Calibration device and process

Abstract

The present invention relates to a calibration device (4) comprising: —at least one impact plate (5), characterized in that the device (4) also comprises: —at least one first sensor (1) embedded into a first moving object (3A), said first sensor (1) measuring the way, velocity, form of movement and impact strength directly in the first moving object (3A) hitting the impact plate (5), —at least one second moving object (3B), —at least a first radio-frequency identification transmitter with antenna (1A) embedded into the first sensor (1), —at least one second sensor (2A,2B,2C) attached directly underneath the plate (5) for determining characteristics of the first moving object (3A), when hitting the impact plate (5), —at least one first means (6) for receiving first data provided by the first sensor (1), before and while hitting the impact plate (5), and for receiving second data provided by the second sensor (2A,2B,2C), when hitting the impact plate (5), —at least a second radio-frequency identification transmitter with antenna (6A) embedded into the first means (6), —the first sensor (1) and the second sensor (2A,2B,2C) are interacting with the first means (6), —at least one second means (7) for analysing the data provided by the first sensor (1) and by the second sensor (2A,2B,2C) and for calibrating the second sensor (2A,2B,2C) located on the impact plate (5) and determining the characteristics of at least one second moving object (3B) when hitting the impact plate (5), —a high speed camera (20) is configured to assess parameters due to the impacts of the first and second moving objects on the impact plate (5).

Claims

1. A calibration device comprising: at least one impact plate, wherein the calibration device also comprises: at least one first sensor embedded into a first moving object, said first sensor measuring the position, velocity, type of movement and impact strength directly in the first moving object hitting the impact plate, said type of movement being selected among rolling, sliding, jumping, hitting the plate, at least one second moving object, at least a first radio-frequency identification transmitter with antenna embedded into the first sensor, at least one second sensor attached directly underneath the plate for determining characteristics of the first moving object, when hitting the impact plate, at least one first means for receiving first data provided by the first sensor, before and while hitting the impact plate, and for receiving second data provided by the second sensor, when hitting the impact plate, at least a second radio-frequency identification transmitter with antenna embedded into the first means, the first sensor and the second sensor are interacting with the first means, at least one second means for analysing the data provided by the first sensor and by the second sensor and for calibrating the second sensor located on the impact plate and determining the characteristics of at least one second moving object when hitting the impact plate, a high speed camera is configured to assess parameters due to the impacts of the first and second moving objects on the impact plate.

2. The device according to the previous claim 1, wherein the characteristics of the first sensor are chosen among determining the number of impacts of the first object on the plate, determining the frequency and the force of the impacts of the first object on the plate, determining the type of movement of the first object, and determining the spin of the first object.

3. The device according to the previous claim 1, wherein the second sensor is arranged in a triangular pattern.

4. The device according to the previous claim 1, wherein the first sensor is a 9-axis sensor.

5. The device according to the previous claim 1, wherein the first moving object is pre-weighed.

6. The device according to the previous claim 1, wherein the first and/or second moving object is selected from a first family being vegetables or the object is selected from a second family being fruits or the object is selected from a third family being balls, or the object is selected from a fourth family being medicaments.

7. The device according to the previous claim 1, wherein the first and/or second moving object has a cubic, cuboid, spherical, cylindrical, conical, triangular prism, hexagonal prism triangular-based pyramidal, square based pyramidal, hexagonal pyramidal shape.

8. The device according to the previous claim 1, wherein the first and/or second object has a height and a diameter of at least 2 cm.

9. The device according to the previous claim 1, wherein the impact plate is flat, has a smooth surface and has a triangular, square, rectangular, rhombus, parallelogram, trapezoidal, kite, pentagonal, hexagonal, heptagonal, octagonal, nonagonal, decagonal, circular, elliptical, crescental, pipe, half-pipe shape.

10. The device according to the previous claim 1, wherein the first means is a data logger.

11. The device according to the previous claim 1, wherein the second means is a wave-flat analysis dictionary based on multiscale swept frequency wave packets of various shape and duration.

12. The device according to the previous claim 11, wherein the wave-flat analysis dictionary is a 7D-chirp atom.

13. The device according to the previous claim 1, wherein the plate is entirely made of a metallic or a ceramic material or a graphene material.

