8/3/2019 Fruit Arma

1/4

APPLICATION OF ARMA MODELING T O THEIMPROVEMENT OF WEIGHT ESTIMATIONS INFRUIT SORTING AND GRADING MACH INERYJ. V. Francis, J. Calpe, E. Soria, M. Martinez, A . Rosado, A.J. Serrano, J. Calleja*,M . DiazoG.P.D.S., Departament dEnginyeria Electrbnica, Universitat de Valhc ia .

C/ Dr. Moliner, 50.46100 Burjassot (Valencia) - SpainE-Mail: Jose.V.Frances @uv.es(*) Dismuntel, S.A.L. Avda Pais Valencii, 155,E-46680 Algemesi (Valencia) Spain(0) Maxfrut, S.L. Avda de 10s Deportes 5, E-46600 Alzira (Valencia) Spain

ABSTRACTAccurate weighting of pieces in different sorts of conveyorbelts or articulated chains at fast speed is a key feature in manyindustrial processes. This paper presents a procedure toimprove the performance, whether increasing speed oraccuracy, of the load-cell-based weighting subsystem in a fruitsorting and grading machine to achieve an accuracy off l gramat a speed of 20 fruits per seconds. The proposed solutionincludes a signal preprocessing based on a previous ARMAmodeling of the weighting subsystem response plus a powerline-noise removal and a simple sample averaging in theplateau. The procedure has been tested off-line using realsignals acquired from a prototype machine.

1. INTRODUCTIONIn industry, applications where the weight of an object passingon a load cell must be estimated are frequent (Fig. 1) . Theseapplications often require meeting tight speed and accuracyspecifications which are, in some terms contradictory. In fruitsorting and grading machinery, a usual specification is +Igaccuracy at speeds of 1 0 pieces per second; although, in realpractice, it is seldom achieved because very simple digitalprocessing (basically, low-order averages) is performed. Ourgoal is to achieve the same accuracy doubling the speed; i.e. 2 0pieces/second; obviously, it requires higher signal processingcomplexity. Du e to its industrial applicability, studies about thisproblem ar e barely published.

Weight

Load CellL l ____,Fig. 1. Scheme of the d ynamic weighting system.This paper has been partially supported by the Comisi6nInterministerial de Ciencia y Tecnologfa (CICYT) and theEuropean Commission under project 1FD97-0977-C02-01

In the following section, both the machine and the experimentalequipment employed to acquire and process the signals will bedescribed. In the third section the proposed procedure toestimate weight from raw signals will be discussed: the firststage includes the use of an ARMA model to reconstruct thesignal, followed by a removal of the distortion of the mainsharmonics and a final low-pass filtering. Most of thedescription will be related to the worst case; i.e. maximumspeed, although conclusions are applicable to lower speeds. Theprocedure is validated with a series of tests on tracts obtainedfrom real data in working conditions. Finally, some conclusionsare exposed.

2. EXPERIMENTAL DISPOSALA real commercial fruit sorting and grading machine providedby Max frut S.L., Alzira (Spai n), with two sortin g lines has beenused. This machine carries the single fruits using finger-likecups attached to a rotating chain. The acquisition hardware wasa modification of an amplifier card provided by Dismuntel,S.A.L. Algemesi (Spain), based on th e LTCl 100 monolithicinstrumentation amplifier from Linear Technologies Corp.,Milpitas (CA). Data were acquired with a DAQCard-Al-16X E50 from National Instruments, Austin (TX), with a low-pass filter whose cut-off frequency is 400 Hz at a samplingfrequency of IKHz. ADC resolution is 16 bits although just 14are useful given the SN R of the analogue acquisition andamplifying circuitry. The relation between converter units andweight is approximately 1 gram = 13 converter units.The weighting module consist of a rail of a low-frictionmaterial (Teflo n) that reduces the impact of cu ps entering theweighting area at high speeds mounted on an aluminum framehousing the load cell. The rail has a physically isolated tractwhere the real weighting is performed. The hereby-used loadcell is a steel lOLbs from Artech lndustries Inc. (Riverside,CA) with 2.096m VN (2 lOLbs [l].A synchronization signal is provided by an optical encoder as areference for measurements and transmitted to the acquisitionPC via a CAN bus. This reference was set to the point wherethe cups leave the weighting area.The analysis was performed off-line using MATLAB 5.2 fromMathworks Inc., Cambridge (MA) on a Pentium I1 based PC-platform [ 2 ] .

0-7803-6293-4/00/$10.0002000 EEE 3666

8/3/2019 Fruit Arma

2/4

3. SYSTEM MODELFruits or vegetables are lined-up and placed into the cup in aprevious stage, then they are tossed at a constant speed andenter a weighting platform as smooth as possible to avoidimpacts and minimize vibrations. However, when the cupenters or leaves this platform and over-ringing is observed inthe signal due to the characteristics of the load cell. Figure 2shows a typical waveform when a 15Og piece travels over theweighting platform at a speed of 20 fruits per second.

