculated in such a way that the output of the digital filter DF corresponds to the value SW, i.e. the estimated value of the calculation of the artificial neural network or even several artificial neural networks. With appropriately set parameters of the digital filter DF (for example, order, corner frequency . . . ), the disturbance variables are filtered out of the measurement signal and the fluctuations of the measurement signal are smoothed. In FIG. 6 a), the changeover to a digital filter DF takes place with a very good estimated value SW1, which corresponds to the actual weight value. The fluctuations in the measurement signal (shown dashed) are suppressed by the digital filter DF. Even if the estimated value SW is slightly wrong at the beginning, it is corrected by the digital filter DF with the real input values towards the correct values. A corresponding standstill criterion SSK evaluates the dynamics of the output signal of the digital filter DF according to a given rule. For example, changes in the value may become smaller than a certain amount, and then the use of the filtered signal for the control is allowed—then a weight signal is assumed to be constant and used for the control of the scale.

[0116] FIG. 6 b) shows a worse estimated value SW2. Herein, it becomes clear that it takes longer for this to approach the second constant value WK2.

[0117] FIG. 7 shows a fourth embodiment of the present invention. The course of the signals S1 and S2 is examined here with respect to the included frequencies, in the present case via Fourier analysis. In FIG. 7 a) the periods of the low frequency signal components f1, f2 (not explicitly labeled here) are calculated and an average value is calculated over an entire period of the lowest. Thereby the influence of this frequency f1 with constant amplitude is completely eliminated, i.e. subtracted. If the influence of the first two low frequencies is to be eliminated, it must be averaged over a time corresponding to the smallest common multiple of the first two periods. In this case, the influence of the frequencies f1 and f2, with constant amplitude, is completely eliminated (FIG. 7 b)).

[0118] If the signal components decay in an attenuated manner (FIG. 7 c)), the average value will contain a residual component which reflects the decay of the amplitude (FIG. 7 d)).

[0119] If the damping factor is known, the averaging can then take the decay of the amplitudes into account accordingly and thus achieve a complete eradication of the influence of the signal components (FIG. 7 e)).

[0120] The damping factor can either be calculated from the signal curve itself or it is system-specific and known, since every mechanical system has an eigenfrequency.

[0121] FIG. 8 shows a fifth embodiment of the present invention. Here, FIG. 8 a) shows a weight signal WS affected by disturbance variables.

[0122] Corresponding interference frequencies are to be filtered out. Here, the course of the signal is first examined for the frequencies it contains, for example with the aid of a Fourier analysis. From this, the interference frequency f1, as well as its instantaneous amplitudes and the phase positions can then be determined (FIG. 8 b)). After a certain number of measurements, i.e. a measurement over several oscillations, the course of these measured variables can be used to calculate an damping of the respective frequency. Then, in parallel with the further acquisition of the weight signal, an counter-phase compensation signal f.sub.1 can be determined (FIG. 8 c)). From this, a post-processed weight signal W can be calculated, in which the counter-phase compensation signal f.sub.1 is added to the weight signal WS (FIG. 8 d)). The disturbance variable can thus be eliminated. An adjusted weight signal W is then obtained from this. The calculated damping can be continuously recalculated from the measurement signals in order to correct amplitudes of the compensation signals. The measurement signal is therefore liberated from one or more interfering frequencies, the weight signal W is therefore almost completely liberated from the dynamic components of the interfering frequencies and is therefore much quieter. This weight signal W is then used as input value for the calculation of the estimated value with at least one artificial neural network according to one of the previous embodiments. A standstill criterion can detect a valid and correct value earlier than with the actual weight signal, and the time of the weighing process becomes shorter. In a multihead scale, this may result in higher performance or better combination capabilities due to a greater number of weighing devices available for combination.

[0123] FIG. 9 shows a sixth embodiment according to the present invention; here, a measurement process is carried out according to one of the previous embodiments. However, an adaptive correction of the filtered measurement signal can be performed here to compensate for a detection error accordingly. Also in this case, a standstill criterion SSK determines the time at which the weight signal W can be considered valid. FIG. 9 a) shows that a first value that is considered valid, i.e. first detection value EEW, is not yet equal to the second constant value WK2 and the course of the weight signal W over time will still change until it reaches the second constant value WK2. The first detection value EEW as well as the course of the weight signal W over time are stored in this embodiment. Over several measurements, an average course of the detection error MVEF is formed until a static final value is reached. For each upcoming measurement, a value corresponding to the time course is then subtracted from the presence of the first detection value EEW and thus this is corrected accordingly. This is shown in FIG. 9 b)—here the detection error MVEF is subtracted from the weight signal from the detection value EEW.

[0124] The uncorrected course of the weight signal W is in turn included in the ongoing calculation of the detection error MVEF. Due to the continuous new calculation of the detection error MVEF, it adapts to the current behavior of the measuring arrangement and also according to the measuring conditions. Environmental influences can also be considered here, such as temperature, humidity, solar radiation or similar. The correction and the behavior of the signal processing are therefore adaptive. A correction formula can be calculated from the course of the detection error MVEF, and this can be used for the correction (for example, a linearly decreasing correction LKEF, starting with the detection value EEW over a time span of EEZ, as shown in FIG. 9 c). Even with a simple, linear correction, the corrected course of the values is significantly more accurate than the uncorrected one, especially in the first time span after initial acquisition.

[0125] FIG. 10 shows a combination scale KW on which a process according to the invention can be carried out. The combination scale consists of a distribution plate 1 on which products are discharged. This distributes it to dosing chutes 15, each of which is provided with a drive 11. The product falls from the dosing chutes into various storage containers 12 and then into the weighing device 13.

[0126] The individual weighing devices 13 can weigh the products contained therein and form a combination thereof—for example, if 100 g of product is to be packaged, certain weighing devices open so that a product weight as close to 100 g as possible can then be discharged.

[0127] When these weighing devices 13 open, the products are discharged into a collecting chute 14, from where they can then be fed to a packaging unit (not shown here). Signal processing is shown on one weighing device 13 by way of example. Here, a weighing signal goes to a control device 20, which accordingly performs a signal processing procedure according to the present invention. This generates an estimated value SW for the weight signal of the individual weighing devices 13. These estimated values SW are used by the control device 20 for combination finding, and the opening of the individual weighing devices 13 is controlled as soon as a combination could be found.

[0128] The invention is not limited to the described embodiments. Other input and output values may be used for the artificial neural network(s). Also, other ways of determining the pre-processing of the weight signal may be used.

[0129] It should also be noted that any of the embodiments of the present invention may be combined in any manner.

[0130] The present invention relates to a signal processing method for weight signals W of scales, in particular combination scales K.

[0131] Signal processing is performed using preprocessed discrete values W(i) of the weight signal W, which are supplied to at least one artificial neural network. With the help of this at least one artificial neural network, an estimated value SW for the actual weight is determined, for example in a weighing device of a combination scale. This is performed faster than if waiting for the actual weight signal. The estimated values SW are forwarded to the combination scale KW, which uses them to form combinations.