14. A calibration method comprising the following successive steps: embedding at least one first sensor into a first moving object, said first sensor measuring the position, velocity, type-form of movement and impact strength directly in the first moving object hitting the impact plate, said type of movement being selected among rolling, sliding, jumping, hitting the plate, incorporating at least one second moving object, fixing at least one second sensor directly underneath an impact plate, determining a first data provided by the first moving object, before and while hitting the plate, via the first sensor, determining a second data provided by the first moving object, when hitting the plate, via the second sensor, receiving the first data and the second data by at least one first means, analysing the data provided by the first sensor and by the second sensor via a second means, calibrating the second sensor located on the impact plate, determining the characteristics of said at least one second moving object when hitting the impact plate, assessing parameters due to the impacts of the first and second moving objects on the impact plate by a high speed camera-.

15. A non-transitory computer readable medium having stored thereon instructions of a computer program for implementing the method according to the previous claim 14 when said instructions are executed by a processor, said non-transitory computer readable medium being embedded into the second means, said second means being configured for analysing the first data provided by the first sensor and the second data provided by the second sensor in order to calibrate the second sensor of the impact plate and the second means being further configured for determining the characteristics of the second moving object when hitting the impact plate.

Description

DESCRIPTION OF THE FIGURES

(1) FIG. 1: shows a schematic presentation of the device (4) of the present invention. The drawing is not at scale (e.g. the impact plate (5) is bigger than the first moving object (3A) normally). The high speed camera (20) is necessary. Data from the first (1) and second sensors (2A,2B,2C) are directly collected on the data logger (6) in real time. The data logger (6) gives a common time stamp to all datasets. Directly connected to the data logger (6) is a software embedded in the second means (7) which analyses the data set with a chirplet analysis of the first impact of the object. This chirplet analysis is used to characterize the size and the characteristics of the object (3A,3B) hitting the plate (5). In addition, the specific impact point onto the impact plate (5) is directly assessed by the data from the smart object (3A) and via cross correlation from the triangular shaped orientation of the second sensors (2A,2B,2C).

(2) FIG. 2A shows a triangular shaped localization of the three impact sensors (2A,2B,2C) under the impact plate (5). S1, S2 and S3 mean object (stone) drops or impacts on the superior surface of the plate (5). A1, A2 and A3 mean accelerometers or second sensors (2A,2B,2C).

(3) FIG. 2B: the object (stone) drop S1 is equidistant from accelerometers A1, A2 and A3 on the impact plate (5).

(4) FIG. 2C: the object (stone) drop S2 is located, on the impact plate (5), very near to (or even directly above) accelerometer A1 and at about the same distance from A2 and A3.

(5) FIG. 2D: the object (stone) drop S3, on the impact plate (5), is located near accelerometer A2 but further from A3 and even further from A1.

(6) FIGS. 3A and 3B: show the importance of the impact location for the assessment of the characteristics of the objects hitting the plate. It also shows that it is not possible to differentiate between stones of 6 g (g2) or 90 g (g1) hitting a plate when you do not know the point of impact. To the contrary with a smart object that tracks its way to the plate this localization is possible (not shown on this figure). In this experience, two objects (gravels) are dropped on an impact plate at three different plate positions (d=20 cm, d=10 cm and d=0 cm), d is the horizontal distance from the centre of the plate where the sensor is located. The title is “hydroacoustic approaches, amplitude/frequency versus position and mass”. It shows that the amplitude of the signal increases if the location of the impact of the object is close to the location of a sensor placed under the impact plate.

(7) FIG. 3C: this figure shows time-frequency maps obtained by corresponding wavelet transform (using a standard Morlet wavelet) after dropping a 90 grams gravel at 20 cm, 10 cm, and 0 cm from the sensor location. The wavelet coefficients are indicated. The signal has been converted from the temporal domain (amplitude against time) to the frequency domain (amplitude against frequency). The result of this conversion is a spectrum with specific amplitude for each frequency. As this conversion is effected continuously with regard to the time, our database has a three-dimensional structure, i.e. amplitude against frequency against time.

(8) FIG. 3D: this figure shows time-frequency maps obtained by corresponding wavelet transform (using a standard Morlet wavelet) after dropping a 6 grams gravel at 20 cm and 0 cm from the sensor location. The wavelet coefficients are indicated.

(9) FIG. 4: shows an agricultural application of the present invention. A smart objects elevator projects smart moving objects (3A) and non-smart moving objects (3B) (e.g. a potato, an apple, a ball, or a pharmaceutical) against an impact plate (5) (having second sensors (2A,2B,2C) underneath the plate (5)) for calibration purposes of the impact plate (5) via a first means (6) and a second means (7) which is connected directly to the plate (5). (Part of the figure is taken from S. A. Shearer, J. P. Fulton, S. G. McNeill, and S. F. Higgins, Biosystems and Agricultural Engineering T. G. Mueller, Agronomy: Elements of Precision Agriculture: Basics of Yield Monitor Installation and Operation)

(10) FIG. 5: shows a scheme top view of the device of the present invention.