Th e effect of a piece entering the platform can b e modeled withan step input if a long enough tract of the signal is available. Asweighting platform is necessarily short, it is m ore convenient toconsider the ringing observed when the piece leaves theplatform and the following cups have been removed. Figure 3shows a tract of the original signal obtained when the piece getsoff the platform, a second-order behavior is clearly observed.The input to model such a system is then:

1 n

8/3/2019 Fruit Arma

3/4

sorting machinery uyes o p t i c a i e n d e r 4 t u syi!cl~ronizationpurposes an d it is straightforward to rise a pulw to mark th emoment when the cup exits the weiphting platform. The start ofthe plateaus IC no re ~~rtnbersorneo obtarn ds i t tins 1w.n*mpmrally shown io depend on both speed and weight.Although a number of tcchiiiqries were coilsidered, th eproposed procediire results simpie nnd efficient.From real reg stcrs a complex relation between weight andplateau-lengths (i n samp les) for a given speed is observed, but arough simp1ificatio:i yields good results:1.

2.3.

4 .5 .

A Look-Up Table (LUT) containing the length (insamples) of the plateaus obtained for different weights(every 20 grams) at maximum speed (20 fruits per second)is built.The pre-processed register is stored in a circular buffer.When weight-synchronization is received, the presentvalue of the pre-processed signal is taken as a roughestimator for the weight and the LUT is accessed to see thecorresponding length of the nearest weight.The number obtained is corrected according to the chainspeed with a linear function.An average of the obtained length is performed using thedata in the circular buffer and the outcome is convertedinto grams according to a linear function obtained in thecalibration procedure.

This procedure is fast enough to be performed in real-time,although we could afford loosing several samples as thesecorrespond to the descent of the plateau.Other methods for weight estimation from pre-processedregisters were tested with valid results but requiring morecomputational burden. One of them performed a backwardsmoving-standard deviation analysis from the synchronizationpoint to detect the starting point of the plateau and other oneanalyzed the second derivative of the signal to find out drasticchanges in slope.

101

Fig.4. he-processed registers for an empty cup, a 50g pieceand an 150g piece at 20 fruits per secon d. Plateau limits aremarked with vertical lines.

-1u , , , , , , , ,

Fig. 5. Th e overshoot in the original signal for a 150g weightisremoved after deconvolution.4. RESULTS

The resulting ARMA model is a second-order, zero-phasesystem with zero delay, so it results straightforward to equalizethe system response.A model is obtained for a 30g weight at 6 fruits per secondremoving 5 cups after the weight and analyzing a 100ms tract.Empirically, the application of the single resultant model hasyielded good results for any weight and chain speed.To carry out tests, pattern pieces were manually placed in theworking prototype at a distance of the weighting platform toavoid unwanted vibrations. For practical purposes, at least twoempty cups were left between consecutive pieces. Given theslow response of the used load cells, one every two cups wereremoved from the chain, that causes a reduction in the effectivespeed of the machine measured in fruits per second. Anyway,this will be solved using faster cells that could not be receivedfrom the supplier in time for this paper.In order to validate the proposed procedure, tests were made atmaximum speed. As some mechanical aspects of the availablemachine are not completely solved, the 20 fruits per secondspeed could not be m aintained for a long time. Therefore, only8 measurements were made for 5 different conditions: emptycu p (0 grams), 30 g, 50 g, lOOg and 15Og. Figure 5 shows anoriginal register with the 150 g piece and the result ofdeconvolving it according to the obtained model.Table 1 show the results obtained at this speed together withtheir mean values, standard deviation and absolute maximumdeviation. Note that deviation respect to the ideal value neverexceeds the 1 gram tolerance.In order to increase the number of samples and assure theperformance of the procedure, chain speed was reduced to 16fruits per second and a 150 g piece was processed 10 0 times.The results, shown in Figure 6as an histogram, show a mean of149.9 58 and an standard deviation of 0.52g. The number o fvalues exc eedin g the 1-gram tolerance is 4. The obtainedresults have been processed with a series of randomness tests[5] and have revealed to be random. We have not been able to

3668

8/3/2019 Fruit Arma

4/4

fully characterize the obtained results but a Gaussian-likerandom distribution with the parameters previously stated fitswell enough. As a fairly rough estimation, if Gaussianity isassumed, the probability to obtain weight measurements out ofth e f l gram is 0.056.

a deconvolution of the original signal based on a previouslyestimated second-order ARMA model. The required + I -gramaccuracy is achieved at the speed of 20 fruits per second.The first tests on real register show the robustness of th eapproach as no further adjustments were required after theinitial calibration and data were acauired in different davs i nalmost real conditions.The algorithm can be implemented on an stand-alone real-timesystem for on-line processing.Future research on th e area could be directed to avoid thevibration effects that can heavily affect the measurements andhave been avoided in the currently available prototype withcostly and not very robust mechanical solutions.

REFERENCES[ I ] Pallis, R. Sensores y acondicionadores de sefial.Marcombo. 1994.

15 [2] Ljung, L. System Identification Toolbox for use with[3] Ljung, L. Sytem Theory: Theory for the User Prentice-[4] Proakis, J.G.; Manolakis, D.G. Introduction to Digital[5] Kendall, M. Time-SeriesCharles Griffin & Co. Ltd 1976.

Vel$ll, 0s

MATLAB. The Math W orks Inc. August 1995.Hall, 1996.Signal Processing. MacMillan Pub. Co. 1988.

Fig. 6. Histogram of the values obtained after passing a 150gweight 100times at a speed of 16 fruits per second.5. SUMMARY

A new p rocedure has been proposed to estimate the weight infruit sorting and grading machinery. The core of the m ethod is

Table 1. Results for the test for different weights at 20 fruits per second.

3669