(11) FIG. 6: shows a scheme side view of the device of the present invention.

(12) FIG. 7: shows a 3 axial inertial measurement unit (Bosch BMI 160): accelerometer: +/−16 g; gyroscope: 2000°/s.

(13) FIG. 8: shows a 3 axial geomagnetic sensor (Bosch BMM 150): accelerometer: +/−1300 microT. Sampling: up to 100 Hz (10× internal oversampling).

(14) FIG. 9: shows a test with different drop heights in the air. Sensor and test setup: smartstone in stone sphere (100 Hz), one accelerometer in the centre of the plate (A), 125 kHz, a high speed camera (6500 fps), one drop position (a1) (43.5;49.7), heights: 3 and 5 and 7 cm.

(15) FIG. 10: shows raw smartstone acceleration data (exp. 34, 3 cm, sensor 111).

(16) FIG. 11: shows also raw smartstone acceleration data (exp. 34, 3 cm, sensor 111).

(17) FIG. 12: shows raw accelerometer data (exp. 34, 3 cm, sensor type 4394).

(18) FIG. 13: shows raw accelerometer data (exp. 34, 3 cm, sensor type 4394).

(19) FIG. 14: shows the maximum resultant acceleration (smartstone): wide range and overlap of ranges:

(20) TABLE-US-00001 Height (cm) maximum deviation from mean (%) 3 22.4 −29.4 5 20.8 −38.8 7 27.2 −29.5

(21) FIG. 15: shows the integral of acceleration peak: smaller range, no obvious relationship to height of fall:

(22) TABLE-US-00002 Height (cm) maximum deviation from mean (%) 3 9.6 −8.8 5 8.4 −13.3 7 18.1 −12.4

(23) FIG. 16: shows a fit of linear model: all acceleration peaks versus height. R{circumflex over ( )}2 should be read R.sup.2, which is the mean amplitude to mean per heights of smartstone peaks.

(24) FIG. 17: shows a fit of linear model: maximum peak per height versus height. It shows also a linear relation of maximum peak to drop height as a plausible model. R{circumflex over ( )}2 should be read R.sup.2, which is the mean amplitude to mean per heights of smartstone peaks.

(25) FIG. 18: shows results of plate accelerometers at different drop heights. Small circle: exceeded range? Big ellipse: sensor detached from the steel plate.

(26) FIG. 19: shows the results of plate accelerometers; smaller range of acceleration peaks (compared to smartstone); removed outliers deviation between 3.7% and 15%

(27) TABLE-US-00003 Height (cm) maximum deviation from mean (%) 3 15.24 −10.24 5 13.24 −13.45 7 3.74 −7.77

(28) FIG. 20: is a graph showing the results of plate accelerometers; acceleration signals: example for time-frequency analysis based on wavelet transformation.

(29) FIG. 21: is a graph showing the results of plate accelerometers; acceleration signals: example for time-frequency analysis based on wavelet transformation.

(30) FIG. 22: is a graph showing the results of plate accelerometers; acceleration signals: example for time-frequency analysis based on wavelet transformation.

(31) FIG. 23: is a magnitude scalogram showing the results of plate accelerometers; acceleration signals: example for time-frequency analysis based on wavelet transformation.

(32) FIG. 24: is a magnitude scalogram showing the results of plate accelerometers; acceleration signals: example for time-frequency analysis based on wavelet transformation.

(33) FIG. 25: is a magnitude scalogram showing the results of plate accelerometers; acceleration signals: example for time-frequency analysis based on wavelet transformation.

(34) Discussion of FIGS. 20 to 25:

(35) FIGS. 29 to 34 are prior art used in the system.

(36) FIG. 26: shows smartstone acceleration versus accelerometer amplitude; low determination for linear model to peaks: R.sup.2=0.03; mean amplitude to mean per heights of smartstone peaks: R.sup.2=0.97.

(37) FIG. 27: shows smartstone acceleration versus accelerometer amplitude; low determination for linear model to peaks: R.sup.2=0.03; mean amplitude to maximum per height of smartstone peaks: R.sup.2=0.998.

(38) Discussion of FIGS. 26 and 27:

(39) The successful calibration is highlighted on FIG. 26 and on FIG. 27. It is the calibration of a 543 g stone. A successful calibration in FIG. 26 and on FIG. 27 is the correlation of smart object and impact sensor signals with a regression line with a height R2 of nearly 1. There is now a solid relationship between the parameters of the smart object and the plate impact measurements. So it is possible to extract from the regression line the information of the smart object only by measuring the plate impact or vice versa.

(40) The scalogram with the procedure taken from the prior art technique on FIGS. 20 to 25 gives the mass of the object, and the new calibration on FIG. 26 and FIG. 27 gives the drop height/velocity of the object and the forces acting onto the object. The velocity of the object is also measured via the smart sensor in the object (see FIG. 79). The position of the impact onto the plate is done with the triangulation procedure of FIGS. 36 and 37.

(41) The innovative aspect of the present invention is shown on FIGS. 26 and 27, i.e. the combination of smart object and impact sensor signals.

(42) FIG. 28: shows a graph about the high speed camera; displacement and oscillation of steel plate; displacement peaks: range<15%; visible overtones in vibration; change in level due to smartstone weight: compressed rubber, bent steel plate.

(43) FIG. 29: shows underwater drop tests. Sensor and test setup: smartstone in stone sphere, three accelerometers in the position B1, B2 and B3 (type 4394, sealed with liquid tape), 14 cm water over the steel plate, one drop position (b1) (35,25.5), heights: 3 and 5 and 7 cm.

(44) FIG. 30: shows results of underwater drop tests. Smartstone acceleration data: examples for 7 cm damping of peaks. No free fall (buoyancy and friction in water).

(45) FIG. 31: shows results of underwater drop tests. Smartstone acceleration data: examples for 5 cm damping of peaks. No free fall (buoyancy and friction in water).

(46) FIG. 32: shows results of underwater drop tests; accelerometer data: no useful data recorded at 2 of 3 accelerometers.

(47) FIG. 33: shows results of underwater drop tests; maximum resultant acceleration: lower mean and maximum (compared to tests without water); similar relative ranges

(48) TABLE-US-00004 Height (cm) maximum deviation from mean (%) 3 23.9 −25.3 5 23.5 −19.9 7 25.5 −29.9

(49) FIG. 34: shows results of air drop tests (air maximum resultant acceleration): comparing water and air (FIG. 45 and FIG. 46); maximum per height: ca. 10-20% lower; overlap of ranges; for maximum per height different slope of regression line; assumption of linear relation still plausible within tested range.

(50) FIG. 35: shows results of water drop tests (water maximum resultant acceleration): comparing water and air (FIG. 45 and FIG. 46); maximum per height: ca. 10-20% lower; overlap of ranges; for maximum per height different slope of regression line; assumption of linear relation still plausible within tested range.

(51) FIGS. 36A and 36B: show transect positions (set up). Smartstone in stone sphere; two types of accelerometers on plate (type 8339—high impact—at B1-B3 and type 4394 at D1-D3); 7 drop positions T1-T7 as transect; constant drop height at 3 cm.

(52) FIGS. 36A and 36B correspond to the prior art used in the system.

(53) FIG. 38: shows transect positions (set up). Positions T1-T4: 20 repetitions per positions; 10 repetitions per accelerometer type. Positions T5-T7: 10 repetitions per positions; accelerometer type 8339 only.

(54) FIG. 39: shows transect positions (examples). Accelerometers type 4394: no usable data for most experiments; example: T1a_001.

(55) FIG. 40: shows transect positions (examples). Accelerometers type 4394: no usable data for most experiments; example: T1c_001.

(56) FIG. 41: shows transect positions. Example: T1d 002 (type 8339).

(57) FIG. 42: shows transect positions. Example: T1d 002 (type 8339).

(58) FIG. 43: shows transect positions. Examples T1 repetitions.

(59) FIG. 44: shows transect positions. Example: T3d 002.

(60) FIG. 45: shows transect positions. Example: T3d 002.

(61) FIG. 46: shows transect positions. Example: T3 repetitions.

(62) FIG. 47: shows transect positions. Example: T5b 002.

(63) FIG. 48: shows transect positions. Example: T5b 002.

(64) FIG. 49: shows transect positions. Example: T5 repetitions.

(65) FIG. 50: shows transect positions. Example: T7b 002.

(66) FIG. 51: shows transect positions. Example: T7b 002.

(67) FIG. 52: shows transect positions. Example: T7 repetitions.

(68) FIG. 53: shows transect positions: results. Smartstone acceleration peak at different positions. Higher acceleration peaks toward edges (T1). Higher acceleration peaks toward corners (T7). Outliers: no clear phase of free fall.

(69) FIG. 54: shows transect positions: results. Smartstone: example for outlier T1d_001. No clear phase of free fall in resultant acceleration. Low peak level.

(70) FIG. 55: shows transect positions: results. Accelerometers: time delay of signal arrival B1-B2.

(71) TABLE-US-00005 Pos. min (ms) max (ms) mean (ms) T1 0.16 0.25 0.17 T2 −0.09 0.10 0.08 T3 −0.01 0.01 0.00 T4 −0.10 −0.09 −0.10 T5 −0.23 −0.13 −0.15 T6 −0.14 −0.13 −0.14 T7 −0.44 −0.15 −0.23

(72) FIG. 56: shows transect positions: results. Accelerometers: time delay of signal arrival B1-B3.

(73) TABLE-US-00006 Pos. min (ms) max (ms) mean (ms) T1 0.14 0.22 0.16 T2 0.01 0.1 0.09 T3 0 0.02 0.01 T4 −0.1 −0.08 −0.09 T5 −0.24 −0.17 −0.18 T6 −0.17 −0.16 −0.17 T7 −0.5 −0.17 −0.28

(74) FIG. 57: shows transect positions: results. Accelerometers: time delay of signal arrival B2-B3.

(75) TABLE-US-00007 Pos. min (ms) max (ms) mean (ms) T1 −0.02 −0.01 −0.01 T2 −0.02 0.1 0.01 T3 0 0.02 0.01 T4 0 0.02 0.01 T5 −0.04 −0.01 −0.03 T6 −0.17 −0.16 −0.17 T7 −0.09 −0.02 −0.04

(76) FIG. 58: shows pebbles of three different sizes and masses. Limestone sphere 531 g, granite pebble 279 g, small quartzite pebble 18 g; three accelerometers (type 8339): B1, B2, B3; three drop heights: 3 cm, 5 cm and 7 cm; drop positions: b1(35;25.5).

(77) FIG. 59: shows a graph with the limestone sphere: examples with 3 cm height.

(78) FIG. 60: shows a graph with the granite pebble: examples with 3 cm height.

(79) FIG. 61: shows a graph with the quartzite pebble: examples with 3 cm height.

(80) FIG. 62: shows a graph with limestone sphere: examples with 5 cm height.

(81) FIG. 63: shows a graph with the granite pebble: examples with 5 cm height.

(82) FIG. 64: shows a graph with the quartzite pebble: examples with 5 cm height.

(83) FIG. 65: shows a graph with limestone sphere: examples with 7 cm height.

(84) FIG. 66: shows a graph with the granite pebble: examples with 7 cm height.

(85) FIG. 67: shows a graph with the quartzite pebble: examples with 7 cm height.

(86) Discussion of results of FIGS. 59 to 67: Level of acceleration peaks depends on Drop height Drop position on steel plate Large range of peak values Broad overlap of ranges for drop heights and/or positions Damping of maximum peak for underwater tests (ca. 10-20% lower) Maximum peak per height for relative calibration Type 4394 accelerometers High ratio between signal peaks and noise no useful data in many cases exceeded sensor range and zero shifts Type 8394 accelerometer (high impact) Sensitivity sufficient also for lighter impacts Smaller ratio between signal peaks and noise Smartstone: wide range of peak acceleration (for one height/position) Similar range for Sensors 111, 118, 119.fwdarw.not sensor-specific Possible explanation? “sampling window” of Smartstone distributes measured acceleration peak into different sampling points Sampling point at 100 Hz is mean of 10 oversampled points, measured peak appears lower

(87) Actual acceleration peaks could be too short to be recorded at 100 Hz.

(88) FIG. 68: shows the maximum range exceeded and zero shifts with an accelerometer type 4394. The full signal is visible—zero shift.

(89) FIG. 69: shows the maximum range exceeded and zero shifts with an accelerometer type 4394. First impact: peaks reaching maximum sensor range.

(90) Discussion of the results of FIGS. 68 and 69:

(91) Possible reasons of reaching the maximum sensor range: Malfunction of data logger or recording software Electrical malfunctions at cables or connectors

(92) Overloading of piezoelectric elements: might cause zero shifts.

(93) Not caused by wetness: problems occur before tests in water.

(94) FIG. 70: shows a possible mismatch of data logger channels in file (accelerometer type 8339). Order of channel change (2-3), occurs in a small number of datasets: problem at logger or in recording software.

(95) Discussion of the figures:

(96) Variability in data for repetitions Smartstone acceleration peaks: range<30% Accelerometer amplitude: range<15% High speed plate displacement: range<15% Higher precision for drop height and positions to reduce variability? Smartstone acceleration Peaks depend on drop height and position broad overlap of ranges considerably lower acceleration in water Large dataset needed to derive calibration parameters Accelerometer signals Type 8339 shock accelerometer: sufficient signal-noise ratio, amplitude peaks well within range Observable differences in time-of-arrival Further analysis of amplitude-frequency needed Experimental setup Sufficient for simple test cases, large number of repetitions possible Improvements needed: mounting and sealing of accelerometers, support structure More comprehensive experiments: wider applicability of calibration

(97) FIG. 71: shows transect positions: examples T2 repetitions.

(98) FIG. 72: shows transect positions: examples T4 repetitions.

(99) FIG. 73: shows transect positions: examples T6 repetitions.

(100) FIG. 74: shows the orientation, velocity and distance derived by AHRS-algorithm. Raw sensor data and detection of stationary times.

(101) FIG. 75: shows the orientation, velocity and distance derived by AHRS-algorithm.

(102) FIG. 76: shows the orientation, velocity and distance derived by AHRS-algorithm. Acceleration, corrected by estimating.

(103) FIG. 77: shows the orientation, velocity and distance derived by AHRS-algorithm. Velocity derived from corrected acceleration.

(104) FIG. 78: shows the orientation, velocity and distance derived by AHRS-algorithm. Position derived from corrected velocity.

(105) FIG. 79: shows the orientation, velocity and distance derived by AHRS-algorithm.

(106) FIG. 79 corresponds to the prior art used in the system.

DETAILED DESCRIPTION

(107) The smart object (3A) or smart stone (i.e. an object wherein at least one first sensor (1) is embedded into a moving object (3A)) is used to calibrate the impact plate (5) sensor(s) (2A,2B,2C). It is not only calibrated according to the ideal speed of a material transport line (e.g. vegetables, fruits, balls, or pharmaceuticals). It is also possible to calibrate the impact plate (5) sensors (2A,2B,2C) in the sense that the mass of the moving object (3A) is better assessed by the calibrated impact plate (5) sensors (2A,2B,2C). In addition, the calibration of the impact plate (5) sensors (2A,2B,2C) will improve the quality of the assessment of the form of the moving object (3A) and its spin (rotation). Furthermore, the calibrated impact plate (5) sensors (2A,2B,2C) enable to better locate the place of impact of the moving object (3A) on the plate (5), and the forces that affect the structure of the second moving object (3B). According to this last point it is possible to assess the force onto the second moving object (3B) by a smart moving object (3A) (this object records the forces from the accelerometer). When you know the corresponding signal at the impact plate (5) sensors (2A,2B,2C) then it is later possible only to use the impact plate (5) sensors (2A,2B,2C) as the assessment tool without smart objects. In the present invention calibration is preferably realized with moving objects (3A) that are pre-weighed. So you hit the moving object (3A) of a known weight and speed onto the impact plate (5) sensors (2A,2B,2C) to be calibrated. These calibrations are in-situ, because the smart moving objects are real objects of the process in the production line to be investigated. It is therefore possible to make in-situ measurements with a first sensor (1) implanted into the moving object (3A).

(108) The word “calibration” means in the present invention that a database is collected on the second means (7) where signals registered by second sensors (2A,2B,2C) are arranged for each “smart impact”. These smart object hitting signals (amplitude and central frequency of first impact 7D chirplet) from second sensors (2A,2B,2C) are stored in the second means (7) in addition to the corresponding information for each signal on speed of the object, location of impact of the object, mass of the object, and spin of the object. This spectra database with the additional information is the calibration database. First moving object (3A) and second moving object (3B) are only used to assess this additional information linked to the second sensors (2A,2B,2C) spectra. Once the database is accessible it is possible to assess each impact of any object by a simple look-up correlation with the database of calibrated impacts.

(109) Using “smart objects”, where the sensors are implanted into the moving objects (3A), which are hitting or scratching the plate opens new possibilities for relevant calibrations. The first moving object (3A) containing the BMX055 sensor module includes an active radio-frequency identification chip, an accelerometer, a magnetometer and a gyroscope. This kind of well-known sensor is developed by the company SST (smart sensor technologies) in Rheinberg (Germany). In this case, the self-calibrating probe is powered by button cells (1.55 V, 20 mAh). This module comprises a triaxial 12 bit acceleration sensor, a triaxial 16 bit gyroscope, and a geomagnetic sensor, together with an active RFID tag, memory, a chronometer, and a thermometer. The sensor module data provides orientation, tilt, motion, acceleration, rotation, shock, vibration and heading of the probe. The chronometer and thermometer provide auxiliary data on time (resolution 1/32768 s) and temperature. The ranges of the sensor module are +/−4 g for the accelerometer (where g denotes the acceleration due to gravity), +/−2500 μT for the magnetometer (where T denotes the unit Tesla), and +/−2000° s.sup.−1 for the gyroscope. One sensor axis is aligned with the long axis of the cylinder, the other two axes orientations are indicated by the battery screw.

(110) The two sensors are of different types (the first sensor (1) is the sensor implanted inside the smart 50 moving object (3A), the second sensor (2A,2B,2C) is of the type of sensor placed under the impact plate (5)). All second sensors (2A,2B,2C) are connected with each other by an electric cable. We use at least two different types of sensors in the present invention.

(111) The first sensor (1) implanted inside the smart moving object (3A) is a BOSCH BMX055 sensor module including an active radio-frequency identification chip (active RFID tag), a triaxial 12 bit acceleration sensor (accelerometer), a geomagnetic sensor (magnetometer), and a triaxial 16 bit gyroscope. This first sensor (1) is connected to the data logger via a small antenna placed inside the smart moving object (3A) and an antenna at the he outside of the data logger (6A). The first sensor (1) is built into the moving object (3A), and that's why the moving object (3A) is called a “smart object”.

(112) The second type of sensor (2A,2B,2C) may be glued, fixed, screwed under a stainless steel plate and acts as an impact sensor. The plate can also have various other shapes (e.g. pipe, half-pipe). At least one second sensor (2A,2B,2C) may be glued, fixed or screwed underneath the impact plate. These second sensors are also commercially available. It is preferable to use at least a triangular shaped array of three second sensors (2A,2B,2C) instead of one sensor. A triangular pattern of the second sensors (2A,2B,2C) allows detecting the location of the first impact of the smart object in addition to the localization of this impact with help of the smart object (3A) and of the high speed camera (20) (please refer to FIG. 2).

(113) The first and second sensors do not exchange data between each other.

(114) Measuring the way, velocity, form of movement and impact strength directly in smart moving objects (3A) hitting the impact plate (5) enables us to improve the calibration method.

(115) Plate acoustic impact sensor(s) and smart moving object records are signals characterized by strong variations of amplitude and frequency components with time. The signals from both systems are processed through a time-frequency analysis and time-series analysis (matching the temporal axis of both systems). Smart object signals can be processed with similar techniques than the one used for the plate-accelerometer (i.e., time-frequency transform). Main interests of the smart object are the speed of the object, the spin, the impact forces and the spatial information where the object hits the plate. A successful identification of the type of motion from smart object signal characteristics is possible, it should be a major contribution to the plate impact sensor calibration results. By combining plate and rock-accelerometer, this complete set-up is considered as a new spatio-temporal measuring device.

Advantages of the Invention

(116) An advantage of the present invention over the prior art is that it is possible to calibrate a device (e.g. to regulate the speed of a production line in view of the impact force of an object (e.g. a potato) against an impact plate, e.g. if potatoes are too much shocked). In the prior art it is not possible to calibrate anything due to the lack of several technical apparatus.

(117) Another advantage is that the smart moving object (3A) used for the calibration registers its way over the impact plate (5) (e.g. hitting, creeping, rolling). In addition, the second sensors (2A,2B,2C) measure in-situ hits to the plate (5) without disturbing the calibration process with external measurement equipment.

Detailed Description of the Working Embodiment of FIG. 1

(118) The moving objects (3A,3B) are projected or are moved through a vacuum or a fluid which can be a gas (e.g. air) or a liquid (e.g. water) before hitting an impact plate (5) placed in the respective vacuum or fluid. The moving object (3A) is called a smart object because a first radio-frequency identification transmitter with antenna (1A) is embedded into the first sensor (1), which itself is embedded into the first moving object (3A). A blind hole is first pierced/drilled (depending on the material of the object) into the object and the first sensor (1) is placed inside the hole which is then sealed by any appropriate material (e.g. silicon) in order to avoid the loss of the sensor during the moving of the smart object (3A). During the projection/moving of the smart object (3A) through a vacuum or fluid (gas-gas or gas-liquid or liquid-gas or liquid-liquid), and before hitting the impact plate (5) located in the same vacuum or fluid (gas-gas or liquid-liquid) or different fluid (gas-liquid or liquid-gas), the first sensor (1) sends via the antenna (1A) data to the first means (6) (e.g. spin and trajectory/course of the object). When the moving object (3A) is hitting the impact plate (5), at least one time, or is rolling on the impact plate (5) the second sensor(s) (2A,2B,2C) send(s) data to the first means (6) (e.g. number of impacts of the first object (3A) on the plate (5), the frequency and force of the impacts, the form of the first object (3A), the type of movement of the first object (3A,3B), the spin the first object (3A,3B)). All second sensors (2A,2B,2C) are connected to the first means (6) via an electrical cable. The second sensors (2A,2B,2C) can also be linked one to the other via an electrical cable. The first means (6) is collecting all data, it attributes a common time stamp to the different types of data, and sends them to the second means (7) which analyses the data provided by the first sensor (1) and the second sensor(s) (2A,2B,2C). A computer program makes the analysis and permits to calibrate the second sensor(s) (2A,2B,2C), placed underneath the impact plate (5), for further measurements of the moving objects (3B) in relevant processes.

(119) The term “calibration” means the act of comparison of a calibration database—created with the help of smart objects on the second means (7)—with signals from the impacts of moving objects (3B) without internal sensor after the calibration phase. One collects a database on the second means (7) where signals registered by the second sensors (2A,2B,2C) are arranged for each first impact by a smart object (3A). The core of the matching pursuit algorithm is to decompose the first impact signal into a set of functions (so-called dictionary). For the FAAD (First Arrival Atomic Decomposition) method and impact plate signals, we chose a very complex dictionary (7D-chirplet) to get the better approximation of the signal in a minimum number of iteration. FAAD allows determining the impact signal properties (amplitude, frequency). These smart object hitting signals (amplitude and central frequency of first impact 7D chirplet) from the second sensors (2A,2B,2C) are stored in the second means (7) in addition to the corresponding information for each signal on speed of the object, location of impact of the object, mass of the object, spin of the object. This spectra database with the additional information from the smart object (3A) is the calibration database. Moving objects (3A and 3B) are used to assess this additional information linked to the second sensors (2A,2B,2C) spectra. Once this database is available it is possible to assess each impact of any object by a simple look-up correlation with the database of calibrated impact.

(120) In addition to this calibration we get further information on the process of the assessment of production lines by impact sensors. We get the information if single particles hit the plate only once or more often. This information can only be extracted from the smart sensor assessment. It is important to know because we than know how often objects are double counted in the production process and one can estimate a correction factor to this end. This is the second type of calibration, the assessment and correction of double or multiple hits.

(121) The third type of calibration is the knowledge gain from the point of impact localisation.

(122) The detection of the first arrival at the accelerometer is difficult for large distances (10 cm and 20 cm) because the first event is of low amplitude and mixed with later arrivals resulting from 50 rebounds of the moving objects (3A,3B) on. In this context the way the object (3A,3B) takes towards the plate is important. It gives us the exact location of the point of impact onto the plate “before” the object hits the plate. So the analysing software of the 7D Chirplet (second means (7)) knows the point of impact in advance and can focus on the analysis of the nearest accelerometer and including the information about the distance between the point of impact and the accelerometer.

(123) It is necessary to use the high speed camera in parallel. The high speed camera is needed to assess the vibrations and deformations of the metal plate due to the impact by the object, to describe the type of impact and to explain the smart objects gravity forces. It is essential that the high speed camera is configured to assess the parameters due to the impacts of the first and second moving objects (e.g. the vibrations and deformations) on the impact plate (5). So for the patent the high speed camera is a vital device for the calibration.

(124) Possible Industrial/Commercial Uses:

(125) The device and process of the present invention can be designed and manufactured to meet specific requirements of production lines.

(126) Each production line where objects of a certain size are registered or assessed by impact sensors can be improved. If we have the possibility to make an object smart (by implanting a special sensor) we can improve the calibration of the relevant existing impact sensor and the production processes therewith.

(127) A big and growing market exists.

(128) Note that the smart objects might preferably not be cereal grains (because cereal grains might be too small to make them smart with implanted sensors). The smart moving objects (3A) of the present invention are preferably not in the millimetre range but they are in the centimetre range (at least 2 cm height and 2 cm width). Non limiting examples are potatoes, tomatoes, apples, oranges, balls, pharmaceuticals). The limiting factor is the size of the board where the BOSCH sensor, battery, storage and antenna are located.

(129) The present invention is not limited to the agricultural field. It can also be used in the medical field and sport field.

(130) The term “comprising” or “comprises” used in the claims should not be interpreted as being restricted to the means listed thereafter. It does not exclude other elements or steps. It needs to be interpreted as specifying the presence of the stated features, integers, steps or components as referred to but does not preclude the presence or addition of one or more other features, integers, steps or components, or groups thereof. Thus, the scope of an expression “a device comprising means A and B” should not be limited to devices consisting only of components A and B.

(131) It is appreciated that the features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. Any feature of an embodiment disclosed in the present invention can be combined with any other feature mentioned in the present invention.